Few conversations are as persistent in executive circles as this one: “We invested in AI — so where is the return?” The question, once reserved for sceptics, is now being asked by the very people who championed the technology. And that, in itself, is a sign that the market is maturing.
The data speaks plainly. Only 6% of CFOs reported an increase in profit or revenue as a direct result of AI adoption, according to Gartner research. A McKinsey study reinforces the diagnosis: most organisations remain in the experimentation stage (32%) or running pilots (30%), with only a third actively scaling AI initiatives across the enterprise. And even among those who have progressed along this journey, returns do not always arrive at the expected pace.
What lies behind this disconnect? And, more importantly, what do the corporations that genuinely deliver results do differently?
The problem is not the technology — it is the adoption strategy
The dominant narrative of recent years positioned AI as a ready-made solution capable of transforming businesses on its own terms. One simply had to choose the right tool, train the teams, and await results. This premise, seductive in theory, proved fragile in practice.
What is observed in organisations that struggle is not a technological failure — it is a strategic one. AI was treated as an IT project when it should have been led as a business transformation initiative. The difference between these two approaches is profound and has a direct bearing on outcomes.
IT projects have a beginning, a middle, and an end. They have defined technical deliverables, timelines, and budgets. Business transformation initiatives, by contrast, require process redesign, cultural realignment, redefined metrics, and — above all — executive leadership committed to the long term.
When AI is managed as the former and evaluated against the expectations of the latter, frustration is the inevitable result.
The five most common mistakes in corporate AI adoption
1. Starting with the technology, not the problem
One of the most prevalent missteps is an inverted sequence: the organisation selects the tool and then searches for the problem it might solve. The correct approach is the reverse — identifying the strategic business challenges first, and then assessing whether and how AI can contribute to addressing them.
Organisations that begin with the problem hold a fundamental advantage: they know what to measure. Without a clear value hypothesis, any result becomes anecdote rather than evidence.
2. Scaling before validating
Enthusiasm for AI has led many companies to expand the number of projects before consolidating learnings from the first ones. The result is a fragmented portfolio of disconnected initiatives, each producing insufficient data for any meaningful conclusion.
The lesson from 2025 and 2026 is unambiguous: successful organisations did not begin with their most ambitious project. They started with the most concrete problem, measured the result, and expanded with method.
3. Underestimating data quality
The promise of AI depends, in a direct and non-negotiable way, on the quality of available data. Without reliable, structured, and current information, models produce inconsistent outputs — and projects stall not because of technological limitation, but due to inadequate data infrastructure.
Investment in data management and quality is expected to grow by 23% amongst Brazilian companies over the next two years, according to SAP research conducted with Oxford Economics. This movement reflects a critical realisation: before investing in AI, one must invest in the data that feeds it.
4. Overlooking the cultural and human dimension
Gartner research indicates that only 11% of customer service and support leaders affirmed that generative AI met its primary business objective — and this is one of the contexts considered most mature for adoption. The finding reveals that even in seemingly straightforward cases, technology alone does not deliver.
What consistently makes the difference is the degree to which teams were prepared to work alongside AI — not only from a technical standpoint, but in terms of mindset, trust, and the redesign of their roles. Silent cultural resistance is one of the most significant causes of failure in corporate AI programmes.
5. Absence of governance and success criteria
AI projects without clear success metrics tend to persist without generating real value. The absence of governance creates an environment in which it is difficult to discontinue underperforming initiatives — and equally difficult to scale those that perform well.
AI projects routinely handle sensitive data: client records, employee information, contracts. Before scaling any solution, it is essential to validate security, privacy, compliance, and governance requirements, and to define responsibilities and boundaries of use with precision.
What separates those who deliver from those who experiment
The market is beginning to distinguish between two corporate profiles in relation to AI: those who accumulate pilots and those who generate impact. The differences between them lie not in the tools deployed, but in the strategic decisions made before adoption begins.
A focus on high-impact, lower-complexity use cases to start. Repetitive, high-volume processes — document triage, lead qualification, report automation — are where AI demonstrates ROI most rapidly. This is the path through which organisations build consistency before advancing to more complex decisions.
Data as a strategic asset, not a by-product. Organisations that already have structured, centralised, and governed data hold a genuine competitive advantage in AI adoption. A well-structured CRM, for instance, represents the minimum foundation for any client-facing AI initiative.
Metrics defined before implementation. Companies that establish clear KPIs before a project begins — average task time, cost per process, error rate — have the foundation needed to make rational decisions about scalability or discontinuation.
Active and committed executive leadership. AI adoption without senior leadership sponsorship rarely moves beyond the pilot stage. When the C-suite is genuinely engaged, resources follow, internal barriers fall, and cultural change gains momentum.
The horizon that opens for those who get the strategy right
The numbers for the Brazilian market are encouraging for organisations that adjust their approach. The current average return of 16% on AI investments is projected to nearly double over the next two years, reaching 31%, according to research by SAP and Oxford Economics. Currently, 23% of corporate tasks in Brazil already involve some form of AI support — and this figure is expected to rise to 40% by 2027.
Gartner further projects that by 2028, at least 15% of day-to-day workplace decisions will be made by autonomous AI agents — signalling a transition from AI as a support tool to AI as a structural component of operations.
For organisations still accumulating pilots without scale, this horizon represents a closing window. The competitive advantage of corporate AI does not lie in being the first to adopt — it lies in being the first to generate genuine and sustainable value from it.
From experimentation to strategy: what to revisit now
For corporations that recognise their own challenges in the patterns described above, the strategic review begins with four fundamental questions:
- Do our AI projects originate from clear business problems or from available technologies?
- Is our data sufficiently organised to feed the models we are adopting?
- Were success metrics defined before implementation — or do we evaluate outcomes subjectively?
- Is executive leadership genuinely involved, or is AI treated as an exclusively IT matter?
The answers to these questions reveal, with precision, where the bottlenecks lie — and where the review should begin.
The encouraging news is that the path is well-mapped by those who have already navigated this journey successfully. AI has not failed the companies that have yet to see results. What failed was the adoption strategy. And strategy — unlike technology — is something that can, and must, be redesigned.
The Bakery supports organisations in building structured innovation strategies — from roadmap prioritisation to measuring real business impact. If your organisation is reviewing its AI adoption strategy, speak to our team.
