By Mark Tice
It’s a well-documented idea that using cross-functional teams–those consisting of professionals from different departments–to solve business problems can remove siloed, department-specific approaches that result in subpar solutions.
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Someone on a cross-functional team is more likely to ask questions or challenge an idea that anyone from a single department may never have thought to consider. These questions may lead to complex debates–ones that require deeper problem-solving because of the diversified expertise and opinions among the team members–but they’re necessary stress tests that ultimately lead to better solutions.
This approach to company growth–one grounded in a potentially uncomfortable but beneficial debate–is the basis on which companies should be implementing AI. And it starts at the personnel level, just as it does in team creation.
The latest, most advanced packaged AI solutions foster cross-functionality. Scalable, affordable and usable throughout organizations, they pull together disparate data and create easy-to-understand visualizations that enable cross-functional teams to focus on more strategic work and solve more complex problems.
The idea that AI saves time isn’t a new one. But using AI to help evolve traditional organizational roles, enhanced collaboration, and the useful cross-functional friction that results when disparate teams are brought together is innovative. The resulting AI-enabled insights usher in workplace changes that push executives to adjust their growth strategies, projects or priorities in ways they wouldn’t have imagined otherwise.
Businesses that have already implemented this approach have uncovered tremendous value–both in their elevated talent and in their increased bottom lines.
In the media and entertainment world, for example, CFOs are becoming revenue strategists as they analyze subscriber data for insights about audiences and trends. That’s a shift that entails a highly interdisciplinary approach requiring insights that originate and apply to every dimension of their enterprise.
Knowing which subscribers are likely to cancel their subscriptions to a video streaming service and how they might be retained, for example, requires a deep understanding and interpretation of audience behavior. The services possess the data they need to gain that knowledge, of course, but AI gives cross-functional teams the tools they need to generate insights that maximize the data’s value in the CFO’s decisions.
In health care, currently less than 5 percent of cancer patients enroll in clinical trials because most cross-functional teams of hardworking doctors, staff, trial sponsors and others lack a means of quickly determining whether patients are eligible for studies that apply extremely specific criteria for admission. Using AI and medical data, clinical researchers can spend less time looking for trial participants and more time selecting optimal treatments.
The same capabilities apply in other areas of medicine. Researchers and physicians at Semmelweis University in Hungary, for example, are using AI to analyze historic patient data to detect higher cardiovascular risks and predict outcomes for current patients.
In manufacturing, specialized analysts are taking on strategic responsibilities with the foresight of AI that reduces costly stoppages. Rather than waiting for vital equipment to break down, AI and sensitive vibration sensors give the analysts and their cross-functional teams the first clues about small problems in machinery that could balloon into crises if not for early interventions and maintenance.
Risk officers in banks and financial firms can deploy AI to dramatically reduce investigations of false positives and focus on real instances of suspicious behavior. Achieving that goal efficiently requires an omniscient perspective on an institution’s data. Once AI develops that perspective, only cross-functional teams have the context and multidimensional expertise to fully realize its potential in rooting out fraud.
The potential implications are far-reaching. The Center for Creative Leadership, a premier leadership development organization, for instance, uses enterprise AI to identify racial, sexist and other biases in leadership development data—and the potential implications for organization policies and practices. Most immediately, the team has used AI to mitigate biases when developing leader profiles and personalizing content for leadership development journeys.
That is how AI supports the development of cross-functional teams. Rather than focusing entirely on hiring more talent, executives found AI elevated the capabilities of their preexisting personnel, a remarkably cost-effective investment that generated significant value as measured by the problems it solved. This success became especially clear during the COVID-19 lockdowns.
Both online and brick-and-mortar retailers have used AI to forecast shortages and manage inventories for years. Although the model isn’t foolproof–remember the shortages of bathroom tissue and Clorox wipes during the early days of the pandemic–it was a key tool that companies relied on to help keep shelves stocked when global supply chains were in chaos.
Now AI is expected to play a critical role in tackling supply chain challenges in the post-COVID economy. A team overseeing a company’s supply chain with AI serves as a “central cross-functional brain” for that enterprise, McKinsey wrote in a report earlier this year. But while AI can monitor every link in a business’ supply chain from procurement to sales, forecast demand and suggest responses to deviations with end-to-end transparency, achieving those capabilities require enterprises that are ready.
“Companies must take organizational steps to capture the full value from AI,” the McKinsey report found. They need to build cross-functional talent to improve and integrate planning, in other words.
AI can, and should, go hand in hand with the digital transformations happening across nearly every industry. Forward-thinking executives, however, will use cross-functional teams to extract the most value from AI. They know that a diverse set of professionals will be more likely to optimize AI to reach insights that narrower perspectives would overlook.
It’s the best and quickest way to field a dream team.
Mark Tice is an operating partner at SymphonyAI, a Los Altos, California-based enterprise AI company focused on delivering AI solutions for vertical sectors. Tice has over 25 years of experience working in the software field, including two successful exits to NYSE-listed companies as CEO of venture-backed startups.
Illustration: Dom Guzman
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