Insights

By Kenny Akridge

If you’ve ever struggled to get a large language model (LLM) to handle a complex task—only to end up with incomplete, confusing, or just plain wrong responses—you are not alone. The problem isn’t the model though; it’s the approach. Cramming too much into a single prompt will lead to disappointing results.

The solution? Prompt chaining. Instead of overwhelming the model, break your request into a series of prompts. This allows the LLM to tackle each step with precision and leads to more accurate and reliable results.

If you’ve been disappointed with LLMs, it’s time to rethink your strategy. The key isn’t asking for less, it’s structuring your requests more effectively.

When One Big Prompt Fails

Let’s look at a simple example.

Imagine we have two versions of a document, and we want to find changes and summarize the impact of the changes. We might take the following approach:

1. Compare the original text with the new text.
2. Describe the grammatical differences.
3. Summarize the changes.
4. Assess the impact.

It’s tempting to bundle everything into a single prompt, but LLMs can lose focus, misinterpret the goal, or hit text limits. The results might be:

• Vague or incomplete responses.
• Jumbled steps (e.g., skipping the summary).
• Incorrect or inconsistent impact ratings.

When so much is happening, it’s easy for the model to overlook important details.

LLMs perform best when given clear, focused instructions. Overloading a prompt with too many tasks forces the model to juggle too much at once, increasing the risk of errors.

Prompt chaining solves this by breaking the process into logical, manageable steps.

How Prompt Chaining Works

Think of prompt chaining as an assembly line, with each step refining and building upon the prior output. Here is a simple example based on our document comparison problem:

Step 1: Compare Text Versions

Feed the model the original and new text, asking it to generate a list of differences. Keep the prompt simple:

• Here’s the original text: [text A]. Here’s the new text: [text B]. Please list the differences.

Step 2: Summarize the Differences

Take the detailed comparison from Step 1 and ask the LLM to condense it:

• Summarize these differences in one or two paragraphs.

Step 3: Assess Impact

Pass the outputs from Steps 1 and 2 into another prompt to evaluate impact:

• Based on these differences, classify the overall impact as High, Medium, or Low. Briefly explain why.

By chaining these steps, each prompt has a clear focus, ensuring accuracy and consistency throughout the process.

Benefits of Prompt Chaining

1. Improved Accuracy

Each prompt focuses on a single task, reducing errors and ensuring precise responses. Instead of overwhelming the model, we guide it through a structured workflow.

2. Less Confusion

When prompts are too complex, the LLM can misinterpret them. By breaking tasks into separate prompts, each step is clear and easier to follow.

3. Easier Debugging

If a mistake occurs, identifying the issue is simple—we only need to adjust one part of the chain rather than rewriting an entire prompt.

4. Better Scalability

Whether we’re processing a few entries or thousands, prompt chaining ensures the LLM handles each one efficiently without running into input length limits.

Practical Tips for Effective Prompt Chaining

· Keep Prompts Clear and Concise: Direct, simple instructions yield better results.

· Use Consistent Formatting: Define how you want the output structured (e.g., bullet points, tables, plain text, JSON).

· Document Your Chain: Track each step to diagnose and refine your workflow.

· Validate Each Step: Before moving to the next prompt, verify that the output makes sense and aligns with expectations.

Wrapping Up

Prompt chaining transforms how you interact with LLMs, turning scattered, inconsistent responses into well-structured, accurate outputs. Next time you're working with an LLM, resist the urge to overload a single prompt. Instead, break it down, chain it together, and watch the results improve.

Try prompt chaining in your next project and see the difference. Have insights or success stories? Share them in the comments below and let’s learn together!

By Tela G. Mathias

Traditionally, federal housing agencies, regulators, and the mortgage industry as a whole have relied on Program Management Offices (PMOs) to implement new systems and processes. As waterfall has fallen out of favor, and especially as the dreaded Scaled Agile Framework (SAFe) has exploded in popularity, PMOs have been renamed as Agile Management Offices (AMOs). They still have all the same problems, even with the new name.

The best PMOs are integrated, essential parts of high performing teams. They comprise people who really understand “the business” or how to deliver value with technology (or, ideally, both). They pick up all the things that fall down. They break down barriers. They help deliver bad news. They keep the team fed.

But more often than not, they become: The Process Police.

They pester us weekly to give data for: The Integrated Management Schedule (IMS).

They nag us to update: The Weekly Status Report (WSR).

Data calls for the IMS and WSR are always a dead giveaway that our PMO has become The Process Police. Why? Because integrated, essential parts of high performing teams don’t need to make data calls. They just know. Because they are essential. They are integrated. They know what’s going on.

With the rise of generative AI (GenAI), maybe there’s a case to rethink the approach. Instead of a PMO that manages projects, what if instead we had a Program Efficiency and Innovation Office (PEIO) focused on driving efficiency, fostering innovation, and unlocking AI-driven improvements? True production implementation of genAI at scale in mortgage remains spotty. Many lenders, servicers, and vendors are still trying to overcome genAI fear, figure out governance, navigate the completely uncertain regulatory landscape, and understand what it takes to make their data AI ready.

Enter the Program Efficiency and Innovation Office (PEIO)

A Program Efficiency and Innovation Office can tackle these issues head-on. The efficiency arm ensures we run a tight ship – on schedule, at or under budget, and delivering the value proposition. The innovation arm stands up the genAI lab environment, mines for use cases and opportunities for additional efficiency, and runs experiments. Efficiency and innovation work in tandem to meet the core business objectives, optimized using the best modern technology has to offer. The goal is to replace lengthy status meetings and rigid project plans with investment stewardship, outcome focused agile experimentation, and incremental innovation delivery. It’s a difficult balance, especially in federal where contracts simply do not support this kind of approach, but one we think can be achieved.

Intersecting Private and Public Sector with Efficiency and Innovation

One of the goals of the PEIO might be to intersect the best of Silicon Valley with the private sector in partnership with the public sector. Let’s take skilled genAI operators, intersect them with experienced (useful) program managers, apply hard work and hustle, and see what happens. Some things a PEIO would need to consider:

1. How to use GenAI to manage projects better. A PEIO could implement genAI-driven tools to track program efficiency, automate reporting, automate research tasks, and mine for opportunities. GenAI can help draft project documentation, perform aspects of risk assessments, and generate compliance reports, reducing administrative overhead. The PEIO will need to be the huma-in-the-loop in these scenarios, ensuring AI-generated insights are explainable and traceable, which is critical to maintaining transparency in decision-making.
2. Standing up responsible GenAI frameworks. The PEIO should work with other stakeholders to establish the lightest weight, responsible AI governance framework that defines AI usage boundaries, ensures model accountability, and addresses regulatory compliance. Where genAI is used to deliver operational value, the PEIO should pay attention to and report out on AI auditability processes, keep documented decision logs, understand validation criteria, and understand bias mitigation (if applicable based on use case for the program). The PEIO could also serve as (or intersect with) the training team to create the AI aware workforce and grow the necessary skills.
3. Enabling the provision of AI-ready data. A PEIO sees around the corners when it comes to AI ready data. Driving the team to data quality standards, ensuring structured, tagged, and machine readable datasets for AI use. It should be aware of data governance policies that cover data lineage, security, and accessibility for GenAI applications. It should work with cross industry stakeholders on achieving AI ready policy data in housing.
4. Ensuring federal and commercial contracts support agility. The PEIO must work with procurement teams to include flexible AI adoption clauses in contracts, allowing iterative implementation. It should ensure AI compliance requirements are embedded in vendor agreements, covering model transparency, data ownership, and security. The office needs to develop contract monitoring frameworks to assess AI effectiveness and adjust terms as regulations evolve.
5. Introducing GenAI labs and an experiment-first approach. The PEIO must help the team establish controlled environments for testing AI solutions, ensuring alignment with business objectives before full deployment. It should aid in development of structured experimentation protocols, defining success metrics, failure thresholds, and rapid iteration cycles. The PEIO should facilitate collaboration between teams to ensure AI innovation is scalable, secure, and regulatory-compliant.
6. Connectivity to the regulatory environment and legal progress. The PEIO must maintain a system for continuous monitoring of regulatory and legal updates, especially now as the landscape is full of uncertainty and constantly changing. It should integrate AI tools that automatically assess the impact of regulatory changes on ongoing initiatives. The PEIO should coordinate with legal teams to preemptively address potential compliance risks before they disrupt operations.
7. Thoughtful approach to scaling solutions, GenAI or otherwise. The PEIO must work across companies and agencies to develop a structured framework for scaling AI projects, ensuring interoperability with existing systems and minimal operational disruption. It should align teams to implement phased rollouts, continuously measuring AI impact and adjusting strategies based on performance data. The PEIO should ensure workforce readiness by integrating targeted training that is right for each role and appropriate to the phase of the lifecycle of the team.
8. Ensuring stewardship of all financial investments. Of course, the PEIO should continuously monitor program operations to ensure sound financial stewardship. More than ever, the PEIO should have a direct line to budget and program executive leadership to talk openly about opportunities for additional fiscal responsibility, while also ensuring funds are available for experimentation and innovation.

The Call to Action

Federal housing agencies and state and federal regulators should, first and foremost, focus on providing AI ready policy data for the industry. They should then look inward to their own readiness for AI and start to take the necessary procurement steps to create agile and genAI ready contracts. They should consider appointing a PEIO leader to drive this type of culture change and evaluate the performance of this new role based on outcomes on a 30-60-90-day time horizon.

Mortgage lenders, servicers, and vendors may want to consider a shift from a traditional PMO to a PIEO. They may want to inventory and assess current processes and methods with an eye towards opportunities for increased efficiency. In parallel, they should continue their responsible efforts to bring genAI to their organizations. They should urge their federal and state counterparts to join us as we try to move the industry forward with AI-ready policy data.

By Tela G. Mathias

Traditional service level agreements (SLAs) are how we measure technology performance in the mortgage industry, and really in all software solutions. These agreements historically focused on quantifiable metrics such as system uptime, response times, and service availability. The attempts to scale and increase adoption of generative artificial intelligence (genAI)-based solutions in mortgage has created a need for more sophisticated performance measures that go beyond traditional operational metrics.

While traditional SLAs effectively measure whether a web-based loan origination system (LOS) loads quickly or if an automated underwriting system (AUS) remains accessible, they fall short in evaluating the quality and reliability of generative artificial intelligence (genAI). A system can maintain perfect uptime while delivering inaccurate or biased results. This gap between operational performance and actual effectiveness necessitates a new framework for measuring AI system performance.

AI evaluations shift how we measure technology performance in mortgage lending. These systematic assessment methods focus on the quality and reliability of AI outputs, rather than just system operational performance. For instance, let’s imagine a hypothetical genAI agent whose objective is to resolve complaints from consumers regarding escrow shock, an unexpected and significant increase in a homeowner's monthly mortgage payment due to changes in their escrow account requirements.

This agent monitors email to identify complaints of this type, runs a root cause analyzer, creates a management action plan, kicks off a workflow for a human in the loop, and presents the contextual plan to the operator for review and communication to the homeowner. We might need an evaluation framework to measure:

1. Compliant classification accuracy. Did the AI system find the right complaints? Did it miss any?
2. Root cause analysis quality. Did the AI system correctly determine the root cause of the complaint?
3. Action plan effectiveness. Did the AI system create the right action plan? Did it correctly report the complaint to CFPB? Did it annotate the system correctly?

These types of metrics fit well within an organization’s responsible AI (RAI) framework and help us evaluate our performance against the reliability pillar, especially.

The emergence of open-source evaluation tools has made it at least feasible, even if technically challenging, for mortgage companies to implement RAI frameworks. Tools like promptfoo enable systematic testing of large language models, helping organizations:

1. Validate model outputs against established criteria
2. Identify potential security vulnerabilities
3. Ensure consistent performance across different scenarios
4. Monitor and maintain compliance with industry standards

As genAI continues to transform mortgage lending, the industry should adopt evaluation-based performance metrics that match the sophistication of these new technologies. This evolution from traditional SLAs to evaluation frameworks will help ensure that AI systems operate reliably and deliver trustworthy, compliant, and fair results.

Organizations that adapt their performance measurement approaches to include evaluations will be better positioned to leverage AI technologies effectively while maintaining the high standards of accuracy and fairness. I believe in and will encourage regulators and housing agencies to look for evaluation-based performance frameworks in genAI based systems.

• The best antidotes that I’m aware of for genAI all start with education. How can regulators work with industry to become aware? What does an AI-aware regulator look like? What responsibility does a regulator have to become AI-aware? On what timeline?
• How can the industry use AI responsibly if no one defines what responsible use in mortgage actually is? Frameworks are fine, but frameworks are not specific enough to be especially useful, the devil is in the details – who will provide the details?
• If no one provides the details and the industry figures it out as best it can, what are the consequences for industry if we “figure it out wrong”? If wrong is not defined up front, is it actually wrong?

This whole conversation is about weighing the benefits against the risks. Many of us are afraid of what bad things can happen. Regulators may feel a sense of personal responsibility to the homeowner, to the American taxpayer, and to the world in some cases. A responsibility to create a safer, more responsible mortgage industry. In 1995, the cost to originate a loan was $3,500, today it can be as much as $13,000. Is there not room in there for innovation? Safe innovation? Responsible innovation? I think there is. Let's partner as an industry to make it so.

By Tela G. Mathias

The single hardest problem in mortgage is compliance. Being compliant. Staying compliant. Proving compliance. Understanding compliance change. The goal of our regtech startup, Phoenix Burst, is to simplify mortgage compliance. Imagine a world where compliance just happens. What if you could push a button and provide regulators and housing agencies with evidence of process compliance?

It all starts with AI-ready policy data provided by housing regulators and agencies.

At the heart of compliance is change. Something changes and we have to figure out what to do about it. At PhoenixTeam, we envision regulatory change fulfillment as a wheel, with change at the center and evidence of compliance as the outermost ring. The bullseye of the wheel is some kind of a change notice. Today, we can find out about a change in a variety of ways – a notice can be published on the federal register, an email notification from an attorney regarding a state change, a marked-up handbook chapter, a webpage update. Maybe a combination. Some of us subscribe to AllRegs. Throw in a Thompson Reuters and a Lexis Nexus. Maybe hire an attorney firm to decompose the Freddie Mac servicing guide. You get the point.

I liken this whole process to one of “trolling the internet for changes”.

I say this lovingly. I’m certain that regulators and housing agencies are truly doing their absolute best to communicate what they need. I just think we can do better. I know we can do better.

Let’s take the Federal Register application programming interface (shout out to whomever stood that up – we love it, and we use it almost every day!). We are able to consume regulatory language through this interface, compare current to prior versions of the regulations, determine the changes, and create plain English change statements in minutes. And get this – we can do it with almost 100% accuracy.

We can do that same process with a “track changes” portable document format (PDF) document as well. But it’s so much harder, and our accuracy rate is only 79%. And when I say much harder, I mean really a lot harder. The document snippet above was created by Phoenix Burst using generative artificial intelligence. No human was involved. As we are still testing this capability, we did perform 100 percent substantive human evaluation of all results to ensure accuracy. These human evals will ultimately move to system evals as we productionize these capabilities.

We intend to take the following actions to help move the industry towards AI-ready mortgage policy data:

1. Meet with the Mortgage Industry Standards Maintenance Organization (MISMO) and start the conversation about a standard interface for regulatory and policy data.
2. Meet individually with each regulator and federal housing agency to collaborate on needs and start to formulate a standard.
3. Publish a white paper for the industry on how agencies and regulators can work with industry to simplify compliance, starting with AI-ready mortgage policy data.
4. Host a generative artificial intelligence (genAI) summit in April of this year, in Washington DC, centered around regulators and agencies and how the industry can work together to take advantage of these new technologies (responsibly!).

We are looking for ideas and input from everyone, so please reach out if you would like to join us. Stay tuned for more on how genAI will upend compliance in mortgage. Nothing changes if nothing changes, so let's change it!

By Melanie Lewis, Partner, PhoenixTeam

Artificial Intelligence (AI) has the power to transform mortgage operations—but without clear goals and measurable outcomes, it risks becoming an expensive solution. At PhoenixTeam, we’ve learned that innovation without a clear use case is just a hobby. And a use case without measurable value? That’s wasted potential.

The key to unlocking AI’s potential lies in a structured approach to evaluate, prioritize, and measure its impact. ROI isn’t just about dollars saved—it encompasses employee sentiment, efficiency gains, and other less quantifiable benefits. By focusing on outcomes and disciplined evaluation, we’ve developed a repeatable framework to turn AI’s promise into measurable results.

Start with the Big Question: Where’s the Value?

The first step in pursuing an AI project is aligning goals, processes, and measurable outcomes. One guiding framework we use is Gartner’s Defend, Extend, Upend model, which categorizes AI initiatives into three strategic portfolios:

• Defend: Strengthen and optimize existing operations.
• Extend: Build on current capabilities with incremental improvements or new tools.
• Upend: Disrupt the status quo by creating entirely new business models or approaches.

This framework ensures every AI initiative pursued is aligned to the organization’s strategic AI vision and addresses the right problems. For each use case, we ask: Are we protecting what works? Enhancing what’s already there? Or breaking the mold entirely? This approach helps identify high-impact opportunities and prioritize them effectively.

Align on Outcomes: Define Success from the Start

Before analyzing costs or ROI, it’s critical to establish a shared definition of success — both for the AI solution's outcomes and the value/ROI analysis. Securing alignment and shared understanding on priorities early ensures that the analysis focuses on what matters most to stakeholders.

1. Clarify Goals: Is the objective to reduce costs, improve efficiency, or achieve qualitative outcomes like enhanced employee satisfaction?
2. Set Expectations: Agree on the level of detail required. Is a quick estimate sufficient, or is a deeper, data-driven analysis necessary?

For example, if an initiative reduces manual work and reallocation of those impacted resources is a goal, then there may be no need to present savings tied to a reduction in force. Similarly, if cost savings is the goal, understanding expense drivers becomes critical. Aligning on these priorities prevents missteps and keeps efforts focused where they’ll have the greatest impact.

Map the Current State: Build a Strong Foundation

With outcomes defined, the next step is mapping the current state. Understanding your organization’s existing processes, people, technology, and costs provides a baseline for evaluating AI’s potential impact. Key areas to assess include:

• Processes and People: What workflows are in place? Who’s involved, and what are they doing?
• Technology: What systems are used? Where are the limitations?
• Current Costs: What does it cost to operate today?

Begin by identifying inefficiencies and opportunities for improvement. Document existing systems to evaluate compatibility with potential AI solutions and determine how seamlessly new tools might integrate. Engaging subject matter experts (SMEs) to validate assumptions ensures that the final analysis is both accurate and credible. This process also refines reusable templates for future projects, saving time and effort.

Calculating ROI: A Scalable Template

Once the current state is mapped, the focus shifts to distilling findings into an actionable ROI analysis. Here’s how we approach it:

1. Net Cost Savings: What does it cost to do this process today? What will it cost post-AI implementation?
2. Cost and Benefit Breakdown: Factor in labor spend and savings, model training, infrastructure and maintenance, and potential market fluctuations over time.
3. Realistic Projections: Apply a 20% contingency buffer to account for inefficiencies and adoption hurdles.
4. Record Qualitative Feedback: Document notable comments from subject matter experts and stakeholders about the value of implementing AI solutions.

The result is a clear, actionable view of the potential ROI tailored to what matters most to the client—whether that’s cost savings, efficiency gains, or qualitative improvements.

But it doesn’t stop there. ROI analysis transitions seamlessly into action, starting with a proof of concept (POC) to validate assumptions in a controlled environment. Once tested, we compare POC results to initial projections and refine our approach, creating a repeatable, scalable process.

Practical AI for the Mortgage Industry

For mortgage companies, AI is more than a tool to streamline operations—it’s a catalyst for reimagining workflows. While ROI is a key focus, it’s important to understand that the first step is to define broader strategic goals, align stakeholders, and ensure the AI project is positioned for success. Only once these foundations are in place do we dive into the detailed ROI analysis. PhoenixTeam’s Practical Mortgage AI (PMAI) approach enables organizations to:

• Align on outcomes and define success metrics.
• Map workflows to uncover inefficiencies and identify opportunities for AI-driven improvement.
• Build scalable frameworks that ensure AI’s impact grows with the business.

By prioritizing tangible ROI, intangible benefits, and scalable processes, mortgage leaders can confidently pursue AI initiatives that drive measurable value. Whether defending current operations, extending capabilities, or upending the status quo, PhoenixTeam is here to help you unlock AI’s full potential—one measurable success at a time.

Ready to understand the ROI of your AI strategy? Reach out to learn more or join our AI for Mortgage Professionals course.

By Tela Mathias, COO and Managing Partner at PhoenixTeam A fascinating study came out in September regarding the rate of adoption of genAI in the United States. Bottom line up front – the rate of adoption of genAI is outpacing the internet and the personal computer by a factor of two. That’s just bonkers. Think about what that means for many of us – in our lifetime we will have seen three truly disruptive technologies reach mass adoption. I remember when my mom came home with a laptop for the first time – it must have weighed 25 pounds and was as big as a microwave. The screen was tiny, maybe five inches by five inches. I remember not having email until college, and even then, not really using it.

The world is so different now and will become even more different in the next few years. Looking at the chart above, you can see it took a full five years for the internet to reach this same point, and about 13 years for the personal computer. Yet here we are. This tells me the next two years are going to be crazy, and that now is the time to really lean into genAI and making it useful in our organizations.

The survey first defined genAI as follows:

“Generative AI is a type of artificial intelligence that creates text, images, audio, or video in response to prompts. Some examples of Generative AI include ChatGPT, Gemini, and Midjourney.”

I love how simple this is. This helped to eliminate the “unknown” use of embedded AI solutions. It also helped control for people who don’t understand terms like “large language model” and “machine learning”. I suspect they were also trying to get at people that have knowingly made a decision to use a conversational genAI chat solution. Then they asked if respondents had used genAI for their work, followed by asking if they used it for personal use.

The authors found that:

“…39.4 percent of all August 2024 respondents say that they used generative AI, either at work or at home. About 32 percent of respondents reported using generative AI at least once in the week prior to the survey, while 10.6 percent reported using it every day last week.”

Almost one third of Americans are using genAI at least once a week, and ten percent every single day. According to the study, these are some of the most common tasks performed at work, in order of the frequency of use. This helps us get a sense of the most common everyday use cases:

1. Writing communications

2. Performing administrative tasks

3. Interpreting, translating, summarizing

4. Searching for facts or information

5. Coding software

6. Documentation or detailed instructions

7. Generating or developing new ideas

8. Support with customers or coworkers

9. Data analysis or visualization

10. Tutoring or educational assistance

People are using genAI at work in their day-to-day lives. They are finding ways to be productive. The productivity gains are there. The next logical question is how organizations can take advantage of these productivity gains. This is an interesting question, and the survey found that:

“Employer encouragement is highly correlated with AI use: 82.9 percent of workers who report encouragement also report using generative AI, compared with only 7.1 percent of workers who report no encouragement.”

This means that those of us who are banning the use of genAI at work are missing out on the potential for productivity gains. The incredibly well researched and articulate professor Ethan Mollick out of Wharton noted that:

“…when I talk to leaders and managers about AI use in their company, they often say they see little AI use and few productivity gains outside of narrow permitted use cases.”

Organizations are not seeing the gains that workers are. He noted that this seems to be due to two major factors – scary policies and lack of internally driven research and development in an AI lab environment. He points out that organizations can crowd source innovation from workers by encouraging them to safely innovate, and then use the lab to operationalize that innovation and drive enterprise value.

This tells me that we really must figure out how to balance the real risks inherent to generative technologies in a way that does not stymie innovation. I continue to believe that the organizations that find a way to take advantage of genAI with truly innovative use cases will get the edge, broadly and specifically in mortgage.

Last thought for today – equally staggering about the study, fully 60 percent of Americans have never knowingly used a genAI solution. That is also bonkers! So when you are in a room of 20 people talking about genAI, up to 12 of them might have absolutely no idea what you are talking about. With such limited understanding, how can organizations get started? We must educate and create awareness. If AI is going to be for everyone, we really must lean into educating our organizations.

GenAI is still very early in implementation in mortgage, which means the edge is still up for grabs. But it will start with understanding what we are talking about. We are offering some free education if you are interested.

Sentiment analysis emerged as a field in the 1990s, and NLP-powered sentiment analysis emerged as an innovative call center technology at that time, allowing machines to determine how a customer call was going in near real-time. Combined with automatic speech recognition (ASR), traditional call recording solutions became more intelligent with NLP, enabling meaning to be derived from text generated from speech.

The Miracle of What Happened Next

In 1998 the convolutional neural network (CNN) was invented, and in 2009 the first graphics processing unit (GPU) was used for the purpose of deep learning. This major technological leap was actually enabled by video games, as GPUs has previously only been used to render high quality video game imagery. Then in 2017, the groundbreaking paper “Attention is All You Need” was published by eight computer scientists working at Google, which introduced the transformer architecture and the mechanism of self-attention.

Combined with the massive gains in accelerated computing, this gave us mass availability of generative AI (genAI) in 2022 with the release of ChatGPT 3.0.

GenAI is a type of artificial intelligence that creates new content, such as text, images, or code, by learning patterns from existing data.

Penetration of Generative AI in Mortgage Today

While confusion persists around the distinction between AI and genAI, successful applications like operator chatbots leveraging retrieval-augmented generation have emerged. AI-assisted code generation has become routine, and large language models (LLMs) are being used in sentiment analysis and customer interaction analysis. GenAI-based tools are gaining traction in OCR, ICR, and marketing content generation, though adoption remains cautious, particularly for customer-facing roles. Mixed policy approaches reflect this hesitance, with some enterprises fully embracing tools like ChatGPT while others impose outright bans, highlighting cultural and educational barriers.

Despite these advancements, confusion about genAI's use cases, often conflated with traditional AI or expert systems, further complicates adoption. Discussions about AI guardrails are becoming more prevalent, reflecting the need for clearer guidance and responsible use. However, truly innovative applications of genAI in industries like mortgage remain scarce, likely due to restrictive policies, a chilling effect from regulatory fears, and insufficient investment in organizational research and development (R&D) labs. This underscores the need for more targeted exploration and education to unlock genAI’s potential while addressing these challenges.

Recently I was asked about the use of guardrails in our product and put together a white paper to formalize our internal documentation and thinking. I was curious to explore the relationship between LLMs and how we use them, and to see what the thinking was in other fields like robotics. Guardrails refer to the policies, protocols, and technical measures we put in place to prevent AI systems from producing undesirable or harmful results. Frankly, the concept of guardrails applies to use of ANY technology, especially in mortgage where the consequences of “getting it wrong” can be so significant.

Of course, Sheridan’s classification scheme always comes up, but I was also impressed with the work of Jenay M. Beer “Toward a Framework for Levels of Robot Autonomy in Human-Robot Interaction”. Sheridan and Beer both offer ways of thinking about how humans interact with technology. Based on the need and what can go wrong as we work to meet that need (think borrower applying for a home loan), we need to care about how we ensure technology is doing its job. The question is not can we automate a use case but should we. And if we “should” automate a use case, what safeguards can we put in place to ensure the results are good for humankind.

This is where guardrails come in.

At PhoenixTeam, we think about a broad set of guardrails applied throughout the product, and then specific controls within each of the four categories. We also have a variety of additional controls in our roadmap and dedicate about 30% of our development capacity to improve safety and responsible use.

1. We employ the OpenAI 4o stack through a PhoenixTeam OpenAI “team” account for access to both the OpenAI application programming interface and the ChatGPT experience. This business user account type ensures that any data we exchange is not used to train OpenAI models.
2. All AI-generated content is grounded using retrieval-augmented generation (RAG). This technique involves retrieving relevant information from a trusted dataset to provide context for the AI’s responses. By anchoring outputs to specific, verified sources, we significantly reduce the likelihood of hallucinations or contextually irrelevant content.
3. We have spent hundreds of hours engineering our prompts to stay grounded, minimize hallucinations, and optimize results. By providing clear instructions and context within the prompts, we minimize the potential for the AI to generate incorrect or unrelated information.
4. We have implemented a multistep process that incorporates the option for human oversight at each step in the generation process to help ensure the accuracy and appropriateness of AI-generated content. Having a human in the loop (HITL) strikes the right balance between enabling innovation and mitigating risk of unintended results and is an important component of Acting Comptroller of the Currency’s Michael J. Hsu’s remarks on AI risk management toll gates.
5. Although we have selected the OpenAI stack as the initial foundational model behind Burst, ultimately this is the customer’s choice. Customers may prefer to use a more open-source model. They may seek additional copywrite and intellectual property protection than those offered by OpenAI. We work with enterprise customers to put the model of their choice behind Burst.
6. We have introduced limited systematic evaluation guardrails to monitor AI outputs continually. We are actively evaluating multiple evaluation frameworks for future implementation to enhance our monitoring capabilities and to help ensure consistent compliance with responsible AI standards.
7. We current store the chunk from which the statement was generate in our relational database (RDB). We plan to implement additional visible content tracing to individual end-state artifacts later this year. This will enhance transparency by allowing users to trace outputs back to their original sources, fostering trust and accountability.
8. For speed and efficiency purposes, we have avoided model fine-tuning in Burst. We intend to evaluate the cost benefits of model fine tuning, and this discovery effort is on our roadmap for next year. Fine-tuning could allow us to tailor the AI models more precisely to our specific use cases and responsible AI guidelines.
9. Our use cases do not require or accept any personally identifiable information about consumers (or any human). We make extensive use of process, policy and regulatory information in the public domain to enrich client process-based information and generate artifacts from this knowledge.

We have a lot to learn yet and continue to explore the use cases we implement in our solution, the operational process supporting the use of our application, and how what we do fits into the broader ecosystems of our clients. Please come see this in action and ask your questions at our demonstration at MBA. Chatbots are great, but there's so much more to applying genAI in mortgage. Understand how Sheridan's mental model for assistance versus automation applies to use cases in mortgage. See it in action in a live demo of Phoenix Burst and be ready to ask all your questions about building production applications with genAI.

Thomas Sheridan (born December 23, 1929) is American professor of mechanical engineering and Applied Psychology Emeritus at the Massachusetts Institute of Technology. He is a pioneer of robotics and remote-control technology. Jenay M. Beer is an associate professor at the University of Georgia (UGA) Institute of Gerontology, with a joint appointment in College of Public Health (Department of Health Promotion and Behavior) and the School of Social Work.

By Tela Gallagher Mathias, COO and Managing Partner, PhoenixTeam

There is endless hype and little real information about how to put genAI safely into practice in the mortgage industry. I have done about 40 interviews and feedback sessions since May and in addition to being fun, it has really opened my eyes to what’s going on in mortgage since the genAI frenzy started. I have a lot of gratitude for each person or team that has taken the time to meet with me. This topic has refilled my technology love tank.

Let’s break it down – what’s the deal with genAI in mortgage?

First, let’s acknowledge that “AI” has been in mortgage in some form or fashion for about 30 years. Built-for-purpose AI, sometimes called “narrow” or “weak” AI has dozens of practical applications, widely adopted in the industry. What’s already out there falls in four broad AI subspecialties.

I think the mad scramble to come up with AI strategies is unnecessary and fails to give the industry enough credit. We’ve been doing AI for years, we just didn’t call it AI.

Next, what’s all the hype about? The hype is about a form of AI that is now widely available called generative AI (genAI). In mortgage especially, the form of genAI we are really excited about combines machine learning (ML) with natural language processing (NLP) to generate new content. Generative AI refers to a type of artificial intelligence that can create new content, such as text, images, music, or code, by learning from existing data. Unlike traditional AI, which is typically designed to recognize patterns or make predictions, generative AI models are capable of generating new, original outputs that are similar to the data they were trained on.

Why should we be so excited about this? What is everyone losing their shirt about? This type of technology is truly disruptive. Occasionally, just a few times in a lifetime, a tool comes along that revolutionizes the way we live and work – and genAI is one of those tools. For those of us old enough to remember it, think back to when the internet became available – or when your mom lugged home the first personal computer. Everything changed. That’s what this is like. So we should definitely all be paying attention.

And finally, where is this going in mortgage?

My prediction is that the uses of AI today will continue. Our applications of machine learning will evolve, we will see more and more supervised and unsupervised learning applications (think smarter models). We will see companies being to (responsibly) use the decades of transactional data that has been sitting idle in new ways to drive new value and enrich the lives of homeowners. We will see (and, in fact, are seeing already) the next levels of technology in OCR and intelligent content recognition (ICR) that will finally perfect document recognition and data extraction. Expert systems will be, well, more expert. We will still have numerous uses for business rules based deterministic systems. This is mortgage after all.

What we specialize in at my company, however, is genAI. Where is that going? We will continue to see operator-facing chat and “ragbots”, or bots that use retrieval augmented generation (RAG) to enrich requests to the large language models (LLMs), constrain the answers provided, and improve operator experiences. More and more, we will see the lives of our operators enriched and simplified with immediate and relevant answers to all sorts of questions.

We are going to see more and more assisted customer email responses that combine sentiment analysis with language, image, and video-based generative content, most uses with a “human in the loop” to control for model hallucinations and ensure only accurate and appropriate content is going to homeowners. We will see the most creative companies invest in experimentation with multimodal enrichment approaches, again with a human in the loop. Without naming names, whole classes of technology will become obsolete, as what they do will be replaced with a much cheaper, simpler alternative.

We will not see unguarded, wholly automated applications of genAI put in front of customers. The industry will not accept it, and the technology is not ready. The cleverest companies will embrace the uncertainty and go into the lab to run lots and lots of science experiments. They will scour their organizations for use cases (don’t forget, innovation without a use case is just a hobby). They will take those use cases and figure out which ones can be solved or enabled with genAI. They will filter these lists and take this tipping point moment in time as a chance to revisit their technology roadmaps and invest in technology, much of which will have nothing to do with AI all.

It's a great time to be in mortgage.

AI is largely a ten percent job, ten percent of the time, for ten percent of your team. Mortgage business leaders continue to need to keep their focus on operations, compliance, and customer success. Technology leaders have their hands full maintaining a hybrid ecosystem of core heritage solutions and more modern point solutions. They must also keep up with the crushing pace of change, constant drumbeat of integration challenges, and the latest security threats.

We believe every company in the industry needs a practical approach to artificial intelligence that can be implemented quickly and safely. Much has been written about the lack of modern technology in mortgage, and we see the mass commercial availability of AI and generative AI as an opportunity to leapfrog this prevailing paradigm. We believe companies who seize the opportunities in AI will be the ones that gain a critical edge over everyone else. Enter practical mortgage AI from PhoenixTeam.

Written by Erin Bittenbender, Kellie Stoll, and Heather Kvasnak at PhoenixTeam

With nearly 300 online and in-person attendees and representatives from over 100 companies, the MISMO Spring Summit 2024 was packed with insights on AI, MISMO Adoption, Uniform Appraisal Dataset (UAD), Mortgage Compliance Dataset (MCD), credit reporting, and much more. Let's dive into our key takeaways from the event.

Adoption & Implementation

MISMO standards awareness and adoption was the common theme at this year’s Summit. Work groups brainstormed ideas to raise awareness and encourage engagement across all stakeholders in the mortgage industry.

For many in the industry, “MISMO” is often seen as a forum exclusively for tech professionals to discuss industry standards. While it is true that MISMO standards foster innovation in the mortgage ecosystem as it pertains to digitalization, interoperability, and data transfer, there is a strong need for business professionals, including senior and C-suite leaders, to understand how implementing MISMO standards can:

• Streamline processes
• Enhance productivity
• Decrease application-to-closing timeframes
• Reduce costs associated with errors and rework
• Digitally communicate with industry partners and vendors

MISMO initiatives are designed to advance the mortgage industry from pre-application to post-closing. By fostering a deeper understanding and broader adoption among technical and business leaders, the work efforts of the many MISMO volunteers will set the stage for a more efficient, interconnected, and innovative future for the industry.

Artificial Intelligence is Here to Stay

MISMO Residential Governance approved a new Artificial Intelligence CoP. The CoP will monitor industry trends related to AI, identify AI standards that the MISMO CoP could produce, and create AI education materials. Keep an eye out for the Call for Participants to join the new CoP. Our team can't wait to participate! Tela Gallagher Mathias, Brian Woodring, John Comiskey

Preparing for Implementation of the Uniform Appraisal Dataset (UAD)

MISMO is spinning up a new effort in the Property and Valuation CoP to develop a comprehensive dataset specification that will facilitate the procurement of valuation services without the reliance of form numbers. This work effort will provide the industry with a common framework to exchange transactional order information using the new terminology and requirements being introduced in 2025 as a part of the Fannie Mae and Freddie Mac Uniform Mortgage Data Program (UMDP) Uniform Appraisal Dataset (UAD).

Mortgage Compliance Dataset (MCD)

The “Standardizing State Examination Data with the Mortgage Compliance Dataset” panel highlighted how lenders, compliance vendors, and regulators can leverage the MCD to improve the examination experience, increase vendor portability, and reduce LOS interface maintenance costs by developing a standardized dataset. The MCD is wrapping up its 60-day public comment period and is designed for:

• State and federal mortgage regulators (and their compliance vendors) to perform automated compliance testing on closed loans that can be utilized across LOS platforms and doc vendors.
• Mortgage lenders (and their LOS and compliance vendors) to perform compliance testing pre-closing and post-closing.
• Other parties interested in post-closing origination compliance testing prior to whole loan purchases and securitizations.

The Credit Reporting CoP continues to develop guidance to support the industry’s implementation of the FHFA / GSE credit report changes. The CoP also hosted two educational sessions led by Jamie Norris, from Experian, that covered:

• The Benefits of Consumer-Permissioned Data: Empowering Credit Profiles and Scores
• Open Banking:Harnessing Alternative Cash Flow Data for Mortgage Consideration

FHFA is hosting a stakeholder forum on Tuesday, June 25th at 3:00 pm ET, focused on preparing for the publication of the VantageScore 4.0 historical credit scores on July 10th. You can find more information including registration information on the Fannie Mae and Freddie Mac sites.

The MISMO Spring Summit underscored the pivotal role that MISMO standards play in advancing the mortgage industry. By bridging the gap between technology and business leadership and by embracing new initiatives such as AI and the Mortgage Compliance Dataset, the industry can achieve greater efficiency and innovation. Continued collaboration and education among industry stakeholders is essential to realizing the full potential of these standards, ensuring a more streamlined and effective mortgage ecosystem for all.

Follow PhoenixTeam on LinkedIn for the latest news and updates.

By Tela Gallagher Mathias, Managing Partner and COO, PhoenixTeam

What is a value engineer?

In our Accessible AI Talks series with Brian Woodring, and in several of our recent articles, I’ve introduced the concept of the “value engineer”. We have seen some engagement around this idea, so I thought I would expand in this article.

It is really hard to make software. Too hard. At times, it is utterly thankless. Working tirelessly to bring a product to market only to have it miss the mark or miss the window. As one of my favorite clients reminds me so often – time is never our friend. Team environments can be brutal without the right people and the right leadership, and once trust is lost in a team it is very difficult to regain. I’ve said to my kids that trust is lost in buckets and earned in drops. The same goes for teams and the people on them. Lack of trust leads to a toxic team environment where we end up in teams of one – each person or pocket of people working for themselves. Psychological safety erodes. Internally motivated people start to retreat into themselves.

Throw in lack of a shared understanding of a shared vision, forget about it. Nothing valuable is getting shipped. And remember, that is an agile team's single goal – to ship valuable software perceived as valuable by its users.

Now – it is not all bad out there. Despite my ostensible gloom and doom, I have the privilege of serving on some great teams – internal to Phoenix as well as my client teams. Teams that continuously adapt to change, that learn from the past and acknowledge it, that pivot when things are not working. These teams are doing great. However, it is no longer enough to learn from the past. It is now time to learn from the future because the future is happening now – right before our eyes – and it is squarely based on AI and generative AI.

The jobs in software development are changing. Somewhere between one and three years from now, we will see a rapid decline in the number of opportunities for product team members who have less than five years’ experience. And I’m talking about all the roles – from product owners to software engineers, and everything in between. Why? Because AI is eating everything, and AI-augmented software development is here. As Brian pointed out in one of our recent videos, there have been many advances in the developer automation space. Lots of copilot applications, and code generation is not as new as it seems. And that is all great. It is, however, predicated on having the right idea, the right value proposition, the right understanding, and the right language for the right audience.

Enter the value engineer. Value engineering is the process of removing all the waste and manual work in software development and letting generative AI handle that. The value engineer sits on the product development loop and curates results. He, she, or they are empowered with a new augmented product development platform that empowers them to create every artifact across the software development lifecycle with a single click (or maybe a few, but you get the idea).

We believe that a new role will emerge, one that pairs directly with the customer and the software engineer to rapidly deliver valuable product in about as much time as it takes today to achieve shared understanding (yes that can take a while but, again, you get the point). If we can understand a domain, and rapidly identify its special parts – the parts AI cannot find for us, the parts that come from decades of experience – we can create a product to enable it.

The value engineer is a modern-day superhero for modern-day product development. He, she, or they are a master product owner, a skilled test automation engineer, a powerful guerilla tester, a product designer, a software shipping solution architect – all rolled into one. And this role is emerging now. At PhoenixTeam, we are developing a product that will enable everyone to be a value engineer, and we are using it now. First it was a custom generative pre-trained transformer (GPT) to test our ideas. Now it is an (in development) AI-powered software development platform. At least that is what we hope it will be. We are still figuring it out. Join us for a preview at the MISMO AI summit in June in Las Vegas where we will show the first real version of our idea – Phoenix Burst.

• The critical role of the imagine space, where teams brainstorm ideas and determine how to add value swiftly.
• Rapid Feedback and Idea Testing
• The limitations of minimum viable products (MVPs) and advocated for maximum viable products that deliver substantial value to users, prompting immediate adoption.
• Challenges in Maintaining Shared Understanding
• The potential of AI, particularly generative AI, to expedite idea generation, validation, and maintaining shared understanding among team members.

• Their experiences in software development and the importance of learning from past failures and successes.
• The significance of AI in accelerating software development, its impact on time-to-value, and its accessibility to a broader range of professionals.
• The emergence of new roles, such as the “value engineer”.
• Current experiments with generative AI.

By Jeremy Romano, Managing Practitioner | Experience Design & Product Management

In software development, storyboarding is the essential blueprint for crafting exceptional user experiences. The product design phase and its artifacts are often the most misunderstood, underutilized, and notably absent components of the product and software development processes. Software development is more effective when guided by a design thinking methodology: Empathize, Define, Ideate, Prototype, Test, Implement, and Iterate. Products achieve optimal outcomes when they incorporate a user-centered process: Understand, Explore, Design, Evaluate, Refine, Implement, and Maintain. Despite these well-established frameworks, most organizations are constrained by resources, time, cutting corners, and making compromises that often leads to losing sight of the end-user's needs and the business goals they aim to achieve. Even the most well-intentioned and successful teams face these struggles.

Over the past three decades, I have collaborated with top-tier brands to create award-winning products. I’ve witnessed product design, a core element of delivering great software that delights customers, become significantly less important to organizations. Now, with AI and Generative AI consuming the market, we believe a window of opportunity is wide open for organizations to embrace the power of AI for product design, specifically, storyboarding.

Why did we choose storyboards for our proof of concept (POC)?

We wanted to prove that one of the most valuable tools for building impactful products can be created faster than ever before and with a high degree of quality and accuracy. While most teams lack the typical skills, time, or resources to effectively produce high-quality design artifacts, using AI to storyboard will transform how organizations visualize and communicate the product vision from the user's perspective. Taking it one step further and harkening back to Brian Woodring’s statement that “technology can be for everyone”, we want even the most artistically inexperienced people to be able to imagine and create impactful visualizations.

The hypothesis is that if AI can enable anyone to create visually compelling narratives without the associated cost and time challenges, with just the click of a button, the industry would see faster delivery, greater adoption, and happier, more productive, end-users.

Our AI Storyboarding Journey

Generative AI is powerful and challenging because it always generates a response. The scenes in our initial image generations were "awesome" but kind of useless, characterized by strangely abstract elements, wild metaphors, and a variety of artistic styles—from watercolor to oil painting, and from photorealistic to Disney-like. While the raw output from the language model was captivating, it often strayed from our intended narrative, and we watched as the age, race, and gender of our characters changed unexpectedly and with no prompt.

Insight #1: Developing our Cast of Characters

The most impactful learning from our POC was in character design. Drawing from cinema and traditional character design, we embraced the concept of using props. The language model required prompts with precise characteristics, so for us, 'props' included specifying ages, hair color, and even learning about various mustaches, beards, and hairstyles, such as the imperial mustache, curtain beard, afro puffs, and even the differences between a pixie cut, a pageboy, a bob, and a long bob. I also discovered the vast array of glasses styles available—rectangle, square, round, cat-eye, brow-line, and aviator (see Image #1). It was this level of detail that allowed us to refine and solidify our cast of characters in a more uniform fashion (see image #2).

Once we fine-tuned our approach, we were able to nail down PhoenixTeam’s cast of characters, each with a unique persona, and personality.

Insight #2: Defining the Scene Style

The second key insight was the importance of limiting the range of artistic styles for the AI. By narrowing down the color palette, specifying line weight and type, and providing a reference style for it to emulate, along with a clear focus for the "camera," we achieved more consistent and repeatable results.

Insight #3: Finding a path to maintain a coherent story flow from start to finish.

The third breakthrough involved enabling our Value Engineers to select one of our characters and a series of user stories, which, when combined with our backend integration of artistic guidelines and camera settings, empowered the AI to autonomously generate consistent and engaging narratives. This 'secret sauce' has been a game-changer in maintaining a coherent story flow from the first panel to the last (see image #3), allowing us to weave seamless narratives that compellingly drive the story forward. We are now able to generate storyboards directly from user stories and acceptance criteria without any human intervention.

AI, for now, is not a magical solution that solves all problems; its role as an incredibly powerful assistant or as John Comiskey states “your ultimate wingman”, invites us to become active and engaged curators. Generative AI possesses the transformative power to break down longstanding barriers, enabling creativity and collaboration at unprecedented scale and speed. We can generate and share ideas more rapidly, pushing the boundaries of innovation and design thinking to make better software faster. This technology allows us to shape a future where AI and human creativity unite to create more meaningful and impactful design outcomes. I invite you to join this exciting journey, and experiment with AI in your projects. Comment below to share your experiences and insights with storyboarding or other image-generating results.

By Kenny Akridge, Managing Director at PhoenixTeam

If you have been following this series and have already experimented with one or more Large Language Models (LLM) such as ChatGPT, you are undoubtedly convinced that Gen AI is completely revolutionary and that it is here to stay. If not, I encourage you to visit our earlier posts and try out some of the simple examples. Before we explore the rules, let’s get a clear understanding of what Gen AI is.

Generative AI (aka Gen AI or GenAI):a type of artificial intelligence that can create new content, pictures [like the graphic above], music, or writing, all on its own. It learns how to do this by studying lots of examples, and then it uses what it has learned to make new things that have never been made before. Think of it like a really smart robot that can draw, write stories, or compose music just by understanding patterns from what it has seen before.

Full transparency – ChatGPT generated that definition. I think it is mostly a good definition though. That brings us to the first rule.

Rule Number One of Gen AI: “AI should almost always do most of the work.”

Why would I write a definition for Gen AI from scratch when the AI can do it for me? I have applied this rule so often for a little over a year now that it has become second nature. To a large degree, it has replaced Google for me. Need to write a complex Excel formula? Gen AI can do that. Need to OCR something, Gen AI can do that. Want to find patterns in a series of data? You guessed it - Gen AI can do that too. There really isn’t much that it can’t do. I find myself constantly telling people around me to “just let the AI do it.”

So, this is amazing, right? I mean, ChatGPT can complete most of the work for my tasks. You might even wonder if this article was composed by AI. Sadly, ChatGPT did not write this article. It would have saved me a lot of time for sure. Unfortunately, this article falls outside of the “almost always” condition of the rule. When I need something to be genuine, to be authentic, to be delivered in my own voice, there is no substitute (at least not yet). That said, I did consult with ChatGPT on many aspects of this article. “Is ‘almost always’ a clause?” ChatGPT said no, it’s a stipulation or a condition.

Ok, so why does Gen AI only do most of the work? Enter the second rule.

Rule Number Two of Gen AI: “You almost always need a human for the last mile.”

I borrowed the term “last mile” from the transportation industry. In short, it means “The final leg of delivering goods or services.” Despite all its powerful abilities, Gen AI doesn’t always produce high quality outputs with acceptable accuracy. Sometimes it guesses and guesses wrong. Sometimes it hallucinates – tells fiction as if it were truth. That’s why most work product needs to be reviewed and polished by a human.

Let’s expand on the human element in AI-assisted application development. Building on these rules, we hypothesize that an artificial intelligence-based collaboration framework, enhanced by both machine and human content curation, will dramatically reduce the time to value in software development. By integrating human insight and feedback directly into the AI development and deployment cycles, we can create high quality software much more rapidly. We will dive deeper into this process in future articles.

You've made it this far... so make sure to hit that follow button to stay connected. We're here to share the latest and greatest insights on all things AI including upcoming interviews from AI experts.

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Check out previous articles from our AI series:

• Starting our AI Journey
• The Peanut Butter and Jelly AI Experiment
• 3 Simple Steps to Kickstart your AI Journey Today

Written By Tela Gallagher Mathias , COO and Managing Partner, PhoenixTeam

Part of the scope of our MVP was to create storyboards, which was actually really hard. I highly recommend running your own peanut butter and jelly experiment. It’s super easy. Here are the steps:

1. Open up ChatGPT.

2. Enter the following prompt: “describe the process used to create a peanut butter and jelly sandwich”.

You’ll find that chat GPT provides an awesome answer, very thorough and just right. Perfect grammar, with all the steps described. Now the fun part.

3. Enter the following prompt: “create a storyboard illustrating the process”.

I’ve run this experiment a dozen times and it is always fun, but the best result remains the one achieved by Tom Westerlind.

What I love about this experiment is how profoundly it reveals both the awesome power and the hilarious limitations of the “today” technology. I say “today” technology because the tech is moving at a breakneck pace. I’m sure the absurd (and at times, disturbing) deficiencies will resolve in months, but I love discovering and overcoming these problems.

The more you stare at this image, the weirder it gets. It starts great – every hungry person ever, staring out the window posing the constant question to themself – “I’m hungry, what am I going to eat?”

As the steps progress, you can see the AI is trying to get it right, it has hints of the process it described so well just a moment ago, but what’s up with the cigarette in (the first) pane three? And those bowls of peanuts and fruit – are we doing tableside PB&J? Why would we use such a huge knife and a spoon to cut our sandwich? It goes on and on.

We’d love to hear from you in the comments on what you see, also feel free to post your image results.

By Tela Gallagher Mathias , COO and Managing Partner, PhoenixTeam

As I mentioned a couple of posts ago, we started our AI journey because we saw both opportunities and threats to our business. So much more than staying relevant, we want to lead the change that is coming with the commonplace availability of the awesome power of generative AI. As I say often, we want to be the change we want to see in the world. That's why I co-created PhoenixTeam almost a decade ago. We started this company to create a place and a culture we would want to be a part of.

You may be wondering where to start your AI journey. Here are the three things you should literally do today.

1. If you haven’t already, create an account with chat GPT and run the peanut butter and jelly experiment from our last post. Be smart guys, don’t put anything confidential or proprietary in there. Unless you are using a team account, OpenAIuses what you put in to train its models.
2. Reflect on what this means to you and your business. Ask yourself – how does this technology apply to my business?
3. Make a list of three people you need to talk to that can help you figure out what you are doing today that leverages AI.

AI technology has been around for decades, just not at the scale you see today, so don’t worry – you are probably doing more than you think. Using optical character recognition (OCR)? That’s AI. Robotic process automation (RPA)? Yes, that’s AI. Chat bots? AI. You get the picture. Tell us what you find out.

Written By Tela Gallagher Mathias , COO and Managing Partner, PhoenixTeam

In late February, we kicked off our #AI journey. As a company that specializes in the design, delivery, implementation, and care of technology solutions, we saw both the opportunity and the threat that AI and generative AI brings to us.

The opportunity – AI augmented product development will empower us to dramatically reduce time to value for our customers. We will be able to test ideas faster and with less waste.

The threat – AI and generative AI will change the talent required in software development in radical ways that we do not yet understand. The jobs required to make software will change.

The facts – Pretty much everyone uses some variation of the same process to create software solutions. If we reduce the time it takes to complete the fundamental steps of software development, we will reduce time to value.

Our hypothesis – we can use #generativeAI to create the basic artifacts of software development faster than traditional approaches.

So, we kicked off a six-week proof of concept with a very talented team of Phoenix practitioners. We started with a plan and then we changed our plan as we learned. Opportunities to add value presented themselves so we pivoted a few times. What didn’t change was our main goal – learning.

We just finished our POC and where we ended up was with a custom GPT that creates context-specific training materials, storyboards, user stories, and acceptance criteria.

It was very refreshing to take something to production in literally days. Once we figured out what we wanted to do, we were up and running in less than a week. We learned a lot and there is more to come as move to the next phase of our AI journey. We’ll keep you posted.

The Agile + DevOps East Conference 2023, a TechWell Corp event, was full of innovative ideas, methodologies, and learnings. This annual gathering serves as a melting pot for industry experts, thought leaders, and practitioners, all sharing invaluable experiences, revelations, and strategies essential for navigating the ever-evolving landscape of software development.

Among our PhoenixTeam attendees was Noah Krueger, Senior Practitioner and Senior Software Engineer. Noah attended numerous sessions jam-packed with cutting-edge Agile methodologies, DevOps practices, and the impact of AI on software development.

Let's dive into his key insights and takeaways from the event ranging from AI, Agile, Leadership and much more:

1. A company’s digital transformation is an inevitable journey that must be handled with care and includes planning and discussions around the tools used, how the teams will pivot to accompany the transformation, and flexibility on the price and timeline. Platform engineering is a necessary role in companies that want to produce and support home-grown tools needed to maintain a digital presence and be at the forefront of their industry.
2. Healthy leadership requires fostering a team culture of purpose, co-intelligence, and autonomy resulting in a resilient learning community.
3. AI has the potential to enhance various aspects of a low-performing engineering team by analyzing metrics, identifying problem areas, generating mockups, and creating better unit tests. It can positively impact deployment frequency, failure likelihood in production, lead times, and recovery time.
4. #GenerativeAI will be a crucial tool (not a replacement) for quality assurance teams as it speeds up the ability to create unit tests and other automated scripts. Understanding how to leverage this tool will help differentiate tech workers in the future. Still, we must also be mindful of its biases and make sure we are not blindly trusting the artifacts that it generates.
5. Generative AI is a tool that can be used across every aspect of the software development lifecycle, including generating requirements with acceptance criteria and supporting UI mockups, system architecture design, and generating code.
6. Large technology organizations that want to scale can easily become hampered by slow team member onboarding, inconsistency in tooling across different teams, high cognitive load demands, and a lack of reusability across the organization. Partnering with a digital modernization partner, like Stackspot, can smooth out these challenges and maintain a high level of productivity and satisfaction amongst technology teams.
7. GitHub actions are a feature that many teams are not taking advantage of, even when their code repositories are stored in GitHub. By streamlining and automating most DevOps steps, software teams can eliminate many tedious tasks, go to market faster, focus on product innovation, and implement features that increase customer satisfaction.
8. There is a growing fear in the tech industry that AI will replace developers, but this concern conveniently forgets that we have leveraged intelligent tools for decades, such as drag-n-drop components, Integrated Development Environments (IDEs), IntelliSense, and many more. The trend has been that the tools are getting better, but more is needed to offset the growing responsibilities of developers. Generative AI will simply become another tool that lifts the tedious tasks off the team’s shoulders, allowing them to focus on the more innovative and challenging work.
9. Estimating is a common task in software development that usually relies on our past to guess the future but has two major issues: our biases and the tendency to rely on averages. Instead of relying on averages, using the data from start and end times, one should be able to provide ranges and probabilities about when projects will be completed.

Our team's engagement in this event showcases our dedication to our clients. We remain at the forefront of innovation and agile methodologies, leveraging transformative technologies to elevate our software development practices. These takeaways will be inputs to PhoenixTeam's strategies and culture of continuous learning, adaptability, and empathy. As tech continues to rapidly advance and new exciting opportunities come our way, we will adapt accordingly, responsibly and transparently.

Follow PhoenixTeam on LinkedIn for the latest news and updates.

Sessions Attended: Lead Without Blame - Building Resilient Learning Teams ◆ Digital Transformation Pitfalls, and How Cloud Development Environment Platforms Can Change Everything ◆ How AI is Shaping High Performance DevOps Teams ◆ The Potential of AI & Automated Testing: Conquer Test Script Challenges with AI ◆ AI-Powered Agile + DevOps: The Future Starts Now ◆ Case-Study - IT Delivery up to 70% faster ◆ Coding at the Speed of Business: Impacts of Modernizing and Scaling the Developer Experience in a Large Bank ◆ Automating Repetitive Tasks with GitHub Actions ◆ AI and the Future of Coding ◆ Chasing Predictability with AI: The Model of You Outperforms You

The MISMO Winter Summit was jam-packed with insightful discussions, forward-looking sessions and it was also a celebration of MISMO’s 25-year anniversary of industry collaboration and standards development. Our team led 15 workgroup sessions, covering the latest standards in mortgage technology, credit reporting, and much more.

Let's dive into the key insights and takeaways from the summit:

Embracing New Technologies:

A common theme of the summit was the focus on new technologies to streamline the mortgage process. Discussions and sessions highlighted the importance of enhancing security and transparency in transactions. The integration of AI, blockchain, and other emerging technologies is not merely a vision of the future, but a reality poised to reshape the mortgage landscape.

Legislative Developments- The VA Home Loan Awareness Act of 2023:

The proposed legislation brings greater awareness to the loan products available to Veterans by incorporating a disclaimer on the Uniform Residential Loan Application (URLA) to increase awareness of the direct and guaranteed home loan programs of the Department of Veterans Affairs. The VA Home Loan Awareness Act of 2023, detailed in a DS News story, is a bipartisan effort that underscores the industry's continued efforts to highlight the VA home loan benefits available to those who have served our country.

New Initiatives: MISMO Certifications, Fee Modernization and Loan Servicing Data Standard

MISMO announced its new certification opportunities, including the MISMO Certified Consultants program and PhoenixTeam is proud to be an early applicant. “The new MISMO Certifications are designed for professionals and technology providers who demonstrate their commitment to effective business practices through their expertise in, and adherence to, MISMO standards,” said David Coleman, President of MISMO. “As adoption of MISMO standards continues to accelerate, we have more industry partners interested in pursuing the variety of certifications we are offering.”

MISMO is seeking participants for a NEW Fee Modernization Workgroup! VA announced plans for a collaborative effort with other federal housing agencies to develop a loan servicing data standard – more to come!

More Insights from the Sessions: AI, Credit Reporting, FHFA and SMART Docs®

• The Emerging Tech Community of Practice session debuted an Emerging Tech updates segment that included trends, articles, and projections about emerging technologies such as AI and Blockchain.
• The Artificial Intelligence panel discussion provided an engaging conversation around “The Missing Middle” and how to bridge the gap between ‘human only activity’ and ‘machine only activity’. Brian Stucky from Rocket Mortgage refers to this as “The Missing Middle.” Human only activities include components such as leadership, teamwork, empathy, judgement, and social skills. Machine only activities include accuracy, speed, scalability, quantitative capabilities, and iteration. The “missing middle” are hybrid activities that can train, explain, sustain, and interact. The implementation involves leveraging AI to perform tasks that don't require human intervention, such as interpreting loan documents and assisting with origination systems. As a result, bankers are obtaining answers approximately 70% faster.
• The Federal Housing Finance Agency (FHFA) shared an update on what it learned during recent stakeholder forums discussing how data digitization might drive transparency, increase access, fairness, affordability, and sustainability in mortgage lending. The stakeholder forums, focused on historical data and sequencing of events, had broad industry participation. FHFA acknowledged the industry preference for “big bang” vs. a phased approach implementation of the credit score changes. It also reiterated movement of the bi-merge implementation timeline and expect to announce upcoming FHFA stakeholder forms by the end of January. Potential topics for future forums may include Gaming, Fair Lending, Cost to Consumers and Consumer Disclosure.
• Although the implementation date for the credit score changes is pending, the Credit Reporting CoP continued their effort to develop implementation guidance of the MISMO standard and the representation of the FICO 10T and Vantage Score 4.0.
• The use of SMART Docs®, remains a focus with the introduction of Version 3 which introduces the ability to verify data and documents while also being tamper evident.

We are grateful for the insights gained and more excited to put them into practice as we continue to execute our federal and commercial clients’ value delivery goals.

Follow PhoenixTeam on LinkedIn for the latest news and updates.

#MISMOWinterSummit #MISMO #ContinuousLearning #Adaptability

• • The common challenges faced by servicemembers transitioning from active duty to veteran status.
  • Rediscovering your identity after dedicating decades to military service.
  • Identifying military skills that seamlessly transition to success in the corporate/civilian sector.
  • Strategies for translating learned skills into a corporate setting effectively.
  • Storytelling as a powerful tool to convey experiences and connect real-world examples to tangible results.
  • Personal challenges encountered during the shift from Active Duty to Civilian Life.
  • What to look for in a company as you are seeking to join an organization as a Veteran.
  • How Blue Phoenix and Blue Bay help Veterans prepare for employment.
  • Details about PhoenixTeam’s newly approved Department of Defense (DoD) SkillBridge Program and its upcoming cohort.

John Trodden, a Marine Corps Veteran, and Blue Phoenix's Managing Partner, believes every Veteran deserves a chance to succeed in the community beyond their tour of duty. After the DOD announced PhoenixTeam's approval, John reflected on his history with the SkillBridge program saying, "When I decided to retire after 25 years of service, my time as a DOD SkillBridge intern was invaluable….Talk about things coming full circle— now that PhoenixTeam is an officially verified SkillBridge Organization, I have no doubt they will catapult the careers of well-deserving veterans as they did for me. ”

“We are humbled and grateful by this honor, for the fourth year in a row, to be recognized by Inc. as one of the fastest growing, privately held companies in the nation,” said Tanya Brennan, managing partner and CEO of PhoenixTeam. “PhoenixTeam’s incredibly talented team continues to deliver value and delight our clients with innovative solutions for their most difficult challenges. Focusing on the mortgage industry, it’s a privilege to have the opportunity to help federal housing agencies and commercial sector clients deliver better outcomes for veterans, farmers, and homeowners.”

“PhoenixTeam has supported USDA RD on multiple key initiatives over the past five years. They provided business advisory support services for GUS 2.0 to launch the new URLA; helped us develop the Business to Government loan and underwriting specifications leveraging MISMO standards; and, most recently, [are] leading the development of the Direct Loan Program loan origination replacement solution.”—Dean Daetwyler, Director, System Implementation and Management Division, USDA SFH

A New Venture: Blue Phoenix

1. Start with the end in mind. What do we want our product to be like in production? What are all the things that must happen before a quality product or feature gets into the users' hands?
2. Determine the items the team has the capability and the capacity to complete during the given sprint or timebox. This becomes the team's definition of done.
3. Assess the tasks that fell off in the second step to determine how to accomplish them "now" and create a consolidated list of items the team can use to improve the efficiency and quality of their process and product over time.

“PhoenixTeam has supported USDA RD on multiple key initiatives over the past five years. They provided business advisory support services for GUS 2.0 to launch the new URLA; helped us develop the Business to Government loan and underwriting specifications leveraging MISMO standards; and, most recently, [are] leading the development of the Direct Loan Program loan origination replacement solution.”—Dean Daetwyler, Director, System Implementation and Management Division, USDA SFH

A New Venture: Blue Phoenix

“PhoenixTeam seeks to enable affordable and accessible homeownership for all Americans through customer-centric technology solutions. With this award, PhoenixTeam now has the unique opportunity to serve HUD Federal Housing Administration, the Department of Veterans Affairs Loan Guaranty Service, and the Department of Agriculture Rural Development Service. We help each agency achieve its modernization goals to best help low- and moderate-income and first-time homebuyers, veterans, and borrowers in rural areas. It is an honor to serve those who serve.” said Tanya Brennan, PhoenixTeam CEO and Managing Partner.

"Launching Blue Phoenix one month ago is a vision realized," said John Trodden, Managing Partner. "Announcing admission on the GSA Schedule today marks another milestone for us, one that allows Blue Phoenix to offer its expertise and services to help our United States Federal Agencies deliver on their technology promises and visions." Trodden, who served in the Marines for over 25 years, says, "Blue Phoenix's mission to "serve those who serve" is rooted in its commitment to provide Veterans employment opportunities as they transition into civilian life. We highlight and lean into the leadership skills and military fundamentals our service members used day in and day out to achieve mission success. As a result of the industry's overwhelming interest in partnering with Blue Phoenix, we are thrilled to expand our services to serve all federal agencies in their journey to achieve their technology vision and mission."

“Becky exemplifies everything it means to be a Phoenician,” said Tom Westerlind, Managing Partner and CFO of PhoenixTeam. “She is caring, open, determined, highly skilled, highly capable, amongst many other amazing qualities. I’m thrilled she’s joining the partnership and feel blessed for the continued opportunity to learn from and grow with her.”

PhoenixTeam ranks at No. 45 with 269% growth rate.

“It is an honor to be recognized by the Inc. 5000 as one of the fastest-growing, privately held companies in the Mid-Atlantic region three years in a row,” said Tanya Brennan, Managing Partner, and CEO of PhoenixTeam. “We are humbled. We are grateful. We are excited for what we have planned to help even more federal housing agencies and commercial sector clients deliver their mission and business goals resulting in better outcomes for veterans, farmers, and homeowners.”

Five Years of Service

Contact

PhoenixTeamMelanie Lewis, Partner904-868-5828melanie.lewis@phoenixoutcomes.com

About PhoenixTeam:

PhoenixTeam is a technology company that specializes exclusively in the design, delivery, and care of mortgage technology solution in the federal and commercial spaces. Our dream is to enable affordable and accessible homeownership for all Americans through customer-centric technology solutions. We believe that by bringing joy and purpose back to software development, while bridging the gap between technology and business teams, we can really make a difference in the lives of clients and homeowners everywhere. PhoenixTeam is a woman and minority-owned small business headquartered in Arlington, Virginia. For more information, visit us at www.phoenixoutcomes.com.

• We celebrate people who try new and better ways of doing things, regardless of the outcome.
• Management trusts people to do a good job without watching over their shoulders.
• When I look at what we accomplish, I feel a sense of pride.
• PhoenixTeam is a great place to work.

View PhoenixTeam’s full Great Place to Work profile and recognitions at https://www.greatplacetowork.com/certified-company/7039913.

ContactPhoenixTeamMelanie Lewis, Partner904-868-5828melanie.lewis@phoenixoutcomes.com

• The company celebrates people who try new and better ways of doing things, regardless of the outcome.
• People here are treated fairly regardless of their sexual orientation, race, or age.
• Management trusts people to do a good job without watching over their shoulders.
• When I look at what we accomplish, I feel a sense of pride.
• PhoenixTeam is a great place to work.

View PhoenixTeam’s full Great Place to Work profile and recognitions at https://www.greatplacetowork.com/certified-company/7039913.

How We Are Fighting Breast Cancer

• To learn more about breast cancer, click here.
• To view organizations to donate to, visit this link.
• For instruction on how to perform a breast self-exam for early detection, visit this guide by the NBCF.
• BreastCancer.org provides an outline of potential risk factors for developing breast cancer.

• We celebrate people who try new and better ways of doing things, regardless of the outcome.
• Management trusts people to do a good job without watching over their shoulders.
• When I look at what we accomplish, I feel a sense of pride.
• PhoenixTeam is a great place to work.

View PhoenixTeam’s full Great Place to Work profile and recognitions at https://www.greatplacetowork.com/certified-company/7039913.

"Financial technology and housing are some of the least diverse sectors, but we need to reflect the diversity of the customer base," said Tela Mathias, managing partner and COO.

"These things don't come into your understanding naturally," Mathias said. “You have to make room for them.”

Leaders and companies are recognized for programs, practices and policies that advance LGBTQIA+ equality and leadership in the workplace throughout the Greater Washington Area and beyond.

“As Dr. Martin Luther King Jr. so famously reminded us, ‘the arc of the moral universe is long, but it bends towards justice.’ This award reminds me that when we have power and privilege, it is our responsibility to use it to bend the arc towards justice,” says Tela Mathias, Managing Partner and COO. “To create access where it did not exist previously and invent a new way of doing business, one that realizes fairness and a commitment to the core values we practice at PhoenixTeam. It’s in our blood as a company and a community.”

An exhausting, but amazing experience that brought a cross-functional group together to form a team to create and test an amazing prototype that set the direction for our next release within a week. Senior Business Strategist

The Outcome

1. End-users did not have independent visibility into key performance and business metrics
2. Users performed work differently, focusing on different metrics at different times
3. There was a lack of trust on the quality of the data and metrics
4. End-users had an inconsistent work prioritization processes
5. Users expressed desire for peer-to-peer performance stack ranking
6. New users faced training and adoption challenges
7. Users wanted visibility into “effort” metrics
8. End-users wished for more intuitive icons
9. The tool’s expected functionality was not consistent across all users

• Facilitating Design Sprints
  • We validate assumptions, accelerate learning about users and their environment, and generate viable and valuable solutions to users’ most urgent challenges.
• Modifying Traditional Design Sprints
  • Our team can help identify the next set of valuable features for existing products or prototypes with limited testing, validation, or user feedback behind them.
• Designing Organizational Structure
  • We streamline processes to allow for small cross-functional teams to accelerate discovery and delivery of value.
• Crafting Customized Rapid Prototyping
  • We host workshops to identify, validate, align, and communicate around value and functionality of products and their features.

www.phoenixoutcomes.com

• 99% of team members are engaged with “high favorability, advocacy, intent to stay, and discretionary effort”
• 96% of team members feel that “senior leaders of the organization value people as their most important resource”
• The top word that best describes the PhoenixTeam environment according to the survey respondents is “supportive”

PhoenixTeam ranks at No. 41 with two-year 244% growth rate.

• PhoenixTeam made the 2021 Regionals Mid-Atlantic at No. 52, raising the ranks by 11 spots since then.
• Inc. Magazine's Inc. 5000 list of the fastest-growing private companies has featured PhoenixTeam twice before as No. 911 in 2021 with a three-year company growth of 242% and revenue increase of 532% and as No. 1160 in 2020 with a three-year revenue growth of 389%.
• Phoenix received recognition as an Best Workplaces in 2021 in honor of our continuous efforts to create a workspace that cultivates growth, value, and offers flexible work hours, affordable health insurance plans, and unique benefits.

• Title & Closing CoP informs industry professionals on how to manage data elements needed to order, update, or cancel title services from a title insurance company.
• Mortgage Insurance CoP teaches practitioners to develop data specifications and mapping guidance.
• Fee Collaboration DWG instructs mortgage experts on how to develop and promote application fee names and definitions for universal understanding.
• Credit Reporting CoP focuses on the data elements that request, track, and cancel credit reports in mortgage lending.
• Loan Application Data Exchange DWG explains the business-to-business exchanges that reflect data in the Uniform Residential Loan Application (URLA).
• Private Label DWG documents the development of a standardized mortgage asset dataset for residential mortgages.
• Business Reference Model CoP helps mortgage professionals to develop a business practice model for events throughout the loan process.
• Property & Valuation CoP analyzes property reporting through valuation services and collateral risk assessment.
• AWG Brown Bag covers the development of the Uniform Mortgage Data Program (UMDP) loan application with industry professionals.
• MISMO 4.0 Next Gen DWG, addresses the new MISMO Reference Model and new challenges in the industry and technology.
• Closing Instructions DWG, creates standardized closing instructions for lenders and closing agents.
• Architecture Work Group reflects on the viability and best practices for using Metadata.
• Origination CoP looks at the origination lifecycle and process of application, underwriting, and closing with MISMO attendees.
• Mortgage Insurance CoP develops data specifications and mapping for mortgage insurance.
• Servicing Transfers DWG shares the practice behind building a cost-effective base process.
• Business Glossary DWG informs participants of vital mortgage industry language using the MISMO Business Glossary.

There is no greater gift than the time you get to spend with a friend or a loved one. With Matt's love for music, he would choose to spend some time with James Marshall Hendrix jamming to some music.

As for a dream home, "how cool would it be to live in a cob construction home?" Matt loves the look and sustainability of these homes and would love the opportunity to live in one.

Matt's college education in fine arts didn't bring him to his career in IT, it was the need to have job stability and the ability to financially support his family. "You can't go wrong with a career in computer science, and it is something I have really found a passion for."

Matt was drawn to PhoenixTeam by the amazing things he heard about the company culture from a fellow team member. "I can crunch data anywhere, but having an awesome group of fun people in the trenches with you every day is what takes it to another level!"

Meet Gloria, a Business Analyst at PhoenixTeam. Spending time outdoors with family, friends and a good glass of wine tops her list! She is always ready for time spent listening to music, enjoying a picnic, engaging in a challenging hike, or a swim at the water park.

Her college career path was in early childhood education, but she is truly grateful for life taking her down a different path, ultimately finding herself at PhoenixTeam. What most drew her to PhoenixTeam was the opportunity to learn skills necessary in today’s world. Gloria enjoys the ability to grow in her career while learning from the incredibly skilled and knowledgeable people at PhoenixTeam.

For 2nd Consecutive Year, PhoenixTeam featured on the 2021 Inc. 5000 List of America's Fastest-Growing Private Companies

PhoenixTeam is excited to announce that we are now a Salesforce Partner! Phoenix is no stranger to Salesforce as most of our client engagements involve Salesforce Product and Implementation leadership. The uniqueness of this partnership comes from the combination of our Salesforce strength with our mortgage and financial services industry mastery. Reflecting on the partnership, Jake Artz, a PhoenixTeam Consultant specializing in Salesforce says, “solidifying Phoenix’s position with Salesforce allows us to tell the world what our clients already know, that we are the most specialized mortgage technology industry consultants in the Salesforce space.”

We are a one-of-a-kind consulting firm. Our focus is mortgage technology and delivering high-value outcomes to our clients with speed, precision and quality. It takes skill, focus and confidence to do this regardless of how “hard” the problem is. Paul Weakley, PhoenixTeam Partner speaks for us all when he says, “This is a door opening opportunity to help our clients and future clients leverage Salesforce to solve business and customer problems. We look forward to impacting and adding value to mortgage technology financial services organizations seeking to leverage the power of Salesforce.”

Salesforce has been part of Phoenix’s strategic technology vision and this partnership cements it on our roadmap for years to come. We look forward to showing the industry the power of uniting Salesforce with the Phoenix mindset to deliver impactful changes and outcomes. Visit our website to read our Salesforce blogs and download our Salesforce Leadership fact sheet. So much more to come – Stay tuned and thanks for the support!

• Be honest about your productivity and set realistic expectations. Keep the communication lines open with your leadership team and know this is a judgment-free zone. This can help reduce the feelings of being overwhelmed and decrease stress.
• Make a dedicated workspace at home to create healthy boundaries between work life and home life. Choose a space that is filled with natural light, as it increases productivity and lifts your mood.
• Set boundaries. It is just as important to ‘arrive’ at work and ‘leave’ work every day, even when you are working from home. When you leave your home office, leave it and do not look back. Unplugging from your job provides mental relaxation and helps you recharge to be your most productive when you ‘arrive’ at work the following day.
• Meditating and moving your body are both good for your mind. Sit down, close your eyes, and think about your breath as you inhale and exhale. Cardio exercise gets your blood pumping and prompts your brain to release mood-lifting endorphins. PhoenixTeam encourages movement and exercise with a quarterly step challenge offered to employees. We create teams and team members download an app that keeps track of their movement. The team with the most steps gets bragging rights of “The Most Active Team.” This is a fun and creative way to keep employees active. Dedicate as little as 15 minutes a day for this to give your mind a midday break.
• Emotional support during this time can be crucial to mental health. Lean on friends, family and colleagues for emotional support. Reach out to talk about feelings and everyday life occurrences. At our PhoenixTeam daily virtual coffee breaks, team members are empowered to talk, unload feelings or just seek advice from co-team members. Step outside and take a deep breath of fresh air! Keep active with hobbies and activities that make you happy and relaxed.
• Above all else, be kind to yourself! Taking care of you should be your first priority because productivity is not the only measure of success, happiness is just as important.

Arlington, VA May 12, 2021 – PhoenixTeam has been named to Inc. magazine’s annual list of the Best Workplaces for 2021. The list is the result of a wide-ranging and comprehensive measurement of American companies that have created exceptional workplaces and company culture whether teams are operating in person or remotely.

Long before PhoenixTeam was formed in 2015, PhoenixTeam’s Managing Partners envisioned a company that put its people first. A place where independent thought, growth and learning was paramount and a space where creativity, openness and empathy was celebrated and encouraged. PhoenixTeam holds this vision as close to our hearts today as then because that is what makes us unique and differentiates us from other companies.

Collecting data from thousands of submissions, Inc. singled out 429 honorees this year. Each nominated company took part in an employee survey, conducted by Quantum Workplace, on topics including management effectiveness, perks, and fostering employee growth. The organization’s benefits were also audited to determine the company’s overall score and ranking.

“PhoenixTeam would like to thank Inc. for recognizing us as one of the 2021 best places to work, says Tanya Brennan, CEO and President of PhoenixTeam. “When we started this company, we envisioned a place where we would want to work ourselves. A place that inspires our people and provides them opportunity to achieve their goals and make an impact on the world. Six years later, having grown the team to almost 100 people, we continue our journey to inspire our people to be their very best.”

“The definition of a positive workplace has changed drastically over the past year,” says Inc. magazine editor-in-chief Scott Omelianuk. “Stocked fridges and nap pods were no longer perks many companies could rely on once work went remote. So, this year’s list is even more important as it reveals organizations that continue to enrich the lives of its employees amid a pandemic.”

About PhoenixTeam

PhoenixTeam is a women-owned small business that provides mortgage technology solutions to improve the home purchase experience for borrowers and help financial institutions, technology providers, and government agencies achieve their desired outcomes. PhoenixTeam specializes in serving federal housing agencies and the mortgage industry, helping deliver better business solutions through technology implementation excellence. For more information, visit www.phoenixoutcomes.com.

Meet Jennifer Drahota, who has dreams of someday writing and publishing her own book. Although she has not figured out what it is going to be about, but she is sure that it will involve tidbits from her crazy and rewarding personal and professional experience. She is taking notes along the way.

‘Quit focusing so much on everything around yourself instead of what is right in front of you. Be diligent with what you have in this moment’. This advice has served her well, most recently in parenting her two young daughters as well as keeping her grounded all around.

Jennifer is proud to be part of this team and loves that at PhoenixTeam, there is so much amazing talent, diversity and leadership of all the team members.