AiThority Interview with Mike Bechtel, Chief Futurist at Deloitte Consulting

Hi Mike, please tell us about your role at Deloitte and how you arrived here?

I serve as Deloitte Consulting’s chief futurist. My team and I look at what’s new and next in tech. By understanding technology trajectories from past to present, we work to project plausible futures. Our goal is to help our clients avoid headwinds, harness tailwinds, and, with any luck, arrive at their preferred tomorrows just a little ahead of their regularly scheduled time. From there, we combine our unique combination of business and technology expertise to help them engineer their tech-forward future.

How did I arrive here? I was an inventor turned investor. I started my career in technology R&D at another global professional services firm where my team and I invented emerging technology solutions to wicked enterprise problems. Gradually, it occurred to me that startups were the ones most likely to build the future, so I co-founded and led a venture capital firm focused on investing in matches between startups and enterprises.

It’s been two years since the onset of the COVID pandemic. As a technology leader, how would rate the performance of the countries in fighting the pandemic with positive outcomes? Could we have all done better with suitable utilization of technologies and resources?

While COVID-19 presented enormous challenges, we saw a tremendous response from many of our clients who became more resilient by challenging orthodoxies and expediting technology transformations from years to months – in some cases even weeks. For instance, cloud-native data management and data sharing technologies really helped some of our clients tackle obstacles posed by the pandemic. In our Deloitte Tech Trends 2022 “Data Sharing Made Easy” trend, we saw how CVS was able to share (and receive) data with government agencies, suppliers, and non-traditional collaborators as part of Operation Warp Speed. For years, folks have talked about data being “the new oil”, but only recently, thanks to emerging cloud-native data platforms like (but not limited to) Snowflake and Databricks, is that data able to flow freely enough become an easily tradeable commodity.

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A critical challenge to adoption of AI across enterprise:

Historically, executives become executives because they’re proactive and decisive. The adoption of AI requires a certain degree of learning to let go of both inclinations. Even the most aligned and well-intentioned leaders sometimes struggle to defer ego and instinct to data-driven insights.

Should government start regulating the AI applications and the work that goes into these innovations? Would this solve the issues related to compliance and AI ethics?

Governance is absolutely critical in the continued evolution of AI. Our recent Future of AI report speaks to a framework we call “The 4 P’s”. Specifically, AI advancements ought to be weighed against their impacts to our principles, our privacy, our professions, and our policies. No regulatory maneuver stands to be a cure-all, but take AI ethics, for example. By elucidating our tacit social and ethical values–making them explicitly articulated in the way we articulate financial metrics–we can move towards training AI’s to optimize for a balance of social, ethical, and financial outcomes. More holistic AI starts, in part, with more holistic training data.

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Could you tell us how you assess the performance of AI and data science capabilities in the last two decades? What changed during the pandemic for the AI market?

It’s important to recognize that AI is not new. Larry Tessler had a great quote, that “AI is whatever computers can’t do *yet*”. 25 years ago, an AI couldn’t beat Gary Kasparov in chess. Until it could. 15 years ago, it couldn’t beat Ken Jennings in Jeopardy, and 5 years ago, Lee Sedol in Go. We doubt the impending advances in AI until they occur, at which time we dismiss them as still nowhere near human intelligence. This, because of our unique pride-of-place: We aspire to create amazing things, but not so amazing as ourselves. The point being, there’s always a “next” in AI, and big advancements that have happened in concert with the pandemic are the increasing adoption of cognitive automation capabilities. Five years ago, many enterprise AI’s were, in fact, predictive analytic capabilities riding “out-of-band” with critical-path business processes. They were able to offer insights and/or critiques to human decision makers, but the timelines were such that they showed up more as Monday morning quarterback, telling you what you might have done differently. Intra, and post-pandemic enterprise AI’s, thanks to: 1. advances in deep learning algos, 2. availability of training data and cloud compute, and it must be said, 3. business decision maker understanding and acceptance, are beginning to graduate from critic to cast member. Fast enough, effective enough, and affordable enough to be deployed into scenarios where they not just critique human decision makers, but augment them, or in a few cases, replace them entirely.

What are your predictions for the AI-based marketplaces in 2022? Which industries and business verticals within the organization are benefiting the most from AI’s adoption?

We think about it as two macro-forces. On the accelerator pedal side, the availability of data, compute, and skills propels AI advancement. Financial services, technology, media and telecommunications, and some product/retail organizations tend to have all three of these accelerants in spades. On the flipside, governmental and industry regulation acts as an understandable brake. Healthcare, public services, resources, and financial services sectors face more of these brakes than most. Given these headwinds and tailwinds, I’d expect to see the leading edge innovations continuing to arise from technology, media and telecommunications and retail players.

Tell us more about your vision for advanced AI applications, especially in the healthcare and life sciences.

AI excels wherever there’s sufficient information and computation to train it. There’s been an explosion of data in the healthcare and life sciences sector over the past 10 years as clinicians of all types are beginning to acknowledge that machine intelligence isn’t out to displace their expertise so much as to augment it. We’re seeing compelling advances in computer vision which are greatly accelerating radiology and diagnostic work, and we expect to see all manner of precision medicine advances given the increasing acceptance of our genomics as a clinical input.

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Any advice to every young AI developer / data scientists looking to build a career in this space:

Imagine, experiment, and tinker. For generations, computer science, and STEM generally, has been a place for linear thinkers incented to code error-free calculations. Applied AI isn’t about telling computers what to do. It’s about teaching them. Teachers, in turn, are most effective when they bring lived experience, analogies, and creativity into the classroom. And so it goes with AI. To teach our digital children well, tomorrow’s AI developers will find that their ability to reframe problems in creative ways will be paramount to their success. In short: In all your efforts to master languages, platforms, and logic models, don’t forget to live a little. It’s the diversity of your experiences that will, in part, inform your differentiated success.

Thank you, Mike! That was fun and we hope to see you back on AiThority.com soon.

[To share your insights with us, please write to sghosh@martechseries.com]