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You could say that when it comes to AI, today’s companies are involved in a competition reminiscent of the space race of the 1960s. So it should come as no surprise that OODA, an old acronym for “observe, guide, decide and Act “, has been adopted by those who want to accumulate business advantages through the use of data and machine learning.
The OODA loop for AI updates the language, but the intent is the same. The more data you have, the better your models will be. The better your models, the better your service. This leads to more usage and subsequently more data. So the cycle continues.
Following this model, you would think that most companies would be quick to adopt AI. In more cases than you might think, it’s the opposite. And this hesitation could have massive repercussions.
According to Boston Consulting Group (BCG) Research Starting in 2020, one in three public companies will cease to exist in its current form by 2025, a rate six times higher than 40 years ago. Additionally, 44% of today’s leading companies have only held office for at least five years, up from 77% in 1970.
This opportunity shows that AI not only has the potential to be an equalizer, it can also be an advantage. That’s because the AI OODA loop has a steering wheel effect. The more times a business goes through it, the greater the competitive distance. Companies that have implemented this model will simply be more difficult to catch up.
What Stops Most Organizations?
In a word, leadership. Many executives who subscribe to methodologies like Six sigma, I don’t want to think about probabilistic methods and uncertainty. They just don’t recognize the need for AI. Even if they did, they would likely be dismayed by their technical debt and how their workforce lacks people with enough experience to connect AI to business use cases.
This shot is backed by a 2019 O’Reilly Media poll made by my regular collaborator Paco Nathan. In the chart below, you plotted the percentage of responses you received when you asked companies at different stages about their AI adoption challenges.
As you can see, those who have advanced to what Paco calls the evaluation phase are no longer in denial and recognize what is preventing them from adopting AI. Their identified problems are a data shortage, a hiring gap, and having executives facing challenges from multiple departments. These companies don’t have the solutions yet, but they don’t intimidate them like the first group.
Interestingly, when a company has entered the maturity phase, its problems are no longer really problems. The companies in this group are making money from artificial intelligence and are working on ways to increase their profits even more.
How to move on
A key idea of a Joint BCG-MIT Sloan Management Review Research Project presents a compelling case for adopting AI for competitive advantage. These data show that the profitability differential between companies in the top and bottom quartiles has almost doubled in the last 30 years.
In my previous article Deadline 2024: Why You Only Have 3 Years Left to Adopt AI, I explored the opportunities AI can unlock and the required sense of urgency. So how can companies take off and go through those evaluation and maturity phases? It really requires a culture change within a company, and of course that starts with the person at the top.
This is reinforced by McKinsey & Company’s State of AI in 2020, where high-performing AI respondents were 2.3 times more likely to view their senior leaders as very effective. This same group was also more likely to say that AI initiatives have a knowledgeable and committed champion in the C suite.
On Nancy Giordano’s new book Leadership, delves into the future of business administration. The essence: there has to be a transition from leadership to leadership. Nancy, who also advises my company, defines the former as “a static, closed, hierarchical organizational approach designed to scale efficiently for constant short-term growth.” She goes on to say that the latter differs as it “cultivates a dynamic, adaptive, caring and inclusive mindset that supports continuous innovation for long-term sustainable value.”
Once the concept of leadership is rethought, it becomes easier to accomplish what needs to be done to start using AI (as it should be run from the top down). This includes:
Design a plan for how AI will transform. Having a vision of how AI will affect your business over the next three years is critical. Consider how you will drive data acquisition, digital spend, and use case exploration in a practical way that eliminates risk and speeds time to results. BCG-MIT research found that companies with the right data, technology and talent, but no strategy, only have a 21% chance of achieving significant profits.
Allow disparate teams to work together. A legacy business practice, such as grouping business units (and their data) to minimize risk, is now a liability. A company that wants to succeed with AI must tear down those walls and empower a network of teams to explore new ways of working together. This will help improve agility and innovation.
Lean on diversity. It’s not just about making sure teams have a mix of genders and ethnicities. It is also about inviting employees with different professional experiences. Companies hoping to thrive with AI should embrace a wide variety of perspectives. This means being open to dissent as well.
Rethink how people interact with machines (and vice versa). BCG research shows that when you create feedback loops, there is a greater chance of success. To take advantage of this, you will want artificial intelligence to learn from human feedback, humans to learn from artificial intelligence, and artificial intelligence to learn autonomously. Doing these three things gives a business a 53% chance of making a significant financial profit (versus a 5% chance of doing nothing).
Moving forward with AI not only requires a change in technology, it also requires a change in process, culture, and collaboration. Those who will thrive with AI are the ones who invest in strong cultures and better communication structures.
High-performing AI employees tend to agree. In the 2020 McKinsey survey, 52% of these employees said their team leaders feel empowered to advance AI initiatives in collaboration with their peers across all business units and functions. 42% also believe that strong, centralized coordination of AI initiatives must be balanced with close connectivity to business end users.
If you are serious about using artificial intelligence to gain and maintain an edge in the marketplace, ask your employees about the changes they would like to see in how they are managed and how they interact. A feedback loop is as crucial to success as the OODA loop. By institutionalizing both, you will be able to accumulate an advantage, or at least stop falling behind.
Steve Meier is co-founder and chief growth officer of the artificial intelligence services company KUNGFU.AI.
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