There is a specific kind of gold rush today. The companies that are winning the race for successful AI implementations are investing up to four times more in the foundations – data quality, management and staff readiness – than the market’s prodigies. These are gigantic outlays that are akin to building an ultra-modern skyscraper. The problem is that despite the luxurious façade, you can still hear the structure creaking in the boardrooms.
This is where the title paradox manifests itself. Although the money stream flowing towards data ‘hygiene’ is unprecedented, according to Gartner data, only one in three technology leaders are looking to the future with genuine optimism. Only 39% believe that current investments in artificial intelligence will realistically improve the company’s bottom line. What we have, then, is a situation where the biggest players are buying the most expensive insurance policies while still being unsure whether their ship will even make it to port.
Why is this happening? Because the mandate of data and analytics leadership by 2030 is evolving dramatically. It is no longer about simply ‘owning’ the technology, but about providing the perceptual intelligence and contextual foundations that allow machines to realistically understand the business world. The success of AI has become a challenge of trust and a complete overhaul of the value architecture. Building an AI-first strategy is a pioneering leadership that must face the fact that the old ways of counting profits are no longer compatible with the new algorithmic reality.
The trap of traditional ROI, or measuring the future with an old ruler
Trying to measure the potential of AI with a classic ROI is akin to assessing the usefulness of electricity solely through the lens of candlelight savings. In corporate excel sheets, where every investment has to “bounce back” in a few quarters, building deep contextual foundations often looks like an expensive whim. It is this accounting corset – trying to measure the future with an old ruler – that causes anxiety for nearly two-thirds of technology leaders.
Meanwhile, the modern approach to D&A requires a shift from static ROI to value composition. Leaders who actually set the pace are no longer treating AI as just another ERP module to be ‘fobbed off’. Instead, they are building a value flywheel: a model in which the efficiency gains gained from AI are deliberately and systemically reinvested in the further development of perceptual intelligence and innovation.
In this view, AI becomes the company’s new operating system, not just a tool for cost optimisation. If an organisation gets stuck in an endless loop of Proof of Concept cycles, looking for ad hoc savings, it will probably never achieve the scale necessary to survive the 2030 transformation. This is because the real value comes not when an algorithm is implemented, but when integrated engineering practices allow trust and context to scale across the enterprise.
Foundations are not just about technology
In 2030, competitive advantage will not be measured by terabytes of data, but by the precision with which machines can interpret it. This is where the new mandate of the D&A leader comes in: to deliver *perceptual intelligence. Until now, the role of the data director has often been reduced to being the custodian of a digital archive; today, he or she must become the architect of the organisation’s ‘collective brain’.
The technology itself is merely the engine. The real fuel is context, treated as critical infrastructure. AI agents, lacking a deep semantic layer, resemble brilliant chess players playing in total darkness – they have immense computing power, but cannot see the board. Without a trusted contextual foundation, autonomous systems become mere expensive confabulation factories. This is why shifting the centre of gravity from ‘having models’ to ‘designing meaning’ is so crucial.
Data management is now a steering wheel support system. Pace-setting companies are able to embed privacy and ethics issues directly into the workflows of AI agents. For trust in the world of algorithms is not a sentiment – it is a technical necessity. Without it, every decision made by AI will be fraught with risks that no rational board would accept. A true D&A leader understands that his or her job is no longer to provide dry reports, but to build a foundation on which AI can finally stop guessing and start realistically understanding the business.
Strategy 2030: AI-first as a state of mind, not a shopping list
Ultimately, AI-first transformation is not an IT project, but a test of leadership maturity. By 2030, D&A leaders must abandon the role of technology providers in favour of architects of new operating models. True scaling requires the courage to break out of the ‘endless loop of Proof of Concept cycles’ and move to deeply integrated engineering practices. Data, software and context must stop operating in silos – in the new reality, they are one inseparable organism.
Let us return to the initial paradox: why do only 39% of leaders believe in the financial success of their investments? This scepticism is paradoxically a good sign. It shows that the market is moving out of its phase of childlike admiration for ‘magical’ algorithms and is beginning to understand the scale of the challenge. True return on investment in AI is not a matter of luck, but of consistently building trust and perceptual intelligence.
