GenAI for C-Level: Decisions the CEO Must Make Now

The GenAI moment for executives: why this time is different

Every technology generation has brought with it a promise of transformation. The commercial internet in the 1990s, cloud computing in the 2010s, enterprise mobility — all demanded relevant strategic decisions. But generative artificial intelligence represents a disruption of a different nature. We are not facing a new tool that optimizes existing processes. We are facing a technology that redefines which processes should exist at all.

For the CEO, this distinction is fundamental. Previous technology waves could be delegated to the CTO or CIO without major consequences. GenAI cannot. It affects business models, cost structures, customer value propositions, and ultimately, the company's competitive strategy. When a competitor can generate personalized commercial proposals in minutes instead of days, when credit analysis that took a week now happens in hours, when customer service scales without proportional cost increases — the impact is not operational. It is existential.

This is why decisions about GenAI cannot be made solely by the technology committee. They belong on the agenda of the CEO and the board of directors. The World Economic Forum estimates that 40% of work hours will be impacted by AI by 2030. PwC reports that 72% of CEOs already consider GenAI a strategic priority, but only 28% have a formalized strategy. There are three decisions that cannot wait — and others that require strategic caution.

The 3 critical decisions every CEO must make

1. Strategic positioning: how GenAI fits into the business model

The first and most fundamental decision is about positioning. Will GenAI be used to optimize the existing business model or to create new ones? The answer determines the level of investment, the risk profile, and the speed of adoption.

For the majority of mid-size and large enterprises, the pragmatic recommendation is: start with optimization, prepare for transformation. Use cases such as document automation, productivity assistants, and information summarization generate quick, visible wins that build organizational credibility for the AI agenda. But the true competitive differentiator lies in cases that redefine the value proposition: proprietary credit models, personalization at scale, demand anticipation with proprietary data.

The CEO must make this decision explicitly and communicate it to the board. A company that treats GenAI as an efficiency project will have a radically different posture than one that treats it as a growth lever. Both postures are valid — what does not work is ambiguity.

2. Governance policy: who decides what about AI in the organization

The second structural decision is about governance. GenAI introduces unprecedented risks for most organizations: reputational risk (incorrect or biased outputs), regulatory risk (data protection laws, sector-specific regulations), intellectual property risk (training data, generated outputs), and operational risk (dependency on external models).

The question for the CEO is not technical — it is organizational: who has the authority to approve the use of GenAI in critical processes? What is the gate between experimentation and production? How do we ensure auditability?

The model we have seen work best is federated governance: a small, senior central core that defines standards, policies, and platforms, while business units execute their own projects within those guardrails. The center does not execute; the center governs. This model balances execution speed with risk control.

3. Internal vs. outsourced capability: what stays in-house

The third decision is about talent and capability. The deficit of qualified AI professionals is structural. Given this reality, the decision must be stratified.

What must be internal is what we call executive AI literacy — the ability of the C-Level and managers to understand what AI can and cannot do, to formulate good questions, and to evaluate recommendations. This cannot be outsourced.

For technical competencies, a hybrid model tends to work best: a small internal core of ML and data engineers that ensures continuity and business knowledge, complemented by specialized partners for specific projects and demand peaks. The pragmatic recommendation: buy the base, customize the differentiator. Use market foundation models as infrastructure and concentrate internal investment on the layer of proprietary data and the workflows that truly differentiate the business.

Decisions that require caution

Not every decision about GenAI needs to be made now. Some premature decisions can be more harmful than strategic patience.

• Massive investment in proprietary GPU infrastructure: The cost of training and maintaining proprietary models can exceed tens of millions of dollars per year. For the majority of companies, the equation does not add up when compared to commercial model APIs. Wait for costs to stabilize before internalizing heavy infrastructure.
• Aggressive workforce replacement: Companies that use GenAI as a pretext for massive headcount reductions lose the tacit knowledge necessary to feed and validate the models. AI augments human capability — it does not replace it linearly.
• Long-term commitment to a single vendor: The GenAI market is in rapid flux. Long contracts with a single provider create vendor lock-in at a time of accelerated evolution. Maintain architectural flexibility and data portability as non-negotiable requirements.

Haste without governance is the recipe for the 70% of AI projects that fail. Move fast on pilots, move with rigor at scale.

Board checklist: what to present to the board of directors

The board of directors needs visibility into the company's AI strategy, but does not need to understand transformers or fine-tuning. What the board needs to see:

1. Strategic positioning: How does AI connect to the business strategy? What competitive advantages does it enable or protect?
2. Initiative portfolio: What projects are underway, at what stage (pilot, scale, production), and with what results?
3. Investment and ROI: How much is the company investing in AI (total and per initiative)? What is the measurable return to date?
4. Risk map: What are the main regulatory, reputational, and operational risks? How are they being mitigated?
5. Governance: Who is responsible for AI in the organization? What policies exist for ethical and responsible use?
6. Talent and culture: Does the company have the necessary competencies? What is the upskilling plan for leadership and teams?
7. Benchmarking: How does the company compare to competitors and the market in AI maturity?