Forget the gym membership. Here’s the 2026 resolution that will actually transform your organization.
Every January, leadership teams gather to set priorities for the year ahead. They review budgets, realign strategies, and make bold promises about what they’ll accomplish. But if your organization launched AI initiatives in 2024 or 2025, there’s one resolution that matters more than all the others: this year, you’re going to measure what your AI is actually doing.
It sounds simple. It isn’t. According to Gartner, at least 30% of generative AI projects were abandoned after proof of concept by the end of 2025—not because they failed, but because teams couldn’t demonstrate clear business value. The AI worked. The measurement didn’t.
The Pilot Purgatory Problem
If you’ve been in enterprise technology for any length of time, you’ve seen this movie before. A promising technology emerges. Teams rush to experiment. Pilots launch across departments. And then… nothing. The pilots keep running, but they never scale. They become permanent experiments, consuming budget and attention without ever delivering the transformation they promised.
AI has accelerated this pattern dramatically. The barrier to launching an AI pilot is lower than ever—a team can spin up a chatbot or copilot integration in days. But the barrier to proving that pilot’s value remains stubbornly high. When the CFO asks “What’s the ROI on our AI investment?”, most teams can only offer anecdotes and assumptions.
This is pilot purgatory, and it’s where AI initiatives go to languish. A recent industry analysis found that on average, only 48% of AI projects make it into production, and it takes 8 months to go from prototype to production. The problem isn’t the technology. It’s the inability to answer the fundamental question: is this working?
Why 2026 Is Different
The pressure to prove AI value has never been higher. After two years of experimentation, boards and executive teams are demanding results. They’ve seen the hype. They’ve approved the budgets. Now they want to know what they got for their investment.
Meanwhile, AI capabilities are advancing rapidly. Agentic AI—systems that can autonomously plan and execute complex tasks—is moving from research labs to production environments. Organizations that can’t measure the value of their current AI deployments will struggle to make informed decisions about these more sophisticated (and more expensive) capabilities.
The teams that figure out measurement in 2026 will scale their AI programs. The teams that don’t will watch their pilots slowly fade away, replaced by the next wave of experiments that also never prove their worth.
Five Measurement Commitments for 2026
Making “measure AI” a meaningful resolution requires specific commitments. Here’s what the teams that escape pilot purgatory actually do differently.
First, they track outcomes, not just usage. Knowing that 500 employees used your AI assistant last month tells you almost nothing. Knowing that those employees resolved customer issues 23% faster, or processed invoices with 15% fewer errors—that’s actionable intelligence. The shift from counting interactions to measuring business impact is the single most important change most organizations need to make.
Second, they tie AI to existing business KPIs. Your organization already measures what matters: revenue, costs, customer satisfaction, employee productivity, error rates, cycle times. Effective AI measurement connects AI usage to these existing metrics rather than creating a parallel universe of AI-specific vanity metrics. When you can show that teams using AI tools have 18% higher customer satisfaction scores, you’ve made the business case.
Third, they monitor costs proactively. AI costs can spiral quickly—API calls, compute resources, vendor subscriptions, integration maintenance. Teams that measure well know their cost per outcome, not just their total spend. They can answer questions like “How much does it cost us to resolve a customer issue with AI assistance versus without?” This kind of granular cost visibility is essential for making scaling decisions.
Fourth, they document what’s working and what isn’t. The value of AI measurement isn’t just in proving ROI—it’s in learning. Which use cases deliver the highest value? Which teams have figured out how to get the most from AI tools? Which integrations consistently underperform? Organizations that systematically capture these insights can make smarter decisions about where to invest next.
Fifth, they build the case for scaling incrementally. The path from pilot to production isn’t a single leap—it’s a series of gates, each requiring evidence that the AI is delivering value. Teams that measure well can show steady improvement over time, building confidence with stakeholders and earning the resources needed to expand.
How to Actually Keep This Resolution
Unlike most New Year’s resolutions, measuring AI doesn’t require willpower—it requires infrastructure. You need systems that capture AI usage data, connect it to business outcomes, and present it in ways that executives and finance teams can act on.
This is where many organizations stumble. They try to build measurement capabilities from scratch, cobbling together logging tools, custom dashboards, and manual reporting processes. The result is fragile, incomplete, and almost never maintained once the initial enthusiasm fades.
The more sustainable approach is to implement purpose-built AI intelligence platforms that handle measurement automatically. These platforms integrate with your existing AI tools—chatbots, copilots, agent frameworks, AI-enabled SaaS—and provide unified visibility into usage, outcomes, and costs across all of them. Olakai, for example, was built specifically to solve this problem: giving enterprises the data they need to prove AI value and make confident scaling decisions.
The Payoff
Teams that measure scale. Teams that don’t stay stuck in pilot purgatory indefinitely. It’s that simple.
When you can show the CFO exactly how much value your AI initiatives are delivering—in terms they understand, tied to metrics they already care about—you transform the conversation. You move from defending your AI budget to advocating for expansion. You shift from “we think this is working” to “here’s the data proving it works.”
More importantly, you give your organization the information it needs to make smart decisions about AI. Not every pilot should scale. Not every use case delivers value. Measurement lets you distinguish the winners from the losers and concentrate resources where they’ll have the greatest impact.
2026 will be the year that separates the organizations that figured out AI from the ones still experimenting. The difference won’t be which AI tools they chose or how sophisticated their implementations were. It will be whether they could prove their AI was working—and use that proof to build something lasting.
That’s a resolution worth keeping.
Ready to start 2026 with visibility into your AI investments? Schedule a demo to see how Olakai measures AI ROI across your entire organization.