Ep 1: Why We Started an AI Enablement Company (And Why Most AI Products Fail)

It’s Tuesday morning. Your calendar is already red. Now you are in a live demo, a business productivity agent moves faster than any human in the room: it triages an overflowing inbox, drafts client responses in seconds, updates the CRM, and summarizes last week’s meetings with crisp bullet points. No hesitation. No fatigue. Someone jokes that it just did an entire day’s work before the coffee finished brewing.

By the end of the meeting, the decision feels obvious. Funding is approved. A pilot launches. Internally, the story becomes: this is how we get time back.

Then the rollout begins.

Real inboxes don’t look like the demo. Threads loop across teams, half the context lives in people’s heads, and one poorly phrased auto-reply quietly damages a client relationship. The agent updates the CRM perfectly, just not the way sales actually uses it. Managers spend more time reviewing the AI’s work than they ever spent doing the work themselves.

A year later, the numbers tell a colder story. MIT’s The GenAI Divide: State of AI in Business 2025 reports that 95% of generative AI pilots fail to produce measurable profit impact. RAND finds that more than 80% of AI projects fall short, this is twice the failure rate of non-AI initiatives. S&P Global notes that 42% of companies abandoned most of their AI efforts in 2025, up from just 17% the year before.

Intelligence isn’t the issue, It’s a failure to properly fit the solution to the markets needs.

We’ve seen this movie before. Enterprise systems built and tested for "happy path" workflows tend to collapse when they meet real operating conditions like exceptions, edge cases, and human judgment under pressure. National Grid learned this the hard way in 2012, when years of “happy path” SAP testing unraveled during Hurricane Sandy, ultimately leading to a $585 million recovery effort. The software worked, until reality showed up.

These overall are just symptoms of a deeper problem in the approach to AI by industry. And this is exactly why we founded Oliego: to focus on AI enablement rather than chasing the illusion of full automation. In this post, we'll break down the core issues we've observed in the trenches and explain our overall thesis on why enablement is the path to real, sustainable AI outcomes.

The Gap Between Demos <--> Deployment

AI demos are engineered for the “happy path”, the most ideal and straightforward journey a user takes through software to achieve a goal, assuming everything works perfectly. They run on curated data, in controlled environments, with "happy path" inputs that showcase the model's best behavior. It's like watching a highlight reel. It’s impressive, but doesn’t tell the full story of the game.

In production, reality hits a bit different:

• Messy, real-world data: Legacy systems, inconsistent formats, and biased or incomplete datasets cause models to falter.

• Workflow misalignment: Tools that work in isolation don't integrate into daily operations. MIT's report highlights a huge “learning gap". AI systems often fail to adapt, retain feedback, or evolve with business needs.

• Scalability challenges: What works for dozens of users breaks under enterprise load, with issues like latency, cost spikes, and edge cases multiplying.

• Human factors: Users abandon tools that introduce uncertainty or require constant oversight.

The result? Pilots stall. Proofs-of-concept become "science projects." We've seen companies pour millions into flashy prototypes only to quietly shelve them when they can't bridge this gap. Deployment isn't just about the technical aspects; it's about organization, requiring governance, monitoring, and continuous iteration which most teams underestimate.

Why “AI Tools” ≠ “AI Outcomes”

Buying or building an AI tool feels like progress. ChatGPT wrappers, copilots, and off-the-shelf agents are easy to deploy and generate quick wins, like faster email drafting or basic summarization.

But tools alone rarely move the needle on business metrics. Here is why:

• Siloed impact: Generic tools boost individual productivity (e.g., shadow AI usage is rampant, with 90%+ of workers using personal tools), but they don't transform workflows or drive P&L impact.

• No accountability to outcomes: Most tools react to prompts without linking to KPIs. They generate outputs, but there's no closed loop for measurement, feedback, or improvement.

• Lack of integration and adaptation: As one CIO quoted in the report said, "We've seen dozens of demos this year. Maybe one or two are genuinely useful. The rest are wrappers or science projects." Tools don't learn from your specific context, leading to brittle performance over time.

Outcomes require more than just a tool. These new innovations demand redesigned processes, domain-specific customization, and measurable ROI. Sales and marketing pilots dominate budgets (50-70%), but back-office or workflow-embedded AI delivers clearer returns. Tools get you started but outcomes require enablement.

Our Core Thesis: Enablement vs. Automation

This is where our company's core thesis comes in: AI enablement over blind automation.

• Automation promises to replace humans entirely with fully autonomous agents handling end-to-end processes. It's seductive in demos but fragile in practice. Full automation assumes perfect reliability, which current AI (especially generative models) can't deliver consistently. Hallucinations, biases, and unpredictability make it risky for high-stakes tasks. When it fails (and it often does), trust erodes, and projects get scrapped.

• Enablement, on the other hand, focuses on augmenting humans: providing tools, infrastructure, and guardrails that make people 10x more effective. It's about embedding AI into workflows with human-in-the-loop oversight, continuous learning, and domain expertise. Enablement bridges the demo-to-deployment gap by prioritizing integration, observability, and adaptability.

What about the 5% represented successfully in the data? The truth is they pick one pain point, execute deeply, redesign workflows, and often partner with specialized providers for embedded, context-aware solutions. That's enablement in action, delivering clear, meaningful, and measurable productivity gains of 26-55% and real ROI *($3.70 per dollar invested in mature cases).

We're building our company around this: platforms and services that enable organizations to deploy reliable AI at scale, without overpromising autonomy. We help with data readiness, workflow integration, governance, and ongoing evolution. We turn AI from a hype cycle into a competitive advantage.

Looking Ahead

The AI hype train is slowing, but the opportunities are just beginning for those who get deployment right. In future posts, we'll dive deeper into real-world case studies, common pitfalls we've helped clients avoid, and practical frameworks for AI enablement.

If you're wrestling with stalled pilots or wondering how to get real value from AI, drop us a line. We're in the trenches together.

What do you think? Is your organization chasing automation or building enablement? Share in the comments.

Stay tuned for the next note
Live from the trenches.

-Emmanuel