Bryan Kirschner, VP of Strategy, DataStax
Ignoring the potential of generative AI to improve productivity will surely leave you behind as individuals, teams, and organizations. You should be working as an “enthusiastic intern” or “autonomous agent” (or both) as soon as possible.
But it takes a certain amount of strategic thinking to give yourself, your team, and your organization the edge.
The most powerful framework I have found for effective strategic thinking is what Roger Martin calls the Strategy Choice Cascade. A winning strategy is a series of powerful, interrelated choices made in a careful order, where ‘where to play’ precedes ‘how to win’. And the latter sets the stage for what unique features and management systems provide a competitive advantage.
Expand your organization’s superpowers
If we apply this to generative AI, we believe that almost everyone, everywhere, will be using generative AI to delegate some of the “knowledge worker work,” for example, to write the first draft. can be pretty sure. But we find that its most influential use is in amplifying the power of the specific choices each organization already makes about how to compete and win.
To put it in more colorful terms, “one weird trick” for building a high-impact AI strategy is finding ways to make the superpowers you already have even stronger.
A good example of how to find the fit between the “big bets” organizations have already made and the capabilities of generative AI can be found in McKinsey’s discussion of generative AI and the future of human resources.
The company has a concept of “making your own McKinsey”. As a large organization serving basically any industry in any region and on any kind of problem, there is a lot of room for people to build career paths that lead to what they are most passionate about.
At the most basic level, this helps retain employees who are expected to work long hours on difficult problems. But the real magic comes from the way employees are given the opportunity to strengthen the company’s two strategic bets: deep expertise and strong customer relationships.
I have worked with several teams at McKinsey and senior consultants make excellent thought partners at their best, even outside of paid projects. Because senior consultants themselves invest in the same kinds of issues that concern you as an executive.
But for all sorts of questions, the downside of being a large organization serving basically every industry in every region is the need to explore options, match patterns, and find leaders. level of effort. McKinsey partners Lareina Yee, Brian Hancock, and Bill Shanninger highlight how generative AI can help the employee appraisal debate.
But what if I, as an employee, could ask, “Who are the five successful models with my strengths and weaknesses, and what did they do after that?” How can I visualize my career development? How can I keep working on it? You can also hire an assistant to help you plan your professional development. In this way, I was truly grown and aspiring when I checked in a year later.
What if Bill was the person I was supposed to be my own model? I was. You can be inspired by it. I think there’s a lot that enhances what we’ve been working hard to do over the years.
Winning with AI: Mission Driven and Context Aware
A good way to test this kind of thinking is to assess whether the application of AI is equally valuable (or less valuable) in companies that have made similar decisions about how to win.
Example: When the Four Seasons Hotel chain was founded, “traditionally, hotel employees were underpaid, considered temporary and replaceable.” I chose the opposite path of investing heavily in my career development.
While Four Seasons employees have perhaps somewhat more limited flexibility in shaping their jobs than McKinsey, the hotel company leverages its global scale to offer customizable growth opportunities. For example, “Provide global task force opportunities, short-term assignments elsewhere.” , a program that allows employees to travel around the world to learn from and connect with employees at other Four Seasons properties, and “Learning Experts at Each Property to Accelerate Employee Development.”
Given this particular choice regarding “how to win,” an autonomous agent that helps deliberately plan professional development might make a lot of sense for Four Seasons. But now imagine the opposite case. In other words, another hotel chain that still operates on a “high expected sales” model. For better or worse, the company chose to win by keeping staffing investments low, including training costs.
In that context, a more effective use of generative AI would be like a “virtual coach” who could ask new hires 24/7 questions about how to perform the basics of their job or how to deal with unusual situations in the moment. It may be an agent that works for
I’d describe this choice on how to win with AI as “mission-driven, context-aware work.” How does your organization differentiate itself for employees and customers? You and your colleagues probably know it too well. This can be a great starting point for thinking about how generative AI can augment the strengths of your organization.
Learn how DataStax enables generative AI here.
About Brian Kirshner:
Brian is the Vice President of Strategy at DataStax. For more than 20 years, we have helped build and execute strategies for large companies looking for new ways to move forward and a future that is radically different from the past. He specializes in removing fear, uncertainty and doubt from strategic decision-making through empirical data and market sensing.
