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Nearly every company is investing in AI. Leadership teams are plotting out their AI transformation and creating presentations for their boards to review and approve. The automation mandates then roll downhill, and the motivated teams dive straight into setting up pilots and deploying agents. And it’s all happening in the name of growth, productivity, and efficiency.
It’s also not working.
If you feel like your org is still missing out, or not getting the benefits everyone else seems to be talking about, you're not alone. AI just isn't working for orgs at scale (yet). And it's because it hasn't been set up for success.
That’s not a knock on your team. It’s not a lack of effort, or a lack of smart people, or a sign you chose the “wrong” model. It’s a structural issue: most organizations aren’t able to give the AI the workflow context it needs to understand how work actually gets done across teams and tools. And if AI can’t understand the work, it certainly can’t improve it.
This issue is showing up everywhere. Research from MIT found that 95% of AI pilots fail to deliver ROI. The small fraction that do succeed share a common trait: they’re deeply integrated into real workflows, not bolted on top of them. This current trend informs Gartner’s prediction that more than 40% of agentic AI projects will be canceled by 2027 due to unclear business outcomes.
AI runs on models. Business runs on context.
And most AI strategies stall for the same reason: they’re missing the context that agents and teams need to work better.
AI only works when it understands the work
Most organizations are pushing automation forward before they’ve answered three fundamental questions:
- What work actually needs to be automated or improved?
- How is that work actually done (not just the happy path)?
- How much value would automation create if it worked?
With all the AI pressure on leaders, it’s easy to see how slick demos that promise AI transformation at the click of a button (or two) are getting their attention. But the reality is that thousands of tasks, decisions, handoffs, and exceptions are happening across the organization every day. No single system sees all of it. No single team understands all of it. And this means that no AI model can improve it.
Most companies are doing this: giving AI a partial picture and expecting a fully informed strategic partner.
This results in the familiar failure mode: an agent that performs impressively in a controlled demo, then stalls in production because it runs into the messy parts of real work — the approvals, the dependencies, the edge cases, the consequences.
Without this context, AI can only automate fragments. That’s why pilots stall, agents spin, and ROI stays just out of reach.
The real breakthroughs happen when AI is grounded in how work actually happens. Not how people describe it in process surveys, not how it’s measured in one-off time studies, and not how it’s summarized in a slide deck.
If you’re still guessing which automations will deliver ROI, your strategy isn’t intentional. It’s a collection of experiments that never compound, and it’ll leave you burning through time, money, and your credibility. It can also leave you with a more complex, messier outcome than you started with.
How to close the context gap
Here’s the good news: you’re not alone, and this is fixable. It’s still the early days of AI transformations. A McKinsey study found that only 1% of companies believe their AI investments have reached maturity and are delivering measurable business impact.
Most leaders can sense the gap. The future of AI feels promising but still a bit too fuzzy. They see AI pilots that never make it to production. They approved automation initiatives that sounded transformative in the boardroom, but are now stalled out without delivering real value. They’re experiencing momentum without scale, activity without outcomes.
To gain focus, organizations need to address the context gap and they need to do it soon. The people and organizations making real progress aren’t just moving fast. They’re moving deliberately along a Context Maturity Curve. They understand where and how their AI initiatives can move forward confidently to show measurable impact, and where they still need to fill in the context gap.
Now is the time to build your advantage
This next phase of AI is already picking its winners.
They won’t be the companies that ran the most pilots or tested the most tools. They won’t be the ones who made AI work for just one or two teams.
They’ll be the companies that figure out — right now — how to make AI work systematically for the entire business. The ones who build in operational reality. The ones who close the context gap. The ones who understand the Context Maturity Curve and plot their course to the top.
See exactly where your organization lands. Understand what’s holding your AI strategy back, and what it takes to move forward.
