Start with a blunt warning: tech leaders kept repeating the same sentence to me across conference stages, boardrooms and podcast mics this year — if you don’t learn to use AI, someone who does will outcompete you. That idea isn’t fearmongering; it’s a framing device for the choices executives are making now about investment, talent and accountability.
Four practical truths the smartest leaders keep circling back to
The chorus from the industry — from startup founders to heads of trillion-dollar firms — clustered around four ideas. They’re simple, but the work to act on them is not.
- Use AI or lose ground. Executives from Nvidia to OpenAI stressed that AI fluency is becoming table stakes: the competitive edge goes to people and teams that apply these tools effectively, not to those who merely warn about them. That pressure is reshaping hiring and training choices across sectors.
- Soft skills are rising in value. As automation lifts routine work, human strengths — empathy, judgment, collaboration — become the differentiator. Leaders want people who can translate machine outputs into decisions that matter to customers and colleagues.
- The AGI debate matters for strategy. Some leaders talk as if general or superintelligent systems are imminent; others are cautious. Either way, the conversation changes how firms think about safety, long-range bets and guardrails. (If you want the scene-setting on that debate, see how experts are arguing over human-level AI.)(/news/ai-experts-debate-human-level-intelligence)
- Humans need to stay in control. Whether the systems become dramatically more capable or remain narrow tools, executives repeatedly returned to one priority: design AI so it augments agency, not replaces it.
- Share decision rights. A trio of tech, finance and strategy leaders yields better choices than a single “tech czar.”
- Track the right levers. Innovation maturity, automation budget and monetization strategy mattered most in Deloitte’s model.
- Consider hybrid roles. Some organizations are experimenting with combined people-and-tech leadership to manage how humans and AI work together.
- Start with customer or employee friction points and measure them. Pick one to transform.
- Put CTO/CTO-equivalent, CFO and CSO (or their deputies) on a joint investment committee.
- Democratize safe, approved tools to frontline managers so local insights scale.
- Invest in developer workflows and MLOps so experiments become repeatable.
- Build clear guardrails and incident plans tied to misuse, autonomy and safety.
Purpose-first investment: a retailer’s real-world example
Purpose is more than a slogan when you look at Sam’s Club’s rollout of AI across stores. Their leadership decided technology should serve people, not the reverse. That principle guided a rapid rollout of computer-vision exit arches that scan carts in milliseconds. The tune-up: exit times are now faster, manual receipt checks vanished, and associates were freed from repetitive inventory walks.
The effect wasn’t only operational. By eliminating routine tasks, the company reports more associate time for relationship-building — a simple human outcome that feeds retention and member satisfaction. That tidy example captures a recurring point: AI that reduces friction for customers and increases meaningful work for employees tends to stick.
Where boards and C-suites get this wrong (and how to fix it)
Deloitte’s recent modeling gives a quantitative nudge to the qualitative arguments CEOs keep making: AI returns correlate strongly with who sits at the decision table. CTOs driving investments without finance and strategy partners often deliver technical capability — but less profit upside. When CFOs and CSOs are active co-owners, companies are markedly more likely to see improved EBITDA and broader KPI wins.
Practical implications:
Three operational priorities every CEO should treat as non-negotiable
Across a Blackstone CEO forum and other executive gatherings, three repeatable moves emerged as the fastest ways to go from pilot to impact:
1. Educate and equip teams. Push tools into the hands of the people making daily decisions. Democratization — giving local managers access to AI to spot trends or analyze feedback — speeds adoption and relevance.
2. Focus on one needle-moving area. Rather than dozens of pilots, pick a single business process where AI can materially change outcomes and scale it.
3. Use software development as a proving ground. Building or adapting AI-powered engineering processes shows what’s possible and creates reusable assets.
Blackstone’s leaders summed this up simply: human leadership is the biggest driver of AI infusion in enterprises. In other words, technology enables, but people deliver.
Operational playbook: a quick checklist for the next 6–18 months
Infrastructure and search: the quiet arms race
Underlying these efforts is a less glamorous but essential race: compute, data plumbing and retrieval. From enterprise-grade model search to proposals for new data-center architectures, infrastructure choices shape how fast a company can iterate. Recent advances in integrated enterprise search and document grounding are already changing how knowledge workers retrieve context and make decisions — a shift that matters as much as model selection. See a recent example of how search and workspace integration is being rethought for productivity at scale.)(/news/gemini-deep-research-gmail-drive-integration)
Longer term, firms are even debating novel locations and architectures for compute — a reminder that strategic infrastructure investment can be a competitive moat.(/news/google-suncatcher-space-datacenters)
Talent and culture: the slow, essential work
Automation will displace tasks; it won’t automatically create fulfilling roles. Leaders who successfully translate efficiency into better work invest in reskilling, job redesign and clearer career paths. That’s why Sam’s Club’s outcome — freeing associates to build relationships rather than walk aisles with clipboards — matters as much as the technology itself.
Managers also need a new bar: AI fluency plus people skills. Hiring and promotion criteria are shifting accordingly.
The next year will be an operational sprint and a cultural marathon at the same time. That tension is the point: AI’s speed demands decisive, human-led stewardship.
If you want to move beyond the slogans, start by choosing a single, measurable problem and aligning the right leaders around it. The debate over timelines for more powerful AI will continue, but the immediate opportunity — and responsibility — is clear: invest with purpose, govern with humility, and equip the people who do the work.