Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Watch now.

You could say that when it comes to AI, companies today are engaged in a competition reminiscent of the ’60s space race. So it should be no surprise that OODA, an old pilot’s acronym for “observe, orient, decide and act,” has been co-opted by those wanting to amass business advantages through the use of data and machine learning.

The OODA loop for AI updates the language, but the intent is just the same. The more data you have, the better your models get. The better your models are, the better your service becomes. This leads to more usage and, subsequently, more data. Thus the cycle continues.

Following this model, you’d think most companies would be rushing to adopt AI. In more cases than you’d think, it’s the opposite. And this hesitancy could have massive repercussions.


Intelligent Security Summit

Learn the critical role of AI & ML in cybersecurity and industry specific case studies on December 8. Register for your free pass today.

Register Now

According to Boston Consulting Group (BCG) research from 2020, one in three public companies will cease to exist in its current form by 2025 — a rate six times higher than it was 40 years ago. Furthermore, 44% of today’s leading companies have only held their position for at least five years, down from 77% from 1970.

This opportunity shows AI doesn’t just have the potential to be an equalizer, it can be an advantage. That’s because the AI OODA loop has a flywheel effect. The more times a business cycles through it, the greater the competitive distance. Companies that have operationalized this model are simply going to be harder to catch up with.

What holds most organizations back?

In a word, leadership. Many executives, who subscribe to methodologies like Six Sigma, don’t want to think about probabilistic methods and uncertainty. They just don’t recognize the need for AI. Even if they did, they’d probably be dismayed by their technical debt and how their workforce lacks those with enough experience to connect AI to business use cases.

This take is supported by a 2019 O’Reilly Media survey conducted by my frequent collaborator Paco Nathan. In the below chart, he plotted the percentage of responses he received when asking companies at different stages about their AI adoption challenges.

As you can see, those who’ve advanced to what Paco calls the Evaluating phase are no longer in denial and recognize what’s preventing them from embracing AI. Their identified problems are a data crunch, a hiring gap and having execs who are facing challenges from multiple departments. These companies don’t yet have the solutions, but they aren’t daunted by them like the first group.

Interestingly, by the time a company has entered the Mature phase, their problems aren’t really problems anymore. Companies in this group are making money with AI and are working on ways to further increase their profits.

How to move forward

A key insight from a joint BCG-MIT Sloan Management Review research project makes a compelling case for adopting AI to gain a competitive edge. This data shows the spread in profitability between top- and bottom-quartile companies has nearly doubled over the past 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 sense of urgency required. So how can companies get unstuck and proceed through those Evaluation and Maturity phases? It really requires a culture shift 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 respondents at AI high performers were 2.3X more likely to consider their C-suite leaders very effective. This same group was also more likely to say AI initiatives have an engaged and knowledgeable champion in the C-suite.

In Nancy Giordano’s new book Leadering, she delves into the future of company stewardship. The gist: There has to be a transition from leadership to leadering. Nancy — who also advises my company — defines the former as “a static, closed, hierarchical, organizational approach designed to scale efficiently for consistent, short-term growth.” She goes on the say the latter differs as it “cultivates a dynamic, adaptive, caring, inclusive mindset which supports continuous innovation for long-term, sustainable value.”

Once the concept of leadership is re-framed, it becomes easier to achieve what needs to be done to begin AI utilization (as it should be led from the top down). This includes:

Devising a plan for how AI will transform. It’s critical to have a vision for how AI will impact your business over the next three years. Consider how it’ll steer data acquisition, digital spend, and use case exploration in a practical manner that de-risks and accelerates the time to outcome. The BCG-MIT research found that companies with the right data, tech, and talent — but no strategy — only have a 21% chance of achieving significant benefits.

Allowing disparate teams to work together. A legacy business practice like siloing business units (and their data) to minimize risk is now a liability. A company that wants to succeed with AI needs to tear down those walls and empower a network of teams to explore new ways of working together. This will help improve agility and innovation.

Leaning into diversity. This isn’t just about making sure teams have a mix of genders and ethnicities. It’s also about inviting employees with different professional experiences. Companies that hope to thrive with AI should welcome a wide variety of perspectives. This means being open to dissent as well.

Rethinking how people interact with machines (and vice versa). BCG research shows when you create feedback loops, there’s a greater chance of success. To seize upon this, you’ll want AI learning from human feedback, humans learning from AI, and AI learning autonomously. Doing all three of these things gives a company a 53% chance of significant financial benefit (versus the 5% chance that comes from doing nothing).

Soldiering ahead with AI doesn’t just require a change in technology, it also demands a change in process, culture, and collaboration. Those that will prosper from AI are the ones investing in strong cultures and better communication structures.

Employees at AI high performers tend to agree. In McKinsey’s 2020 survey, 52% of these employees said their team leaders feel empowered to move AI initiatives forward in collaboration with peers across business units and functions. 42% also believe a strong, centralized coordination of AI initiatives should be balanced with close connectivity to business end users.

If you’re serious about using AI to gain and hold a market edge, ask your employees about the changes they’d like to see in how they’re led and how they interact. A feedback loop is just as crucial to success as the OODA loop. By institutionalizing both, you’ll be able to amass an advantage — or at least stop falling behind.

Steve Meier is a co-founder and Head of Growth at AI services firm KUNGFU.AI.


Welcome to the VentureBeat community!

DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation.

If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers.

You might even consider contributing an article of your own!

Read More From DataDecisionMakers