The Real Gap Between AI Leaders and Laggards
As AI moves from experimentation to enterprise-wide adoption, organisations are splitting into two groups: those deploying tools and those rethinking how value gets created in an AI-enabled world.
Ali Alkhafaji, who recently became CEO at Apply Digital after serving as chief AI officer at Omnicom, identifies three factors that separate leaders from laggards. The first is the quality of the question being asked.
Laggards ask how to do existing work faster with AI. Leaders ask what becomes possible that wasn't before. That difference cascades through everything: which problems get prioritised, which talent gets hired, which partnerships matter, and what metrics actually count.
The second separator is organisational courage. Most companies have run AI pilots. Laggards file them away as proof of concept. Leaders use them as starting points for reimagining how value flows through the business, willing to disrupt their own models before competitors do.
The third is cultural curiosity. The organisations pulling ahead aren't necessarily those with the biggest budgets. They're the ones where genuine curiosity about what's next runs through teams, not just leadership. People read market signals early, ask hard questions about where technology heads, and connect those answers back to business strategy. Teams that have spent three or four years actively experimenting, questioning and learning aren't just ahead technically-they're ahead strategically. That gap is far harder to close than any technology gap.
CEOs Need to Shift from Project to Posture
Most CEOs treat AI adoption as a project: commission an initiative, appoint a leader, measure deployment percentages, then wonder why results don't follow. The misconception is that adoption requires only operational change. It requires the whole organisation to think differently.
If a CEO doesn't use AI 50 to 100 times a day, their team won't either. The shift that matters is treating AI not as a tool to deploy but as a capability built into how people work daily.
That means serious investment in enablement and change management-not as afterthoughts to technology rollout, but as the primary programme. The technology is straightforward. Helping a 10,000-person organisation develop new instincts about when to trust AI, when to challenge it and how to collaborate with it effectively is a genuine leadership challenge. Most CEOs underinvest in this muscle.
The other shift is urgency without panic. Some AI leadership is performative-moving fast to signal innovation without depth to sustain it. CEOs who will look back proudly on this period are those who built genuine capability, not those who announced the most initiatives.
Balancing Near-Term Wins With Long-Term Bets
The short-term play is clear: infuse AI into customer experience work already underway. Clients feel the impact immediately-faster content, smarter personalisation, more responsive digital products. It's demonstrable, builds confidence and funds the next conversation.
The longer-term bet is structural. Apply Digital is building toward a model of forward-deployed AI consultants-practitioners who embed inside client organisations not to deliver a project and leave, but to build capability from within. That pairs with investment in repeatable, industry-specific solutions grounded in chosen verticals.
Generic AI consulting is already becoming commodity work. Durable advantage sits in knowing CPG, sports and entertainment, B2B manufacturing or healthcare deeply enough that AI solutions carry genuine domain intelligence, not just technical competence applied to a brief.
The two bets reinforce each other. Near-term customer experience work generates relationships, real-world data and credibility that make the longer-term model possible. Walking into a boardroom to pitch AI transformation requires a track record of delivery.
How Scale Becomes a Liability
AI is the ultimate democratiser of size. A 10-person team with the right enablement and tools can operate with the reach and output of an organisation ten times larger. That's genuinely new, and it cuts both ways.
For large enterprises, the implication is uncomfortable: scale, which has historically been a moat, no longer guarantees protection. A nimble competitor with deep AI fluency can close gaps that would have taken a decade to close before.
The path forward for large organisations isn't pushing AI tools out to everyone at once. It's building genuine fluency through structured training and enablement, identifying where AI amplification creates the most leverage, and removing cultural and process friction that stops people from actually changing how they work.
The technology is available to everyone. Organisations that unlock it will be those that treat workforce transformation as the primary investment, not the support track for a technology deployment.
Learn more about AI strategy for CEOs or explore resources on AI for executives and strategy.
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