Beyond Cost-Cutting: Nigel Vaz on Recasting AI as a Strategic Engine for Work
In boardrooms and inboxes across industries, the early promise of artificial intelligence has too often been translated into a single metric: jobs saved, payroll reduced, and short-term margin improvement. Nigel Vaz of Publicis Sapient pushes back against that narrow reading. His argument is simple and urgent: when organizations treat AI primarily as a cost-reduction lever, they strip it of its transformative potential and foreclose a far richer future for work.
Why the cost-cutting story is seductive — and dangerous
Cost-cutting is immediate and measurable. A spreadsheet can show headcount reductions, cycle-time savings, and line-item expense decreases within a quarter. For leaders under pressure to meet earnings targets, that clarity is intoxicating. It also creates a perverse incentive: invest in systems that replace people rather than those that amplify them, prioritize quick automation over long-term productization, and evaluate success in terms of staff reductions rather than value creation.
But the consequences of that framing are profound. Organizations that chase only efficiency risk stagnation. Teams lose trust when automation is framed as a thinning tool. Customers notice when innovation slows. Crucially, the narrow focus can blind leaders to opportunities where AI can unlock new revenue streams, design previously impossible services, and reimagine the shape of work itself.
“AI as a cost play is a one-dimensional story. The real test is whether leaders can see AI as a way to extend human capability and create new sources of value,” Vaz argues.
Reframing: four strategic dimensions of AI
To move beyond cost-cutting, consider four dimensions where AI can play a strategic role:
- Growth and differentiation: Use AI to create products and services that customers will pay for — personalized experiences, new predictive services, and intelligent automation embedded inside offerings.
- Human augmentation: Enhance what people can do rather than replace them — accelerate decision-making, surface insights from data, and reduce cognitive load for employees so they can focus on higher-value work.
- Operational resilience at scale: Build systems that make organizations more adaptive, responsive, and capable of handling complexity — not just cheaper to run, but harder to disrupt.
- Work redesign: Rethink roles, team structures, and career paths so that the workforce evolves alongside AI capabilities, creating more meaningful and productive work.
A practical framework: portfolio thinking, not one-off projects
Vaz recommends treating AI investments like a portfolio. That means balancing:
- Foundational bets: Data platforms, MLOps, and common APIs that reduce friction for subsequent efforts.
- Incremental plays: Small, fast projects that deliver measurable improvements in workflows or customer metrics.
- Transformational initiatives: Integrated launches that create new products or drastically change how work gets done.
- Exploratory moonshots: Higher-risk experiments that may redefine markets or create new business models.
By diversifying investments, organizations capture quick wins while keeping a path to long-term renewal.
Changing the conversation: new metrics and incentives
If AI is only judged by headcount reduction, leaders will optimize for that narrow outcome. Vaz urges a change in the way success is measured. Instead of asking, “How many roles did we displace?” ask questions like:
- What revenue was created or protected because of AI-enabled products?
- How much faster or better are our decisions with AI augmentation?
- How much did customer satisfaction or retention improve?
- Are employees achieving higher output or more meaningful work?
Compensation, performance reviews, and capital-allocation decisions should reflect these broader outcomes. When incentives are aligned to the creation of value rather than its extraction, the kinds of AI projects that get funded will shift dramatically.
Organizing for strategic AI
Turning these ideas into reality requires organizational choices. Vaz describes a set of design principles that leaders can adopt:
- Create durable product teams: Cross-functional teams that own AI-enabled products end-to-end, not just one-off automation tasks.
- Embed data and design expertise: Make data scientists, engineers, and designers partners in value creation, sitting alongside business leaders.
- Invest in the platform: Building shared infrastructure — data pipelines, model registries, APIs — enables reuse and speeds scaling.
- Set guardrails early: Ethical frameworks, explainability standards, and audit trails increase trust and reduce risk.
People strategy: reskilling, mobility and dignity
One of the most consequential aspects of this shift is how organizations treat their people. When AI is framed narrowly as a cutter of jobs, the result is fear and resistance. When it is framed as a tool for enrichment and new opportunity, the result can be motivation and a higher ceiling for talent.
Practical moves include targeted reskilling programs tied to real roles, career mobility paths that leverage AI skills, and redesigning work so that routine tasks are automated while cognitive, creative, and interpersonal work is elevated. It is a matter of design: create roles where humans and AI are partners rather than competitors.
Concrete steps leaders can take this quarter
- Run an AI value audit: Catalogue current AI initiatives and classify them by whether they reduce costs, create revenue, augment people, or redesign work.
- Rebalance the portfolio: Allocate at least 30–50% of new AI investment toward growth and augmentation plays, not just efficiency.
- Define new KPIs: Replace or supplement headcount-focused KPIs with revenue, retention, decision quality, and employee impact metrics.
- Stand up product teams: Move from project-based automation squads to product teams with clear ownership and user-centered roadmaps.
- Publish guardrails: Make transparency and accountability visible — publish policies on use, testing, and monitoring of models.
- Commit to people investments: Tie cost savings to workforce transformation: training, role redesign, and internal mobility programs.
Beware the common traps
There are predictable missteps. The most frequent are:
- Short-termism: Sacrificing strategic bets for immediate cost wins undermines long-term competitiveness.
- Misaligned incentives: Bonuses tied to cost metrics propel the wrong investments.
- Technical hubris: Deploying models without robust monitoring, testing, and human oversight invites errors and erodes trust.
- Neglecting human experience: Systems that frustrate employees or customers will generate resistive behaviors and lost adoption.
A cultural pivot more than a technology one
Perhaps the most important lesson is that this is a cultural and managerial question as much as it is a technical one. Leaders must tell a different story about AI. It is not a magic tool for trimming budgets; it is a lever for reimagining what organizations can do. That requires storytelling, patience, and a willingness to live with some ambiguity while building durable capabilities.
A call to the Work news community
Coverage matters. When reporting focuses predominantly on layoffs and automation as the face of AI, the public conversation narrows in ways that shape policy and corporate behavior. The Work news community is well positioned to broaden that narrative: elevate stories of augmentation, unpack choices leaders make about how to measure success, and hold organizations accountable to the full range of AI outcomes — not just the ones that make for quick headlines.
Nigel Vaz’s plea is not a denial of the efficiency gains AI can bring. It is a call for leadership to be bolder in imagining how those gains are used. Will savings be recycled into growth, reskilling, and product development — or funneled only into dividends and short-term margins? Those choices will determine whether AI becomes a force for reinvention, or merely a tool that quietly narrows the future.
For leaders and readers of Work news alike, the question is immediate: will we allow the story of AI at work to be written as a ledger of cuts, or will we insist on a broader narrative that foregrounds human potential, new value creation, and durable organizational renewal?
