Bryan Kirschner, Vice President, Strategy at DataStax
Ignoring the potential of generative AI to increase productivity is a surefire way to fall behind as an individual, a team, and an organization. You should put it to work as an “eager intern” or “autonomous agent” (or both) ASAP.
But positioning yourself, your team, and your organization to get ahead requires some strategic thinking.
The most powerful framework I’ve found for effective strategic thinking is what Roger Martin calls the “strategy choice cascade.” Winning strategies are a set of powerful and interrelated choices, made in mindful order: “where to play” precedes “how to win.” And the latter sets the stage for what distinctive capabilities and management systems will give you a competitive edge.
Amplifying your organization’s superpower
Applying this to generative AI, we can be pretty confident that just about everyone, everywhere will leverage generative AI to delegate some “knowledge worker legwork” and create first drafts, for example. But its most impactful use will be found amplifying the power of the specific choices each organization has already made about how to compete and win.
To put it in more colorful terms: “one weird trick” to building a high-impact AI strategy is finding ways it can turbocharge a superpower you already have.
A great example of how to go about finding the fit between the “big bets” an organization has already made and the capabilities of generative AI can be found in a McKinsey discussion of generative AI and the future of HR.
The firm has a concept of “making your own McKinsey.” As a massive organization serving essentially every industry in every geography on any sort of question, there’s a lot of room for people to craft career paths that lead to working on what they are most passionate about.
At the most basic level, this helps with retaining employees who are expected to put in long hours working on hard problems. But the real magic comes from the way it gives employees an opportunity to supercharge two strategic bets by the firm: deep expertise and strong client relationships.
I’ve worked alongside several McKinsey teams, and, at their best, senior consultants are excellent thought partners—even outside of a paid project—because they themselves are invested in the same sorts of issues you care about as an executive.
But the downside of being a massive organization serving essentially every industry in every geography on any sort of question is the level of effort required to research your options, pattern match, and find mentors. McKinsey partners Lareina Yee, Bryan Hancock, and Bill Schaninger home in on how generative AI could help in a discussion of employee evaluations:
But what if I, as the employee, can query, “Who are five success models with my strengths and weaknesses, and what have they gone on to do? How can I visualize my career development? How can I continue to work on it?” I could also have an assistant that helps me map my professional development. In that way, when we check in a year later, I’ve really improved and increased my aspirations.
What if Bill is someone I should model myself on? Instead of Bryan having to introduce me to Bill, generative AI helps me realize that I’ve got the makings of a Bill Schaninger. I can be inspired by that. I think there’s a lot that enhances what we’ve been trying to do so laboriously for years.
Winning with AI: Be mission driven and context aware
A good way to test this sort of thinking is to assess whether an application of AI would be comparably valuable in a company that’s made similar decisions about how to win (and less in the opposite case).
For example: at the time the Four Seasons hotel chain was founded, “Traditionally, hotel employees were poorly paid and considered transient and replaceable.” Its chairman and CEO chose a contrarian path: big investments in training and long-term career development.
Although a Four Seasons employee’s flexibility to shape their own job is probably somewhat more limited than at McKinsey, the hotel company does leverage its global scale to offer customizable growth opportunities, such as “Global Task Force opportunities, offering short-term assignments in other locations,” a program that lets employees travel the world to learn from and connect with employees at other Four Seasons properties, and “a learning professional at each property to drive employee development.”
An autonomous agent that helps purposefully map out professional development might make great sense for Four Seasons given this particular choice about “how to win.” But now imagine the opposite case: another hotel chain that still operates on a “high expected turnover” model. Rightly or wrongly, it has chosen to win by keeping staffing investments, including training costs, low.
In that context, a more impactful use of generative AI might be an agent that acts like a “virtual coach” that new employees can ask questions 24×7 about how to get the basics of their jobs done or handle unusual situations in the moment.
I like to describe this choice about how to win with AI as “working mission-driven and context-aware.” How is it that your organization differentiates itself for employees and customers? It’s probably something that you and your colleagues understand well. That is likely a great jumping-off point for ideating about how generative AI can make your organization’s strength that much stronger.
Learn about how DataStax enables generative AI here.
About Bryan Kirschner:
Bryan is Vice President, Strategy at DataStax. For more than 20 years he has helped large organizations build and execute strategy when they are seeking new ways forward and a future materially different from their past. He specializes in removing fear, uncertainty, and doubt from strategic decision-making through empirical data and market sensing.
Artificial Intelligence, Machine Learning