# In the agentic era, clarity beats cleverness > Source: > Published: 2026-09-09 10:00:00+00:00 Every technology wave I’ve lived through has arrived with the same promise and failed in the same way. I spent years as CIO and chief digital officer for Procter & Gamble across Asia, the Middle East and Africa — dozens of markets, wildly different levels of digital maturity, one set of global platforms. I now lead enterprise AI strategy and transformation at Vodafone Idea, an operator serving one of the largest and most price-sensitive subscriber bases on earth. Different industries. Different decades. Identical lesson: technology travels effortlessly across an enterprise. Operating models don’t. That lesson has never mattered more than it does right now, because something genuinely new has happened. For most of the past decade, enterprise AI predicted and suggested. A model scored a customer; a person decided what to do. Agentic systems break that arrangement. They evaluate context, reason across business rules, coordinate across tools and complete work end to end. The model didn’t just get better. The software acquired agency. And the moment software can act, the hardest questions stop being technical. The headline statistics on AI right now look contradictory until you read them together. Adoption is effectively universal. McKinsey’s [State of AI](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai) research found 88% of organizations using AI in at least one business function. Yet only 39% report any EBIT impact at the enterprise level, and roughly 6% qualify as high performers attributing more than 5% of EBIT to AI. A widely circulated — and vigorously debated — report from MIT’s Project NANDA found that [95% of enterprise generative AI pilots produced no measurable P&L effect](https://thehill.com/policy/technology/5460663-generative-ai-zero-returns-businesses-mit-report/). Critics fairly point out the narrow six-month ROI definition. The direction still matches what most of us see in our own portfolios. And on agents specifically, Gartner predicts that [more than 40% of agentic AI projects will be canceled by the end of 2027](https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027) — attributing the failures to escalating costs, unclear business value and inadequate risk controls. Read that list again. Cost. Value. Controls. Not one of them is a model problem. The most useful finding in all of this data is McKinsey’s observation about what separates the high performers: they are around three times more likely to have fundamentally redesigned workflows end to end, something only about a fifth of organizations have actually done. That is the whole game. The winners aren’t running better models. They’re running clearer businesses. At P&G, the single most valuable thing we did before any large deployment was reduce variance. Twelve markets doing the same process eleven different ways is a technology project that will fail before it starts. Every local exception you tolerate becomes a customization, then an integration, then a reason the rollout stalls in market seven. Agentic AI amplifies this by an order of magnitude, because an agent doesn’t escalate a messy exception politely — it acts on it. We use a ladder to force the conversation: eliminate, simplify, standardize, assist, automate, agentify. Most organizations leap straight to the last rung. A process is chaotic, so the instinct is to point intelligence at the chaos and hope. Every rung you skip returns as a runtime exception, and runtime exceptions are where autonomous systems make their most expensive mistakes. But the opposite failure is just as costly and far less discussed. Waiting for clean data and perfect processes is how enterprises spend two years preparing to begin. So, we set a practical bar. A process is ready when the team can articulate three things: what triggers it, where the decision points are and how it fails. If they can’t write those down, no model will compensate. If they can, we move — imperfections and all. That test has saved us more time than any architecture decision we’ve made. Here is where I’d concentrate the attention of any leadership team entering this era. We classify every step in a redesigned process by execution mode — fully automated, AI-executed with human review, joint, human-led, or permanently human-only — each with thresholds and an audit trail. It sounds like governance paperwork. In practice it’s the most clarifying exercise we run, because it forces a decision that technology conversations conveniently defer. And the most valuable output isn’t the list of what we automated. It’s the list of what we marked human-only, permanently. Commercial negotiation and vendor selection. Decisions with direct people impact. Financial postings and payment approvals. Not because a system couldn’t eventually perform them — because accountability shouldn’t move just because capability did. Once those boundaries are explicit, everything else accelerates. Teams stop hedging. Autonomy isn’t the absence of a boundary; it’s speed inside one that somebody owns. My industry is discovering this the hard way. TM Forum research with IBM’s Institute for Business Value found that while [72% of operators expressed confidence in the trustworthiness of their AI, only 14% could produce externally reviewable evidence of it](https://www.telecomstechnews.com/news/tm-forum-telecom-operators-unprepared-ai-safety-regulations/). With EU AI Act obligations for high-risk systems arriving, that gap between confidence and evidence is about to become a very concrete problem — and not only in telecom. Frontier model capability is converging and increasingly available to everyone, including your competitors, on the same commercial terms. What is not available to them is your enterprise’s context. Early in our program I noticed a pattern that I suspect is near-universal: every use case was quietly rebuilding its own understanding of the business. What a customer is. What a site is. How a vendor relates to a contract, a contract to an invoice, an invoice to a dispute. Six teams, six versions of the truth, no two agents agreeing. So, we invested in a shared context layer — a knowledge graph of the enterprise’s entities and relationships, bound to a common process ontology and a single register of agents. Agents read the organization’s context at run time instead of relearning it use case by use case, and every action carries lineage, which means every action can be audited. It is considerably less exciting than model selection. It is also what determines whether your tenth agent takes ten weeks or ten days. AI programs lose credibility in a predictable sequence. Leaders report agents deployed, licenses provisioned, use cases launched. All activity. None of it answers whether the business is measurably better off — which is precisely the gap the 39%-versus-6% split in McKinsey’s data describes. We hold one discipline hard: no value is booked without a baseline, and no baseline counts until Finance has validated it. Cycle time, cost to serve, containment, leakage recovered, dispute resolution time — each measured against a number that existed before we started and agreed by the people who own the P&L. It’s slower. It also means that when we claim value, nobody in the organization argues, and that credibility is what buys permission for the next wave. Telecom is worth watching regardless of the sector you lead, because it is running this experiment at extreme scale and under real-time constraints. Nearly nine in ten operators are [increasing AI budgets this year, up from 65% a year earlier](https://www.computerweekly.com/news/366639271/Artificial-intelligence-drives-autonomous-networks-customer-service-gains), and autonomous networks have overtaken customer experience as the top-ROI use case. A [Bain and TM Forum survey](https://www.bain.com/insights/accelerating-autonomous-networks-a-reality-check-for-telcos/) found around 20% of operators reaching advanced autonomy in selected domains, with technical debt, talent gaps, organizational silos and cultural resistance — not algorithms — named as the barriers to scale. The pattern generalizes. Wherever a sector has pushed autonomy furthest, the constraint has turned out to be organizational. One figure from McKinsey’s [State of Organizations 2026](https://www.mckinsey.com/~/media/mckinsey/business%20functions/people%20and%20organizational%20performance/our%20insights/the%20state%20of%20organizations/2026/the-state-of-organizations-2026.pdf) research has stayed with me: an executive’s estimate that for every dollar spent on the technology, five should be spent on people. That ratio would horrify most AI business cases I’ve reviewed, including some of my own early ones. But it matches my experience across both industries I’ve worked in. In consumer goods, the markets that adopted fastest weren’t the ones with the best infrastructure — they were the ones whose leaders were personally fluent in what the system did. The same holds now. You cannot govern what you have never operated, and a leadership team where nobody has built anything will hesitate at every decision that matters. I don’t believe the agentic era will be won by the organizations with the best models. Those are becoming a commodity. It will be won by organizations that can say clearly what they want done, name who is accountable when it’s done badly, define the number that moves when it’s done well — and then move fast inside those boundaries. That isn’t a technology problem. It’s a leadership one, and it’s the most interesting work available to any executive right now. Here’s the question I’d put to your next leadership meeting. Not which model to adopt. Instead: could your organization name, today, the person who owns the outcome of an agent you deploy tomorrow? If that answer takes longer than a moment, you’ve just found where the work begins.