Consider this real-life scene: Reflecting on the difficult moments of his week, the new CEO of a manufacturing company felt angry. His attention kept going back to the tension in several executive-team meetings. He had an urge to shake the team and push several of its members, who were riven by old conflicts, to stop fighting and start collaborating to solve the company’s real problems. He also sensed, though, that a brute-force approach was unlikely to get very far, or to yield the creative insights that the company desperately needed to keep up with its fast-changing competitive environment. Instead, he calmed himself, stopped blaming his team, and asked himself whether he could break the logjam by pursuing truly new approaches to the company’s problems. It was then that his mind turned to, of all things, artificial intelligence.
Like many leaders, the CEO was struggling to cope with the stress induced by uncertainty, rising complexity, and rapid change. All of these are part and parcel of today’s business environment, which is different enough from the one many of us grew up with to challenge our well-grooved leadership approaches.
It becomes pertinent here to describe the relationship between agility and technology that at first glance seems to add complexity but in fact can be a healing balm: artificial intelligence (AI), which we take to span the next generation of advanced data and analytics applications. Inner agility and AI may sound like strange bedfellows, but when you consider crucial facts about the latter, you can see its potential to help you lead with clarity, specificity, and creativity.
The first crucial fact about AI is that you don’t know ahead of time what the data will reveal. By its very nature, AI is a leap of faith, just as embracing your ignorance and radical reframing are. And like learning to let go, listening to AI can help you find genuinely novel, disruptive insights in surprising and unexpected places.
A second fact about AI is that it creates space and time to think by filtering the signal from the noise. You let the algorithms loose on a vast landscape of data, and they report back only what you need to know and when you need to know it.
Let’s return to the CEO above to see an example of these dynamics in action. The CEO knew that his company’s key product would have to be developed more efficiently to compete with hard-charging rivals. He urgently needed to take both cost and time out of the product-development process. The standard approach would have been to cut headcount or invest in automation, but he wasn’t sure either was right for his company, which was exhausted from other recent cost-cutting measures.
All this was on the CEO’s mind as he mused about the problematic executive dynamics he’d been observing—which, frankly, made several of his leaders unreliable sources of information. It was the need for objective, creative insight that stoked the CEO’s interest in AI-fuelled advanced data analytics. A few days later, he began asking a team of data-analytics experts a couple of broad and open-ended questions: What are the causes of inefficiencies in our product design and development workflow? What and where are the opportunities to improve performance?
The AI team trained their algorithms on a vast variety of data sources covering such things as project life-cycle management, fine-grained design and manufacturing documents, financial and HR data, suppliers and subcontractors, and communications data. Hidden patterns in the communication networks led to a detailed analysis of the interactions between two key departments: design and engineering. Using aggregated data that didn’t identify individual communications, the team looked at the number of emails sent after meetings or to other departments, the use of enterprise chat groups and length of chats, texting volume, and response rates to calendar invites, the algorithms surfaced an important, alarming discovery. The two departments were barely collaborating at all. In reality, the process was static: designers created a model, engineers evaluated and commented, designers remodeled, and so on. Each cared solely about its domain. The data-analytics team handed the CEO one other critical fact: by going back five years and cross-referencing communications data and product releases, they provided clear evidence that poor collaboration slowed time to market and increased costs.
Leading with inner agility
By liberating the AI team to follow a direction and not a destination, the CEO’s original question, “How do we improve productivity?” became a much more human, “How are we working as a team, and why?” Based on this new empirical foundation, he enlisted the engineering and design leaders to form a cross-disciplinary team to reimagine collaboration. Working with the data scientists, the team was able to identify and target a 10 percent reduction in time to market for new-product development and an 11 percent reduction in costs. But the CEO didn’t stop there. He also used the experience to ask his executive team to develop a new agility. The previously fractured team worked hard to build a foundation of trust and true listening. Regular check-ins helped them pause, formulate new questions, invite healthy opposition, and ask themselves, “What are we really solving for?” The team was growing more complex to address the company’s increasingly complex challenges.
In essence, AI can be a huge help to the leader who’s trying to become more inwardly agile and foster creative approaches to transformation. When a CEO puts AI to work on the toughest and most complex strategic challenges, he or she must rely on the same set of practices that build personal inner agility. Sending AI out into the mass of complexity, without knowing in advance what it will come back with, the CEO is embracing the discovery of original, unexpected, and breakthrough ideas. This is a way to test and finally move on from long-held beliefs and prejudices about their organization, and to radically reframe the questions in order to find entirely new kinds of solutions.