Enterprise AI has long questioned whether the models are good enough, fast enough, and reliable enough. As it stands, the answer is a resounding, Yes. A harder question remains – are businesses ready for AI?
The model is no longer the biggest bottleneck. The real challenge is making a raw model reliable inside a functioning business.
That’s the goal of last mile engineering. It’s the discipline of understanding the true challenges that arise at scale. It’s where AI connects to legacy systems, handles messy data, stays within cost targets, and operates inside governance and compliance boundaries.
The Gap Between Promise and Production
Our newest 2026 CIO and CTO study reports that 80% of enterprise tech leaders attribute AI project failures to a lack of visibility or oversight, not to the technology itself. More than half say it’s somewhat likely they will shut down a pilot for poor performance this year. More than four in five say their board is questioning AI spending, yet 71% plan to somewhat increase AI initiative investments in 2026.
That combination says a lot about where the market is: companies still believe in the upside, but they are becoming far less patient with weak execution.
In fact, only 31% of enterprises have AI fully embedded in core decision-making. AI is being tested everywhere, but it’s still not deeply operationalized in most organizations. PwC’s 2026 Global CEO Survey reinforces the point: only 12% of CEOs say AI has delivered both cost and revenue benefits, while companies with strong AI foundations and enterprise-wide integration environments are 3x more likely to see meaningful returns.
That is why the story is no longer “AI is failing because the models are not ready.” The story is that most teams can produce incredibly impressive, immersive and functional POCs, MVPs, and working products, but making the leap to true organizational capability requires a major perspective shift. Prompt engineering or spinning up a simple agent will no longer cut it. Almost anyone can build a demo that appears intelligent. The hard part is building systems that take action reliably enough to support real commercial decisions.
Customer service is a prime example of how this can be done well. This is no longer about chatbots inside a single interaction. It’s about agents that can observe context across systems, plan a course of action, and execute resolution end to end. That’s why the real opportunity is not just faster answers or lower support costs. It’s a fundamentally different service model that was previously impossible; one that moves from reactive support to pre-empting issues, self-healing problems before customers notice them, and delivering the kind of continuity and personal attention that used to be reserved for concierge businesses.
Most companies are experimenting, but few are winning, not because GenAI is weak, but because integration and orchestration are poor, strategy and ownership are not business-led, and broken processes are simply automated instead of redesigned. In customer service, enterprise AI succeeds when it’s built as a transformation of systems, workflows, and operating models, not as a clever layer on top of them.
The Ownership Gap
Another thing to consider is that AI success requires clear ownership from prototype to production, and this is where many enterprises still break down. In most AI initiatives, responsibility is distributed across teams, but accountability for production performance is not. One team owns the model, another the infrastructure, another the integration, another the policy review, yet no one fully owns whether the system actually works at scale inside the business.
ISACA’s March research captures the symptom: 20% of respondents do not know who would be ultimately accountable if an AI system caused harm, and 59% do not know how quickly their organization could halt an AI system during a security incident. When accountability is this unclear, the last mile is improvised instead of engineered.
Our own research shows failures are tied mainly to poor visibility, weak coordination, and poor management rather than to the technology itself. Those are exactly the kinds of breakdowns that emerge when experimentation spreads faster than end-to-end ownership.
The Cost of Speed and the Challenge of Scale
A recent EY survey found that 85% of technology leaders prioritize speed-to-market over thorough AI vetting.
Too often, organizations chase quick wins that look impressive early but are too small to justify the real cost of deployment. The market may not just have a speed problem, but an ambition problem. Companies are often not thinking big enough about where AI can create meaningful commercial value. Enterprise AI requires substantial investment. To do it properly, the goal is to pursue bigger, higher-value opportunities and engineer them properly from day one.
Systems that move too fast will soon find that the real problems are magnified at scale. A system that feels reliable for 100 users can become unusably sluggish for 1 million, even if nothing appears obviously broken. In many cases, the issue is not that the AI failed to work, it’s that the underlying architecture was never designed to support enterprise scale. That is why last-mile execution requires expert AI engineering that can anticipate these problems before they become expensive redesigns.
Deloitte made the same point in March, arguing that ERP still serves as the system of record for trusted data and auditability, and that AI value depends on modular, API-driven modernization rather than layering intelligence onto rigid legacy systems.
None of this means model fallibility should be ignored. There are still real technical limitations, and enterprise leaders should acknowledge them. Reuters reported in April that new research suggests hallucinations may be more deeply rooted in large language models than many advocates assume, especially as input complexity rises.
But that reality strengthens the case for this holistic approach. When a technology is imperfect, the surrounding system has to be stronger.
The New Questions to Ask
It should be clear by now, the question is no longer whether AI is capable.
The question is whether enterprises are prepared to trust it with decisions that actually matter.
The companies that truly transform with AI will be reshaping their organization around these questions:
- Does our AI have the context, access, and guardrails to make meaningful decisions and act safely?
- Are we prepared to redesign workflows, roles, and decision-making around AI, or are we just layering it onto the business?
- How can AI-centered decision-making create frictionless customer experiences?
- Which AI use cases are valuable enough to justify the cost and complexity of deployment?
In the end, the winners will not be defined by how quickly they adopt AI, but by how seriously they commit to the organizational and engineering changes required to make it transformative.
