Better, faster, easier, cheaper: That’s the promise of gen AI. For at least some companies, it’s becoming the reality as well, as leaders find new ways for gen AI—and the increasingly capable agents it enables—to automate, augment, and accelerate work across virtually every function. Early adopters are using gen AI to help strengthen supplier negotiations in procurement and improve quality control in equipment maintenance (see sidebar “Gen AI’s potential across operations”). One digital marketing platform is even using gen AI to manage “long tail” sales accounts that were previously too labor-intensive to serve, for an annual revenue gain of more than $30 million.
Setting a bold aspiration for enterprise-wide impact. These obstacles are all too familiar to the typical COO, who is charged with leading the continuous-improvement efforts that sit at the core of next-generation operational excellence. They were the starting point for a tech industry COO who recognized gen AI’s potential to break long-standing operational logjams—and understood that success would depend on how well people embraced gen AI solutions.
The tech company’s work with gen AI started by tackling one of its thorniest cross-functional problems, where complex coordination led to frequent delays in generating highly tailored statements of work that outlined the details of the technology services each client would buy. Assembling a statement of work required the relationship manager to collect input from experts in internal functions ranging from finance and legal to data security, as well as from the delivery managers and solution architects leading the day-to-day work—and the client, too. Rework and errors were a fact of life, slowing response times to such a degree that relationship managers missed deadlines for important requests for proposals.
To build a tool that could generate statements of work for more than a dozen product lines, the company needed to scale quickly. The answer for this organization was to centralize. Leaders created a single working group comprising three main teams: one for engineering, one for business and data requirements, and one for change management.
The three teams collaborated extensively, particularly in reimagining workflows that would take full advantage of gen AI’s efficiencies. Previously, for example, creating a statement of work involved elaborate rounds of requirements gathering, feasibility analysis, and risk assessment—inevitably generating rework as later reviews identified issues that affected earlier decisions. By analyzing thousands of earlier statements, the new tool developed templates that highlight the most frequent potential problems up front. Specialist experts in legal, compliance, or related functions can instead focus their efforts on problems that don’t have a clear precedent.
Increasing employees’ confidence in a gen AI solution. The change management team’s involvement proved crucial not only in building the tool but also in ensuring uptake once it was deployed. Following the core principles of the influence model, leaders ensured that each product line had its own dedicated change champion, who served as an intermediary between users and the working group to develop and adapt statement-of-work templates that would meet user needs. The change champion would then help communicate with users and build their skills both in using the tool and in improving its capabilities.
The ultimate result is a templatized statement of work that replaces hundreds of document variations, each taking days to produce, with just five that now require only hours to build. This has eliminated thousands of hours of repetitive labor, freeing experienced employees to focus more on high-value work.
Strengthening COO–CIO collaboration
These examples illustrate how using AI to rethink a stream of value can yield much more improvement than simply automating a few tasks. It also requires a much closer integration between the COO and CIO, whose traditional incentives have often been in tension.
COOs charged with modernizing complex, legacy operations have often found off-the-shelf IT solutions to be a difficult fit at best. Yet the cost and complexity of bespoke technology can create substantial burdens for the IT function and the CIO. Some of the friction has dissipated as newer technologies, such as edge computing and standardized industrial communications protocols, have taken hold—along with modular IT architecture and more flexible development practices. But there’s more to be done.
AI’s short innovation cycles and high resource needs have raised the pressure for technology investments to yield their projected returns on schedule, if not sooner. When COOs and CIOs collaborate more effectively, troves of data can become usable insights for revamping operations and creating entirely new sources of value.
The technology company shows how this collaboration can produce results. The COO of the business took the lead in identifying the transformation opportunity and developing it so that it met operational requirements. The CIO’s involvement expanded the vision of what was possible, such as by finding new opportunities to adapt enterprise-wide gen AI investments for the specific data needs of creating statements of work. Along the way, the CIO’s team became more agile in working with the operations team so that the entire project could meet milestones.
COOs already know that dozens of narrow gen AI use cases are unlikely to add up to lasting operational improvement. Instead, gen AI’s potential comes from how it helps leaders rethink entire value chains. This is at the heart of the COO’s role, and its future.
