How Mars, Orange, and Reckitt Are Scaling AI Beyond Pilots
Technology leaders share hard-won lessons on moving from experimentation to enterprise transformation while keeping employees engaged.
Enterprise leaders have moved past asking whether to invest in AI and now face a harder question: how to extract real value at scale. The challenge isn't technical capability—it's organizational execution.
A panel of technology executives from Mars Pet Nutrition, Orange, Reckitt, and Saint-Gobain shared insights on navigating the transition from exploratory projects to enterprise-wide AI deployment, according to Fortune.
Breaking free from pilot purgatory
Many organizations launch multiple AI experiments only to discover they cannot scale beyond initial tests. Rahul Shah, global chief digital and information officer at Mars Pet Nutrition, advocates breaking the journey into discrete phases: identifying five strategic priorities, then progressing from pilots to scale, from use cases to capabilities, and finally from information to decisions.
Ursula Soritsch-Renier, group chief digital and information officer at Saint-Gobain, emphasizes starting with employee pain points rather than top-down mandates. Nigel Richardson, chief information and digitization officer at Reckitt, warns against treating pilots as quick wins. His team found success by examining complete workflows rather than inserting tools into existing processes.
Bruno Zerbib, chief technology and innovation officer at Orange, offers a contrarian view: pilots remain valuable exploration spaces. He cautions against artificial urgency to demonstrate progress, noting that no universal playbook exists for AI deployment.
Why it matters
The gap between AI experimentation and production deployment represents billions in unrealized investment. Organizations that master this transition gain competitive advantage not through technology alone but through their ability to redesign work itself. The companies succeeding at scale share a common approach: they treat AI implementation as organizational change, not IT deployment.
Redesigning work, not replacing workers
The executives agree that workforce engagement determines success or failure. Zerbib focuses on specific job functions where AI delivers clear return on investment, creating success stories that demonstrate enhanced rather than eliminated roles.
At Saint-Gobain, where the industrial workforce has less technology exposure, Soritsch-Renier sees opportunity in reallocating time from administrative tasks to revenue-generating activities like cross-selling. Richardson cites Reckitt's Write-It tool, an agentic AI solution that reduced scientific documentation time from days to minutes, freeing researchers for innovation work.
Shah frames the transformation positively: while coordination roles may diminish, human judgment becomes more valuable as AI handles routine information processing.
Communicating value to boards
Securing continued investment requires translating technical progress into business outcomes. Shah notes that boards care about growth and protection, not use cases. Richardson's team conducts quarterly reviews comparing projected benefits against actual results.
Soritsch-Renier points to concrete wins: an AI tool that scans tenders produced leads with 15 percent higher qualification rates and 10 percent better conversion. Zerbib stresses honesty about struggles alongside successes, warning that overpromising damages credibility.
The common thread across all advice: consistent, transparent communication builds trust and sustains investment.
These insights were first reported by Fortune, which convened the panel of technology leaders to discuss enterprise AI implementation challenges.
This is an original analysis by the Omega editorial team. Source reporting: AI Watch.
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