With decades of experience in management consulting, Marco Gaietti is a seasoned expert in Business Management, specializing in strategic operations and organizational transformation. His expertise in bridging the gap between legacy institutional knowledge and modern digital infrastructure provides a unique vantage point on the current AI revolution. Having navigated complex shifts across various global industries, he offers a profound understanding of how 115 years of corporate heritage can be revitalized through cutting-edge technology to empower a workforce of 150,000 “Associates.”
Large organizations often struggle with top-down technology mandates that fail to gain traction. How does empowering a global workforce to build custom, no-code AI agents change the adoption curve, and what specific steps ensure these tools solve actual role-specific problems rather than adding to digital clutter?
The adoption curve shifts dramatically when you move from a “mandate” to a “capability,” turning the employee into the architect of their own efficiency. At a massive organization like Mars, which operates in over 80 countries, a top-down approach usually hits a wall because a developer in a central hub can’t possibly understand the daily friction of a sales lead in a different hemisphere. By providing no-code and low-code tools, we allow 150,000 Associates to identify their own “micro-pain points” and build agents that address them instantly. This organic growth ensures that the technology is pull-based, meaning employees use it because it actually lightens their specific load, not because they were told to log in. The specific step to avoid clutter is focusing on “business solutions at the core,” where the technology acts as a silent enabler of existing growth pillars rather than a standalone shiny object.
Centuries of institutional knowledge are frequently trapped in disconnected data silos across diverse business segments. What are the practical mechanics of using agentic AI to bridge these gaps, and how can leaders measure whether this connectivity is successfully improving end-to-end execution in sales or marketing?
The practical mechanics involve deploying a unified AI operating system, such as Gemini Enterprise, which acts as a cognitive layer over 115 years of accumulated data across food, snacks, and petcare segments. Instead of an employee spending hours hunting through legacy folders, an agentic AI can synthesize information from 50+ diverse brands to provide actionable insights in seconds. We measure success by looking at the acceleration of “end-to-end execution,” specifically through initiatives like One Demand AI which targets brand building and sales. If a marketing team can reduce the time-to-market for a new campaign because they have instant access to historical performance data across silos, that is a tangible win. Leaders should track these “innovation velocity” metrics to see if the connectivity is actually fueling the company’s growth pillars.
Enterprise AI adoption is currently outpacing the development of formal oversight frameworks. What are the primary risks when non-technical employees design their own automated agents, and what specific governance features must be prioritized to maintain security and consistency without stifling grassroots innovation?
The primary risk is a “governance gap,” where the speed of employee experimentation leaves the company vulnerable to security breaches or inconsistent brand messaging. Currently, while nearly 60% of workers have access to sanctioned AI tools—a jump from under 40% just a year ago—only about 21% of enterprises have mature governance in place. To mitigate this, the platform itself must have “guardrails by design,” ensuring that every agent created is automatically compliant with company principles and security protocols. By prioritizing centralized governance features within a unified platform, you allow for grassroots innovation because the safety checks are built into the tool’s DNA. This means an Associate can experiment freely without the fear of accidentally leaking proprietary data or violating global regulations.
Shifting toward an employee-driven AI model changes the fundamental design of many roles. How should leadership teams redefine job descriptions when AI becomes a capability shaped by the worker, and what metrics can track the impact of this autonomy on long-term engagement and productivity?
Redefining job descriptions requires moving away from task-based lists and toward “outcome-based” roles where the worker is viewed as a manager of digital agents. In this model, an Associate’s value is no longer in data entry or rote analysis, but in their ability to direct AI to unlock institutional potential. Leadership should focus on metrics that reflect this autonomy, such as the number of custom agents deployed and the subsequent hours reclaimed for high-value strategic work. We are seeing a shift where 74% of organizations forecast moderate to high use of AI agents by 2027, which suggests that the “ability to leverage AI” will soon be a core competency in every job description. Long-term engagement is tracked by looking at employee sentiment regarding their “digital enablement”—essentially, whether the technology makes them feel more powerful or more burdened.
Bringing business solutions to the core while using technology as an enabler requires a cultural shift. What strategies help transition a traditional workforce into a community of creators, and can you share a step-by-step approach for scaling these individual AI successes across different global regions?
Transitioning to a “community of creators” starts with a mindset shift where technology is framed as a way to “unlock potential” rather than replace heads. First, you provide the no-code infrastructure globally to ensure equitable access across all 80+ countries of operation. Second, you identify “power users” within specific segments, like petcare or snacking, who are solving real-world problems and highlight their work as internal case studies. Third, you implement a scaling mechanism where a successful agent built in one region can be “templated” and shared with another, creating a cross-pollination of ideas. Finally, you ensure that global leadership reinforces the idea that digital investment is a tool for Associate empowerment, creating a sense of psychological safety that encourages widespread experimentation.
What is your forecast for agentic AI adoption in the enterprise?
My forecast is that we are on the cusp of a total structural realignment, where the “agentic” nature of AI will move from a pilot phase to being the primary operating system for the global workforce. Within the next three years, I expect the percentage of enterprises with mature governance to double as they scramble to catch up with the 74% of organizations that will be using these agents for daily operations. We will see a shift where the “digital divide” is no longer about who has the best software, but who has the best-trained workforce capable of building their own solutions. Ultimately, the companies that thrive will be those like Mars that view AI not as a cost-cutting measure, but as a way to turn over a century of “static” knowledge into a dynamic, living asset for every single employee.
