9 Ways to Evaluate and Incorporate AI into Your Technology Strategy
Today's technology leaders face critical decisions about implementing artificial intelligence in their organizations, requiring strategic approaches backed by industry experts. This article presents nine practical methods for evaluating and integrating AI solutions that address genuine business challenges rather than pursuing trendy technologies. Drawing from expert insights, these strategies emphasize identifying operational pain points, measuring concrete results, and building value through pragmatic, iterative processes.
Map Bottlenecks Before Testing AI Solutions
I've taken a very intentional approach to incorporating AI and machine learning into our operations. I started by mapping out where time and decision-making bottlenecks were slowing us down, things like data analysis, forecasting, and process documentation. From there, I tested a few AI solutions to see which could actually replace manual work instead of just adding another layer of complexity.
For example, we integrated AI into our financial modeling and forecasting workflows to identify trends and cash flow risks faster, and used automation to clean and visualize data across platforms like Fathom and Looker. On the operational side, we implemented AI agents to summarize client meetings, build draft reports, and generate insights from raw business data.
I identify valuable use cases by asking one simple question: does it improve accuracy, speed, or decision quality? If it doesn't move the needle on one of those three, it's not worth implementing. I also make sure every AI use case can be scaled across multiple clients or internal processes before we invest heavily. That's how we've built a lean but tech-forward infrastructure that actually saves time and increases profitability without chasing every new AI trend.
Start With Pain Points Not Flashy Tech
When we first started exploring AI and machine learning, the temptation was to look for flashy use cases — automation, prediction, personalization — the buzzwords that sound exciting in strategy decks. But I quickly realized the key wasn't what AI could do, it was where it could create real leverage. So we flipped the process. Instead of starting with the technology, we started with pain points — recurring inefficiencies, decision bottlenecks, and data we were collecting but not actually using.
We ran a discovery sprint across departments to map these opportunities and scored them by two factors: potential impact and data readiness. That exercise revealed an obvious truth that often gets missed — not every problem is an AI problem. Many were solvable with better process or analytics, but a few stood out as perfect fits for machine learning because they relied on pattern recognition at scale.
Our first real implementation was predictive demand modeling. By combining historical sales data with external signals like seasonality and behavior trends, we built a model that could forecast demand with far greater accuracy. The result was a 25% reduction in overstock and a major improvement in operational efficiency.
What made this successful wasn't the algorithm itself — it was cross-functional ownership. We had engineers, analysts, and business leaders working together to define the problem before touching the model. That alignment kept us from falling into the "tech-first" trap that derails many AI initiatives.
The biggest lesson? Treat AI like a scalpel, not a hammer. It's powerful, but it only creates value when it's aimed precisely at the right friction points. Once you focus on outcomes instead of hype, AI stops being an experiment — it becomes a competitive advantage.
Target Operational Bottlenecks With Measurable Results
Our approach to incorporating AI into our technology strategy began by identifying operational bottlenecks where manual processes were consuming significant time and resources. We carefully evaluated areas like lead qualification and client onboarding, where we could measure concrete metrics before and after AI implementation. This focused methodology allowed us to prioritize use cases with the highest potential ROI, such as our lead qualification workflow that reduced manual review time from hours to minutes. The success of these initial implementations provided both immediate business value and a framework for evaluating future AI integration opportunities across our organization.
Focus Where Humans Spend Time Unnecessarily
We began incorporating AI by asking, "Where are we spending the most human hours with the least strategic value?" This led us to focus on ticket triage in our service desk. Using historical data, we trained a machine learning model to automatically prioritize and route support tickets by content, client history, and urgency. While not perfect, this solution reduced manual sorting time each week and improved response times for critical issues.
We identified this use case by analyzing support metrics and interviewing frontline staff about repetitive tasks. The key lesson was to avoid chasing trends. Rather than asking, "Where can we use AI?" we focused on, "Where are we wasting skilled time?" This approach helped us select projects where automation enhanced, rather than complicated, our work.
Solve Real Driver Pain Points First
Our approach to AI began with identifying inefficiencies that had the greatest impact on drivers, mainly, the lack of real-time insight into available parking. Rather than integrating technology for its own sake, we focused on solving that pain point with measurable results.
We analyzed thousands of data points from driver usage patterns, weather conditions, and route congestion to train models that predict demand with greater accuracy. AI now helps optimize space utilization by anticipating where and when parking shortages will occur, allowing us to respond proactively.
The process started small, with pilot models tested in specific regions, and gradually scaled as performance proved reliable. Through this, we found that AI delivers the most value when it directly improves uptime and convenience for drivers.
By combining operational data with machine learning, we've built a more predictive system—one that helps truckers plan and reduces unnecessary downtime. That's where technology has the biggest real-world impact.
Create Micro Tools For Immediate Impact
When we started exploring AI and machine learning for our organization, we deliberately avoided the expectation of searching for massive, all-in-one solutions that would require enormous time investments.
Instead, we focused on finding small, specific pain points where automation could make an immediate impact.
Our approach centered on creating what I call "micro tools" - lean AI agents designed to handle individual tasks rather than overhauling entire workflows.
We signed up with a product called Relevance AI because their platform allowed us to build these focused agents quickly without extensive development overhead. Each agent tackles one specific job, whether it's data processing, content generation, or routine analysis.
The key to identifying valuable use cases was looking at repetitive tasks that consumed disproportionate amounts of our team's time. We mapped out daily workflows and calculated time spent on manual processes.
Instead of focusing on complete employee replacement, my mindset was and still is how AI can help us "do more with what we have now".
What makes this strategy effective is the compound effect. While each micro tool might only save a handful of minutes here or an hour there, those savings accumulate significantly across the month and even annually.
We're now seeing substantial efficiency gains without the risk and disruption of a major learning curve.
This incremental approach also gave us flexibility to test, learn, and adjust quickly. If an agent doesn't deliver value, we can move on without major consequences.
It's proven to be a sustainable way to integrate AI into our operations and improve slowly but surely.
Run Interactive Workshops To Identify Champions
AI tooling can be polarising - some people love it, others are quite resistant. To spark deeper curiosity, we ran interactive, in-person workshops splitting the team into groups who each tried using (competing) tools to achieve a simple task, then demo their work to the wider group. We then identified 'AI champions' within the team - enthusiastic early adopters who pilot different tools, share tips and more generally advocate for AI adoption across the wider business.
Address Business Problems Before Technology Selection
A practical example of how I incorporated AI and machine learning into our technology strategy came during a large-scale billing and claims modernization initiative for a U.S.-based insurer. The business struggled with high manual intervention in billing exceptions and slow claims turnaround—pain points that directly affected financial accuracy and customer satisfaction.
Rather than beginning with technology, we began with workshops to list out problems faced in finance, claims, and operations. This led us to focus on two key user cases - using AI to spot billing anomalies and applying machine learning to sort claims.
For billing, we trained ML models on three years of transaction data to flag outliers and predict exceptions before reconciliation. In claims, a generative AI model was fine-tuned on historical adjuster notes to summarize narratives and recommend routing categories.
The results were substantial - billing exception volume dropped by 40%, and claim assessment time reduced by over 50%. What made this approach work was embedding AI into existing operational workflows, not layering it as a separate tool. The key shift in thinking was moving from "AI as a pilot" to AI as an enabler of continuous business optimization.
Build Value Through Pragmatic Iterative Process
At Supreme Lending, our approach to evaluating and incorporating AI and ML into our technology strategy has been rooted in a pragmatic, iterative process that prioritises business alignment over hype. We began by conducting a comprehensive audit of our operations, mapping out pain points in the mortgage finance lifecycle( like manual data entry, compliance bottlenecks, and customer onboarding delays) while benchmarking against industry standards and emerging AI capabilities. This led us to build an in-house AI team from the ground up, starting small with proof-of-concept pilots, then scaling to secure, private Agentic-AI applications and specialised LLMs tailored for mortgage-specific tasks like risk assessment and document analysis.
To identify the most valuable use cases, we adopted a value-driven framework that combined quantitative metrics with qualitative insights from cross-functional stakeholders. We prioritised opportunities based on potential ROI, focusing on areas where AI could deliver measurable gains. By engaging directly with our end-users i.e. Loan Officers through workshops and feedback loops, we uncovered high-impact applications that not only streamlined operations but also empowered us to shift from repetitive tasks to strategic, human-centered roles. Our Agentic-AI applications are already serving 800 users across our organization.
Muhammad Waqar
Artificial Intelligence Architect
muhammad.waqar@supremelending.com
