Let’s face it – Artificial Intelligence usage in our modern world has already passed the tipping point. It is no longer a concept or only used by early adopters; it is officially everywhere. We as a society get to see AI evolve in real time, and if we as individuals embrace it, we become part of that continuum. In my lifespan, I first encountered AI in the mid-90s when I watched Hal 9000, a sentient AI computer, sabotage a space mission in 2001: A Space Odyssey, then in the actual year 2001 when I went to see the movie AI strictly because I wanted to see how Spielberg finished the film that Stanley Kubrick started making. Fast forward to the past 24 months, and I have heard numerous AI TED talks, taken AI courses, and conducted a ton of market research to potentially identify which AI companies to invest in. As a Chief Product Officer for a technology company that caters to the mortgage servicers industry, I hear a lot of chatter about AI. Still, its adoption rate is relatively low, especially outside of loan originations. And I get that. Adopting AI can feel like jumping onto a moving escalator. Not to mention, we all have our day jobs that consume our time. This blog is designed to help educate mortgage servicing professionals on different AI tools, approaches to creating an AI strategy, and how to implement an AI tool safely within your organization.
Let’s start by identifying the two most likely branches of AI that will bring efficiency gains to most businesses: Predictive AI & Generative AI. Both of these branches of AI rely heavily on AI learning models. In other words, these AI subsets use data to learn and subsequently take action.
Predictive AI is a branch of artificial intelligence that uses data models to identify patterns and relationships and predict future outcomes. Predictive AI is used in various industries, including Supply Chain Management, Finance, Marketing, and Insurance, to name a few. The benefits of this branch of AI are that it can improve decision-making, reduce operational and regulatory risk, enhance efficiencies, and ultimately increase profitability.
Main takeaway: If you want AI to make decisions, you will most likely achieve this by pursuing a Predictive AI tool.
Generative AI is a branch of artificial intelligence that uses data models to generate new content with similar characteristics. Generative AI can create new images, text, videos, voice narrations, and even music. Generative AI is used in various industries, including Manufacturing, Software Development, Media and Entertainment, and Healthcare. The benefits of this branch of AI are that it can improve the customer experience, enhance creativity and innovation, and increase productivity.
Main takeaway: If you want AI to create something new, you will most likely achieve this by using a Generative AI tool.
Choosing between these two technologies doesn’t have to be an either-or option. Enterprises can adopt both generative AI and predictive AI, using them strategically in tandem to benefit their business. (ibm.com)
This may seem like a loaded question because most businesses can benefit from Predictive or Generative AI tools. As with any product roadmap or business strategy, the decision of what to work on next should be based on a vision of where you want your organization to be over a defined time span. You may want to increase the customer experience to grow your customer base or create operational efficiencies to decrease overhead and increase margins. Get with your organization stakeholders to create an AI roadmap and be sure that various departments are engaged and committed to working towards the goals holistically because implementing AI will require cross-functional collaboration.
A key thing to note is that you must pick an AI strategy your company can execute. What this equates to is you need to ensure that you have the correct data modeling and strategy to appreciate AI’s benefits. As a general rule, these AI tools need access to a lot of data attributes and large sample sizes to be accurate and create the intended benefits for your organization. In other words, be practical and realistic about what you can accomplish and make a comprehensive plan.
43% of data leaders say that data quality, completeness, and readiness are among the biggest obstacles preventing GenAI initiatives from reaching the finish line. (informatica.com)
Identify the business problems you are trying to solve. Stating that you want AI because everyone else you know is using AI is not a plan. Start by identifying areas of improvement or growth objectives you want to solve with AI and establish use cases that support those goals.
First and foremost, determine if your use cases could benefit from the utilization of Predictive or Generative AI tools. Once this is done, conduct research on the AI tools that best align with your needs. Make sure the AI tools offer APIs so that the selected AI tool can integrate with your internal systems.
Working with your IT and Dev teams, identify which internal systems hold the necessary data. Then, extract and prepare data from your internal systems in a format compatible with the AI tool. This process will likely include cleaning, structuring, categorizing, and labeling the data in your internal system.
Once your data is prepped, assign your product and technology teams to work with the AI tools IT team to set up the integration between the two systems. This process will require data mapping to ensure that the data fields from your internal system match the corresponding fields within the AI tool and vice versa.
Work with your technical team to ensure you have implemented security measures to protect sensitive data during system transfer. Remember that you are sending critical data to a third-party vendor, and it is paramount to ensure your customers are protected.
A successful integration with an AI tool doesn’t mean it’s time to go live. The AI tool needs to be trained. This process entails a lot of trial-and-error testing once you have deployed in a UAT environment and begin user acceptance testing, as it will create opportunities for real-time analysis. Emphasize establishing measures, enabling your team to review what the AI tool predicts, decides, or creates before it reaches your customers. This will ensure you don’t develop scenarios where the AI tool creates bad customer experiences or causes compliance issues for your organization once in production.
Create a cadence where stakeholders within your organization regularly monitor the AI tool’s performance. The discoveries and feedback from this exercise will help you make any necessary adjustments to improve accuracy and increase adoption.
We are living in a very exciting era where we get to see AI evolve in real time and have the opportunity to appreciate significant benefits from adopting AI tools. While many different AI methods are available today and many more are on the horizon, don’t let yourself get overwhelmed. Focus on Predictive and Generative AI tools, as those are the two branches that will most likely create big wins for the organization. Once you have created an AI goal, create use cases and ensure your organization has the proper data modeling. Create a step-by-step plan on how your team will implement the AI and leave a lot of time to train the AI tool. Once integrated and deployed, slowly throttle the adoption of the AI tool to make sure the AI tool is trained and is achieving the defined goals. Remember that success and growth are iterative, like many things in product and technology and life in general.
Rob Pajon is Chief Product Officer at OrangeGrid and has spent 17 years as a B2B SaaS product professional in the mortgage servicers industry. OrangeGrid is a workflow automation tool that can create workflow steps to trigger and receive data from AI tools its customers rely on. For any further information or questions regarding this blog, you can email him.
