PT
EN
Companies across all sectors are betting big on artificial intelligence. Sophisticated dashboards are built to impress executives. Data scientists construct increasingly advanced predictive models. The promises are great: intelligent automation, real-time decisions, hyperpersonalization.
But in many cases, something invisible begins to block progress — and it is not the technology. It is the foundation on which it rests: data quality.
When the Project Looks Right but Results Disappoint
Imagine the scenario: an organization launches a generative AI project with strong executive backing. The team is engaged, the partners are market-recognized, the strategy is ambitious.
On paper, everything looks right.
But over time, problems appear:
- Predictive models offer products to customers who have open complaints in customer support.
- Dashboards bring incorrect data or fail to answer strategic questions.
- Automations make decisions completely disconnected from reality.
The analysis reveals a clear pattern: inconsistent, fragmented data without governance. The problem is not the AI — it is the foundation sustaining it.
This Is Not the Exception. It Is the Rule.
Recent studies from the Big Four reinforce this reality:
- According to Deloitte (2023), “55% of companies avoid scaling AI initiatives” due to failures in data quality and governance.
- PwC’s global research shows that “only 27% of executives fully trust the data” used in strategic decisions.
- According to EY (2023), “60% of data leaders say bad data directly impacts financial performance” — whether through reporting errors, automation failures, or misguided decisions.
In short: technology is not lacking. Structure is. This problem has deeper roots than it seems: as discussed in Technology, Information and Value, the quality of use is as determinant as the quality of information.
Seven Actions for Leadership to Avoid This Bottleneck
1. Abandon the “everyone is doing AI” impulse
Heavy AI investments without data maturity can generate more frustration than results. The competitive differentiator lies in the organization’s ability to sustain that transformation with consistency — not in the speed of adoption.
2. Embrace the paradox: do AI even if your data is not good yet
Do not wait for perfection to start. Build MVPs and improve data along the way. One of the biggest mistakes is standing still waiting for an ideal data structure that never arrives. Start imperfect — and iterate.
3. Establish a long-term strategic plan (minimum five years)
You can invest thousands of dollars in a division that will be discontinued. There is nothing worse than structuring information that will not be used due to a strategic shift. A lasting vision with clear goals aligned to the business direction is essential.
4. Build a dedicated team, even if lean
Start with a minimum cell:
- A data steward (data guardian)
- A developer
- A product manager
This structure can already generate relevant impact with controlled cost.
5. Be ruthless about “self-made managers”
There is no more damaging profile for an organization than the professional who only thinks about their own results. Building a data structure with intelligence is the organization’s agenda — not one department’s.
Business areas must actively engage in evolving data quality. First come the organization’s objectives — then personal objectives within it.
6. Structure data based on practical use cases
Data quality must be connected to concrete problems. Involving stakeholders from the beginning increases adoption, ensures ongoing sponsorship, and prevents data projects from becoming technical initiatives disconnected from the business.
7. Adopt a modern, scalable, and economically viable architecture
It is not about spending more — it is about spending wisely. Cloud, modularity, and elasticity are pillars of a structure that grows with the business without compromising OPEX.
Note: this can be expensive if not planned responsibly. Architecture choice is a strategic decision, not merely a technical one.
The Question That Precedes Any AI Project
AI projects do not fail only due to technical failures. They fail, in most cases, for a more basic — and more neglected — reason: bad data.
Before investing in the next algorithm or cutting-edge solution, perhaps the most strategic question is:
“Is our organization prepared, in terms of data, to sustain this journey?”
That answer may determine whether your AI becomes a competitive differentiator — or just another expensive frustration. To understand why every high-potential technology goes through this cycle before delivering real results, read AI and the Productivity Paradox.