New Roles: AI Operations Lead

Companies are spending millions on AI tools but seeing little ROI. The problem isn't the tech; it's the lack of a human to operationalize it.

Welcome to the great AI paradox of 2025: Companies across the globe have collectively spent billions on enterprise AI licenses— OpenAI, Microsoft Copilot, Google Workspace AI, Salesforce Einstein—equipping their teams with state-of-the-art technology. But it's already become a strategic crisis.

While tools are being deployed at a massive scale, the operational and human processes required to extract value from them are lagging far behind. The reason for this disconnect isn't a mystery. It was perfectly captured by a manager I recently spoke with:

"If you're working in a job all day, you're fighting fires... am I gonna do this in the way that I know how, or am I gonna do it in the new way that might not work?"

This single quote exposes the massive friction between AI's potential and the daily reality of work. Most employees simply don't have the time or incentive to become AI workflow experts on top of their primary responsibilities. The missing piece isn't a better algorithm or a more expensive software license. It’s a dedicated human role designed to be the bridge between technology and team performance: the AI Operations Lead.

The AI Implementation Gap: Why Your Expensive Tools Are Failing

Many companies are suffering from what can only be described as the "empty gym membership" syndrome. They've bought every employee a pass to a state-of-the-art fitness center (the AI tools), but there’s no personal trainer to guide them. The scale of this issue is massive. McKinsey's latest "State of AI" report reveals the scope of this new reality, with 71% of organizations using generative AI.

This widespread deployment, however, has created a widening chasm between investment and return.

This reluctance to engage isn't laziness or Luddism; it's a rational response to the crushing demands of modern knowledge work. The average employee's day is a barrage of Slack notifications, back-to-back video calls, and an inbox that never sleeps. Most companies have spent years optimizing for efficiency, stripping out the very "slack" time that is essential for deep learning.

Profile of a New Role: The AI Operations Lead

This is where the AI Operations Lead comes in. It’s crucial to distinguish this role from the more strategic Chief AI Officer (CAIO). While the CAIO sets the high-level vision for AI across the enterprise, the AI Ops Lead is a tactical, hands-on role that makes that vision work on the ground, team by team. They are your company's "productivity translators," and their role has five key functions:

• The Detective: Proactively hunts for inefficiencies and workflow bottlenecks across the organization.

• The Builder: Crafts tailored solutions by developing bespoke prompts and building custom, automated workflows.

• The Coach: Acts as the internal champion for AI literacy, leading hands-on workshops and providing 1-on-1 support.

• The Gatekeeper: Works with IT and Legal to evaluate new tools, ensuring security and data privacy compliance (especially with regulations like GDPR).

• The Scout: Explores the rapidly changing AI frontier, testing emerging tools on real-life company scenarios to separate genuine breakthroughs from marketing hype.

A Hybrid Talent: Who is the AI Ops Lead?

The AI Operations Lead is a new breed of hybrid professional. It requires a unique individual who sits at the intersection of two distinct skill sets.

• Hard Skills: The ideal candidate must have deep technical literacy, including a command of prompt engineering, familiarity with APIs and workflow automation tools, and the data analysis skills needed to measure impact.

• Soft Skills: These are arguably more critical. The role demands exceptional communication and change management abilities to overcome resistance, empathy to understand employee fears, and a relentless curiosity to stay on top of the fast-moving tech landscape.

They may come from diverse backgrounds: a former management consultant, a tech-savvy project manager, or an L&D specialist who became an AI power user that can also review efforts in this space.

The Rhythm of the Role: Daily, Weekly, and Monthly Impact

The Daily Focus: Solving Problems on the Ground On a daily basis, the AI Ops Lead is a hands-on problem solver. For example, they might observe a marketing manager spending three hours on a weekly report, then build an automated workflow that reduces the task to five minutes. The outcome of this single intervention is a clear "Efficiency Dividend."

The Weekly Focus: Building Momentum and Capability Weekly, the role involves scaling these small wins through "AI Office Hours," targeted workshops, and progress reports to the cross-functional AI Steering Committee.

The Monthly & Quarterly Focus: Strategic Alignment and Measurement Strategically, the role's impact is shown through a holistic dashboard, proving its value beyond simple metrics.

Over the course of a year, this rhythm compounds, moving from solving small frictions to fundamentally reshaping core business processes.

The First 90 Days: An Implementation Roadmap for Your AI Ops Lead

Hiring this person is just the first step. To ensure success and build momentum, their first three months should be structured and strategic.

• Phase 1: Discovery (Days 1-30): The primary goal is to listen, learn, and map the terrain. This involves conducting "listening tours" with department heads and employees, identifying the most painful, low-value administrative tasks, and forming the "AI Steering Committee."

• Phase 2: Pilot & Prove (Days 31-60): The focus shifts to securing a high-visibility success story. The lead will build and pilot their first automated workflow for one of the identified pain points, build the "Balanced Scorecard" for measuring success, and begin documenting the process to create a repeatable model.

• Phase 3: Socialize & Scale (Days 61-90): With a proven success story in hand, the goal is to evangelize. The lead will run the first training workshops for a wider group, work with internal communications to publicly celebrate the pilot team's success, and build a formal, prioritized backlog of automation projects for the next quarter.

A Playbook for Success: Navigating the Real-World Hurdles

Even with a great plan, the role will face challenges. Success requires proactively addressing the inevitable obstacles and skeptical questions that will arise.

Hurdle 1: The Human Wall of Resistance Employees may view the AI Ops Lead not as a helper, but as an auditor sent to automate their jobs away, leading to mistrust and hostility.

• Solution: The initiative must be introduced by the CEO with a clear message of empowerment. Start with "quick wins" on universally hated administrative tasks to build trust, and celebrate internal "AI Champions" to create social proof and turn adoption into a positive, employee-led movement.

Hurdle 2: The Corporate Maze of Politics and Turf Wars A common objection is, "Couldn't a Chief of Staff or a Product Ops manager just do this?" This skepticism, combined with friction from IT, HR, and Legal departments, can stall the role's progress.

• Solution: The answer is dedicated focus and specialized skills. Existing roles are already consumed by their primary responsibilities and lack the deep, cross-functional AI expertise. To overcome turf wars, the role must have a clear mandate and formal structures for collaboration. This includes an "AI Steering Committee" with stakeholders from key departments. Crucially, the role must have authority, ideally reporting directly to a COO or Head of Strategy. This approach is validated by McKinsey's research, which identifies CEO-level oversight of AI governance as a critical factor for success, signaling that its mission is a top-level business priority.

Hurdle 3: The ROI Trap and The "Temporary Role" Fallacy Leadership may expect magical productivity gains overnight, and some may question the role's long-term necessity, viewing it as a temporary fix.

• Solution: Define success with a "Balanced Scorecard" that measures holistic impact beyond simple cost savings. Set realistic, phased goals to manage leadership's expectations. To counter the "temporary role" fallacy, emphasize the "Scout" function. The AI landscape is not a static technology to be "learned" once; it's a constantly evolving ecosystem. The AI Ops Lead is permanently needed to evaluate, test, and integrate the next wave of tools, ensuring the company's productivity stack never becomes obsolete.

Beyond Productivity: The Path to an AI-Native Culture

The AI Ops Lead's ultimate goal isn't just to install tools or save hours. It is to serve as the catalyst for a cultural transformation. In the short term, they are a fixer and a coach. In the long term, their success means the entire organization starts to think differently. The company evolves from being "AI-aware" to truly "AI-native," where human-AI collaboration is the default operating model for innovation, decision-making, and problem-solving. The true ROI of the AI Ops Lead isn't just efficiency; it's organizational agility and a sustainable competitive advantage for the decade ahead.

Conclusion: Your Next AI Investment Shouldn't Be in AI

Let's be clear: the era of celebrating AI pilots and "experiments" is over. The next phase of competition will be defined not by who has the best tools, but by who can operationalize them fastest. Continuing to buy expensive AI licenses without a dedicated human to drive adoption isn't a strategy; it's a form of corporate malpractice.

The question for every leader to ask on Monday morning is no longer "Which new AI tool should we buy?" but "Who on my team is explicitly responsible for the ROI of the tools we already own?" If the answer is "everyone" or, more likely, "no one," you've identified a critical failure point. The actionable first step for any leader is to commission a two-week "AI Adoption Audit." Task a trusted individual to map current AI tool usage and identify the top three most time-consuming, repetitive tasks in a single department. The results will almost certainly reveal a multi-million Euro opportunity, making the business case for a dedicated AI Operations Lead self-evident.

The AI Operations Lead is the human interface that turns raw technological potential into tangible business velocity. They improve security, standardize quality, and help manage the strategic talent reshuffling that McKinsey's research has identified.

Showing expected headcount decreases in areas like service operations and increases in product development.

This proves the role isn't just about cutting costs; it's about reallocating human capital to the areas of greatest innovation. Crucially, they give your employees the time and tools to focus on the high-value, creative work that AI can't do.

So, before you approve another six-figure software deal, look at your org chart. If you can't point to the person whose full-time job it is to make that investment pay off, you haven't bought a solution. You've just bought a very expensive problem.

Until next week!
Matteo