How to find the AI projects that deliver the most value for your business

How to find the AI projects that deliver the most value for your business - Identifying High-Impact Use Cases Across Core Business Functions

Look, everyone's talking about Generative AI right now—it’s ignited this huge wave of investment, but the chatter often misses where the real money is made. Honestly, we need to stop thinking about simple transactional efficiency; the focus is rapidly shifting toward Agentic AI systems that can actually autonomously plan and execute complex, multi-step business tasks. And here’s a surprise: while customer service automation was the easy early win, the highest projected financial returns are now showing up in internal functions you might not expect, like R&D and Software Engineering. Think about it: these internal AI systems are accelerating creation cycles, sometimes reducing your time-to-market by a crucial 30%, which is a massive competitive edge. But the model itself? It's often less critical than the underlying data maturity of the department; that’s the silent success factor. If a function has a data maturity score above 3.5, those projects realize value 40% faster—period. For core functions like Legal or Procurement, the biggest magnitude of impact comes from projects designed purely to process and synthesize unstructured data. We forget that 80% of our critical company knowledge is just trapped in contracts, documents, and internal chat threads. Now, a big pitfall we're seeing is that 65% of promising pilots fail to scale enterprise-wide because they only deliver high *local* optimization. So, identifying true high-value use cases means moving past simple efficiency—don't just measure cost reduction. You should be tracking things like "reduction in process variability" or the "decrease in cognitive load per employee," because those reliably predict if people will actually use the tool long-term. Maybe it’s just me, but the projects yielding the highest immediate return often take less than twelve weeks to develop and are narrowly scoped to fix one single, deeply frustrating operational bottleneck.

How to find the AI projects that deliver the most value for your business - Prioritizing Projects Based on Measurable Productivity and ROI

Honestly, we've reached a point where just "doing AI" isn't enough anymore because the hype from a few years back has finally met the reality of the balance sheet. You've probably seen the big numbers thrown around, but the truth is that average returns for most companies are still stuck in that modest 5% to 7% range. I think the problem is that we're still chasing flashy demos instead of looking at the cold, hard math that keeps the money moving. Right now, the people holding the purse strings want to see a clear bump in Net Present Value within 18 months, rejecting the old way of just looking at internal rates of return. It’s basically a survival filter for your budget. If you look at those hitting returns over 15

How to find the AI projects that deliver the most value for your business - Aligning AI Integration with Existing People and Operational Processes

I’ve spent a lot of time looking at why some AI rollouts just click while others feel like a bad organ transplant, and honestly, it usually comes down to whether you're respecting the way people actually get work done. By now, we've largely moved past the idea of replacing jobs and started breaking things down into specific tasks, which makes the handoff between a human and an AI agent feel a lot more natural. But here’s the kicker: if you’re just asking people how they do their jobs, you’re missing the shadow processes—those little workarounds everyone uses that never show up in the official manual. Using automated process mining to catch those hidden steps is why some teams are seeing their win rates more than double what they were a year ago. Think about it

How to find the AI projects that deliver the most value for your business - Moving Beyond Experimental Pilots to Achieve Enterprise-Wide Scaling

You know that moment when the pilot project looks absolutely brilliant, everyone claps, and then it just fizzles out when you try to roll it out company-wide? Look, the biggest shocker we’re seeing is that the budget shifts dramatically; you might spend a fortune training the model initially, but once you scale, the inference-time compute costs actually account for a staggering 75% of your total AI cost of ownership. That kind of financial gravity means your governance model can’t be an afterthought—honestly, organizations adopting a hub-and-spoke structure are seeing a 2.5 times higher success rate in getting projects from the lab to actual production. We also have to face the cold reality that those advanced, multi-step agentic workflows introduce a decision latency of 15 to 45 seconds, which is a lifetime in a standard synchronous application. Think about it: that delay completely forces you to ditch synchronous designs and fundamentally shift to asynchronous user interfaces if you want people to keep using the tool. And here’s a critical oversight: for every dollar top firms put into the AI software itself, they're now allocating three dollars toward human-in-the-loop training and continuous process redesign. Maybe it’s just me, but that heavy investment is necessary because automated monitoring still shows nearly 40% of employees are using unsanctioned third-party AI agents for specialized tasks, creating a massive security and scaling gap you weren't even planning for. Beyond the people issues, scaling requires maniacal focus on data quality; those "active data pipelines" that refresh the model context windows every 24 hours or less are non-negotiable. Why? Because refreshing that data context reliably reduces those annoying hallucination-driven operational errors by 50% in high-stakes environments. We can’t ignore the environmental footprint either; scaling generative models globally has been documented to increase a corporation's Scope 2 carbon emissions by about 12%, making green-compute scheduling a standard operational requirement now, not a nice-to-have. You simply can't treat scaling as just "more compute"; it requires redesigning the infrastructure, the UI, and the human workflow simultaneously. It’s a lot more complicated than just hitting a bigger button.