Artificial intelligence (AI) is not a solo endeavour. To achieve success at scale, organizations must break down silos and foster collaboration across functions, disciplines, and levels of seniority. The most impactful AI initiatives are those that bring together people, technology, and transformation in a coordinated effort.
The necessity of cross-functional collaboration
AI projects often start as proofs of concept (PoCs) or pilots in a single department or function, but their true value is realized when they are integrated across the organization. This requires collaboration between business leaders, technologists, data scientists, and support functions such as cybersecurity, legal and operations.
“The first thing about going from pilot to live is you’ve got to know how you’re going to get into production. You need to be thinking about production support and the operating model. It will be the supporting functions that typically you need to bring along on the journey,” explains a senior AI director, who works for a global corporate group within the travel sector, speaking at a recent Berkeley panel discussion event.
Too often, supporting functions are brought in too late in the process, leading to delays, rework, and missed opportunities. By involving such teams from the outset, organizations can ensure that AI solutions are robust, compliant, and sustainable.
The critical need for AI product management
AI systems are not ‘set and forget’. They require ongoing management, monitoring, and improvement.
“They start from a certain base level of performance and then you need to make them better. It’s a very iterative process,” the AI director said of agentic AI delivery at his organization.
He advocates taking a product management approach.
Quickly, agentic AI becomes a product management problem. Nearly every agent we've built has led to the issue of who's going to manage the system and its continued improvements – the digital employee. Product management is a huge gap. When it comes to collaboration and how you build that link with the business, you need someone who's not an SME or a project manager, but who can keep the evolution going.”
A senior AI director, a global travel company
AI collaboration at the senior leadership level
Senior leadership collaboration is also critical to the success of AI at scale. “One of the things that I've done is set up a forum with the chief people officers, chief transformation officers, CIOs and CTOs. They all have to come together because at the domain level, you're thinking about the operating model, the workforce, skills and capabilities, as well as the technology,” the senior AI director said.
By bringing together leaders from across the organization, businesses can align on strategy, allocate resources effectively, and drive coordinated action.
Overcoming barriers to AI collaboration to achieve real value
Collaboration is not always easy. Different functions may have different priorities, languages, and ways of working. Overcoming these barriers requires strong leadership, clear communication, and a shared commitment to the organization’s goals, which ultimately should translate into meaningful value.
Describing his organization’s view, the AI director said, “Cross-functional collaboration goes beyond the single use case to a very intentional transformation at the level of an entire business domain. Our hypothesis has always been to get value at scale, to get beyond the pilot, to reach that high level of return – the big numbers on the P&L sheet.”
Collaborating to achieve sustainable AI transformation
AI success at scale is a team sport. By fostering cross-functional collaboration, involving supporting functions early, and investing in product management and senior leadership alignment, organizations can maximize the value of their AI investments and drive sustainable transformation.
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