The C-Suite Guide to Winning with AI

This playbook breaks down how senior executives can lead meaningful adoption that drives measurable business outcomes and creates enduring competitive advantage.

AI is no longer a moonshot project confined to research labs. It has crossed the chasm into mainstream enterprise use—and fast.

According to a 2024 McKinsey Global Survey on AI, 72% of organizations have adopted AI in at least one business function, up from just 20% in 2017. This rapid acceleration has made AI the fastest-adopted general-purpose technology in recent enterprise history.

Public familiarity is also on the rise. Pew Research reported in 2023 that 58% of U.S. adults were familiar with ChatGPT specifically, and around one in five had used it. While these numbers are evolving quickly, the signal is clear: AI adoption is no longer a fringe trend—it’s a competitive reality.

Yet despite rising investment and interest, the execution gap remains stark.

McKinsey notes that while nearly all companies surveyed expect to increase their investment in AI, very few have scaled its use or captured significant business value. Fewer than one in five companies have adopted AI across multiple business units, and many cite integration, data readiness, and talent as persistent barriers.

Meanwhile, early adopters are pulling ahead. A Boston Consulting Group (BCG) analysis found that companies effectively using AI at scale reported up to 1.5× faster revenue growth, 1.6× higher total shareholder return, and 1.4× better return on invested capital compared to their industry peers. These aren’t theoretical gains—they’re bottom-line realities.

For enterprises, the window to build durable AI capability is open now. But it’s closing fast. The cost of inaction isn’t just lost efficiency—it’s losing the next decade of growth to more agile, AI-native competitors.

Why AI? The Three Pillars of Value Creation

The best way to sell AI to your board—and your teams—is to make the business case airtight. Three strategic pillars have emerged from early enterprise leaders that justify why AI must be embedded into core operations:

1. Competitive Advantage

AI enables faster time-to-market, real-time personalization, and differentiated customer experiences that feel tailored, not templated.

Klarna, the Swedish fintech company, exemplifies this advantage.

The company launched an AI assistant that now handles two-thirds of all customer service chats, reducing average resolution time from 11 minutes to just 2. The initiative is expected to drive a $40 million profit improvement—all while maintaining customer satisfaction scores on par with human agents.

"Superior experiences for our customers at better prices, more interesting challenges for our employees, and better returns for our investors," said Klarna CEO Sebastian Siemiatkowski. That’s not hype. That’s a margin strategy.

2. Operational Efficiency

AI can eliminate bottlenecks in legacy workflows, especially for repetitive, rules-based tasks that drain human capital without adding strategic value.

Take OpenAI's internal use case: Their support team was inundated with routine requests that consumed time and context-switching energy. By building an internal automation platform integrated with Gmail, OpenAI enabled agents to instantly access relevant customer data and generate replies—freeing them to focus on more complex service issues.

The result? Hundreds of thousands of tasks are now automated monthly.

The takeaway for enterprise leaders: AI isn’t just about doing new things. It’s about doing existing things with dramatically fewer resources.

3. Talent Leverage

AI enables upskilling at scale—non-technical employees can now execute tasks that previously required specialist input, effectively flattening the skill gap.

BBVA, the Spanish banking giant, rolled out ChatGPT Enterprise globally and encouraged employees to build their own use cases.

Within five months, over 2,900 custom GPTs had been developed internally, accelerating timelines from weeks to hours across departments from Legal to Risk Management.

“We consider our investment in ChatGPT an investment in our people,” said Elena Alfaro, Head of Global AI Adoption at BBVA. “AI amplifies our potential and helps us be more efficient and creative.”

The message for CHROs and COOs? This is the democratization of enterprise productivity.

The C-Level Playbook: A Phased Approach to AI Transformation

If you’re sitting in the boardroom wondering where to begin—or how to move beyond scattered pilots—the most important truth is this: AI transformation doesn’t start with a grand vision. It starts with execution, and it scales through structure.

Enterprise leaders succeeding with AI follow a phased approach, focusing on agility in the beginning and resilience over time. Below is a breakdown of this three-phase strategy for turning AI from a shiny object into a systemic asset.

Phase 1: Foundation & Quick Wins (Months 1–3)

Goal: Build momentum and demonstrate value with minimal risk.

Action 1: Secure Executive Sponsorship

No major transformation succeeds without C-suite sponsorship. Set bold, public targets—such as reducing time-to-insight by 90%, or doubling campaign velocity. And, appoint a cross-functional AI steering committee composed of senior leaders from Product, IT, Risk, and Operations.

According to OpenAI’s enterprise deployment team, the most successful companies treat AI as a new paradigm—not a feature—and structure dedicated teams to champion that shift across departments.

Action 2: Identify High-Impact, Low-Effort Use Cases

Forget moonshots.

Focus on “anti-to-do” tasks—manual, repetitive activities that waste valuable talent. Whether it's summarizing meeting notes, tracking competitor movements, or producing first-draft policy documents, AI can handle these with ease.

Launch Darkly’s Chief Product Officer Claire Vo uses this tactic to guide her team: “Every time I do something I find annoying, I ask myself—how can I not have to do this again?”.

Action 3: Establish Initial Governance

Governance shouldn’t be a bottleneck but it must exist.

Develop lightweight risk protocols for data privacy, model output review, and compliance guardrails. Include Legal, InfoSec, and Compliance teams early to preempt friction down the line.

Morgan Stanley’s structured use of evals before scaling AI across advisor workflows is a leading example. Their three-tiered evaluation—on translation accuracy, summarization relevance, and human-comparison tests—ensured alignment with regulatory and quality benchmarks.

We went from being able to answer 7,000 questions to a place where we can now effectively answer any question from a corpus of 100,000 documents

says David Wu, Head of Firmwide AI Product & Architecture Strategy at Morgan Stanley.

Phase 2: Scale & Empowerment (Months 4–12)

Goal: Build an internal engine for AI innovation.

Action 1: Drive Broad Internal Enablement

Train teams on what OpenAI calls the six AI use case primitives: content creation, research, coding, data analysis, ideation, and automation. Organize hackathons, department-specific workshops, and peer-to-peer demos to spark usage.

Marketing at Promega, a life sciences company, scaled content development using GPTs, saving 135 hours in just six months. Their team now uses AI for everything from first-draft email campaigns to multi-language ad repurposing.

Action 2: Foster Decentralized Innovation

Empower business users—not just IT—to lead AI projects.

Follow Estée Lauder’s GPT Lab model: cross-functional teams composed of business owners, technical leads, and subject matter experts co-design use cases, test performance, and iterate based on real business value.

This decentralized approach scales AI ownership and distributes innovation muscle across the organization.

Action 3: Empower Developers

For CIOs and CTOs, the mandate is clear: eliminate the developer bottleneck. Build internal platforms that let engineering teams create AI-powered applications quickly and securely.

Case in point: Mercado Libre.

With its Verdi platform powered by GPT-4o, over 17,000 developers can now deploy AI applications with built-in security, routing, and API integrations—reducing backlog and enabling innovation at speed.

Phase 3: Differentiate & Optimize (Year 2 and Beyond)

Goal: Create a sustainable competitive moat and drive enterprise-wide transformation.

Action 1: Prioritize Strategic, Cross-Functional Investments

Use an Impact/Effort framework to triage and prioritize initiatives.

Projects that sit in the high-impact, high-effort quadrant (e.g., personalized recommendations, real-time financial insights) may take time, but often deliver outsized ROI.

Indeed’s job-matching engine augmented with GPT to explain “why” a job was recommended. It delivered a 20% increase in application starts and a 13% rise in hires. That’s what strategic ROI looks like.

Action 2: Build a Moat with Customization

Generic models won't win markets.

Enterprises like Lowe’s fine-tune GPT-3.5 on proprietary product catalog data to improve ecommerce search, boosting tagging accuracy by 20% and error detection by 60%.

Fine-tuning turns AI from a generalist into a domain expert—one that speaks your language, reflects your tone, and understands your customers.

Action 3: Implement Rigorous, Continuous Evaluation

Treat AI performance like financial performance: measure and refine it continuously. Conduct regular evaluations on output accuracy, relevance, compliance, and hallucination risk.

Morgan Stanley’s rollout serves as a gold standard. Every use case passed a gauntlet of benchmarks before moving to production—ensuring trust, safety, and strategic alignment.

Measuring What Matters: Tying KPIs to Strategic Value

In AI, what gets measured gets optimized.

But traditional metrics like software deployment counts or IT ticket resolution speeds fall short of capturing AI’s real business impact.

For C-level leaders, tying AI outcomes to core business goals is essential, not just for internal accountability, but for winning continued investment and executive buy-in.

Let’s reframe the KPIs through the lens of the three strategic value pillars introduced earlier: Operational Efficiency, Competitive Advantage, and Talent Leverage.

1. Operational Efficiency: Time and Cost Reduction

AI isn’t just about making work easier—it’s about doing more with less, at scale.

Relevant KPIs:

• Time saved per task/process

• Cost reduction per department

• Reduction in human error or rework

• Lower support ticket volume

Example: Poshmark, the fashion resale marketplace, used ChatGPT to auto-generate Python scripts that reconcile millions of spreadsheet rows, saving its finance team hours each week.

Weekly performance reports and executive memos are now AI-generated, dramatically reducing manual labor and elevating analytical output.

2. Competitive Advantage: Revenue Growth and CX Uplift

This is where AI pays off visibly—in market share, personalization, and faster go-to-market cycles.

Relevant KPIs:

• Revenue uplift from AI-enhanced channels

• Customer Satisfaction (CSAT) and Net Promoter Scores (NPS)

• Conversion rates or average resolution time

• Churn reduction or lifetime value (LTV) increase

Example: Klarna’s AI assistant not only cut service resolution time from 11 to 2 minutes, but also improved customer satisfaction while saving $40 million in support costs.

That’s a flywheel—better CX yields better retention and stronger margins.

3. Talent Leverage: Productivity and Innovation Activation

AI doesn’t replace people. It enables them to do more impactful work. This pillar is especially critical for CHROs and COOs aiming to retain high-performers.

Relevant KPIs:

• Internal adoption rates (% of employees using AI weekly)

• Number of use cases launched by non-technical staff

• Time to complete formerly cross-functional workflows

• Employee satisfaction and engagement survey scores

Example: BBVA empowered employees to build over 2,900 custom GPTs within five months. The result? Departmental innovation scaled bottom-up, and project cycle times were slashed from weeks to hours—all without engineering bottlenecks.

The key takeaway: KPIs must shift from tech-first to value-first.

Every AI project should report into a strategic business metric—revenue growth, cost efficiency, innovation velocity, or workforce capability. That’s what earns boardroom credibility.

The Next Frontier — Preparing for Agentic AI

While most companies are still deploying AI as copilots—augmenting human decision-making—the next leap will be agentic AI: autonomous agents capable of executing multi-step tasks across systems, tools, and contexts.

This shift will be as transformational as cloud computing—if not more.

Emerging Capabilities: From Co-Pilot to Operator

OpenAI’s “Operator” exemplifies what’s coming. This internal tool can navigate web interfaces, fill out forms, interact with SaaS tools, and retrieve or input data—without needing APIs or custom integrations.

Similarly, deep research agents can now synthesize hundreds of online and internal sources to generate comprehensive reports. Internal studies at OpenAI show these tools save over four hours on complex research tasks.

These aren’t prototypes—they’re already being deployed.

Strategic Implications for the Enterprise

1. Workflow Orchestration Over Task Automation
Companies will move from single-task use cases (e.g., writing emails) to entire workflow chains that AI can execute end-to-end.

Think: market analysis → campaign design → asset creation → localization → performance monitoring—all done autonomously.

2. Data as the Strategic Lever
Agentic AI demands richer data environments. Companies that have clean, structured, and accessible data will be able to train more context-aware agents—and reap exponentially higher returns.

3. Culture of Experimentation
The path forward isn't about buying the latest tools. It’s about fostering a culture where teams are encouraged to experiment, fail fast, and iterate based on business impact. Companies like BBVA and Estée Lauder are already demonstrating how decentralized innovation drives adoption at scale.

What the C-Suite Should Do Today

• Appoint a VP or Director of Autonomous AI Initiatives

• Invest in data infrastructure and governance for agent enablement

• Start small: pilot agents in well-structured, repeatable processes (e.g., finance reporting or customer onboarding)

• Update risk frameworks to cover decision-making autonomy, audit trails, and compliance for AI agents

Conclusion

The real challenge of enterprise AI isn’t technological—it’s cultural.

Too many companies still treat AI like a shiny gadget in the innovation lab, relegated to pilots, demos, or departmental experiments.

But the organizations making real, measurable progress—those pulling away from the pack—share one critical trait: they embed AI into the very fabric of how they work.

They don’t just ask, “What can we automate?”
They ask, “How should this process look if we reimagined it with AI from day one?”

AI Is a Business Strategy, Not a Tech Stack

As Stéphane Bancel, CEO of Moderna, put it:

We’re looking at every business process—from legal to research, to manufacturing, to commercial—and thinking about how to redesign them with AI.

This kind of thinking doesn’t start in the IT department. It starts in the C-suite—with leaders who understand that AI is not an upgrade. It’s an operating system for the modern enterprise.

That’s why this playbook doesn’t just outline tactics. It’s a call to action for executives who want to lead a generational transformation—not just keep pace.

The Road Ahead: Start Small, Scale Intentionally, Lead Decisively

Here’s what every C-level executive should internalize:

• Start small – Identify one department, one use case, one measurable outcome.

• Scale intentionally – Move from individual tasks to workflows to org-wide platforms.

• Lead decisively – Don’t wait for the “perfect model” or the “next version.” The companies thriving in AI didn’t wait. They acted, learned, and adapted.

Leadership—not engineering—is the critical variable in AI transformation.

You don’t need all the answers on day one. But you do need to start asking the right questions—and giving your teams the resources, trust, and ambition to answer them.

The Bottom Line

AI will not replace you. But the companies using AI will outperform those that aren’t.

And the executives who understand how to wield it—not just responsibly, but boldly—will be the ones who shape the next decade of enterprise value.

This is your moment. Lead it.