How to become a “Digital Maverick”

“No one ever got fired for choosing IBM.”

Once considered a safe bet for capital projects, purchasing services from IBM offered large enterprises a certain guarantee of results and peace of mind for complex, multi-year initiatives. This thinking was acceptable when business reinvention was measured in years and decades. However, in today’s new era of hyper-digitalization and generative AI, maintaining this long-standing business cliché carries increasing, compounding risk. Buying “safe” often comes at the cost of speedy innovation and long-term differentiation, where asset-heavy industry has much to learn and where a new class of business leaders recalibrates the risk vs. reward of these strategic technology investments.

As Gartner puts it in their Maverick predictions series: “Best practices don’t last forever — you need to look toward next practices, too. Challenge your thinking by considering less-obvious developments…1” If leading industrial companies prioritize their long-standing business playbooks in this new era of AI, they risk missing their “iPhone” moment, where making certain bets in digitization (at the right time) pay off in an outsized way, ushering in tremendous simplicity, scale, and a clear, valuable path to autonomous operations.

There is no more apparent opportunity to implement this strategy than by applying emerging innovations in generational AI technology such as ChatGPT and GPT-4. Generative AI possesses the potential to fundamentally reshape how knowledge workers and subject matter experts interact with data and operational processes. In fact, Gartner estimates that “by 2030, 75% of operational decisions will be made within an AI-enabled application or process2.” Connecting the dots between the business and the technology, creating the opportunity, and driving the required change management takes a clear vision and a strong immunity and resilience to failure.

Industrial organizations can no longer afford not to take risks. But which heroes are central to business transformation and are staking their careers (and driving meaningful change) based on the truth that digitalization across industry remains deficient and that a smarter, more autonomous industrial future requires a new playbook?

www.gartner.com/en/podcasts/thinkcast/behind-the-research-maverick-research
Gartner Report: Maverick Research: Data and Analytics Roles Will No Longer Be a Priority

The Rise of the “Digital Maverick”

Characterized by strategic big-picture thinking, relentless determination, strong business understanding, technical prowess, and sometimes a chip on their shoulder, the Digital Maverick is a new breed of industrial leader that is critical for taking an industrial digital transformation program and making it truly meaningful and valuable with the advent of industrial AI.

While other members of digital operations teams think about “deploying operational use cases and analytics,” the digital maverick is thinking about “building new long-term business capabilities.” Instead of searching for keywords like: “what is an industrial chatbot,” “good data management platforms,” or “digital operations dashboards,” the Digital Maverick craves and drives clarity on new ideas such as “ChatGPT for operations,” “retrieval augmented generation” “AI and contextualized data,” and “autonomous digital factory.”

Related: RAG is all the RAGE →

They also know from experience what has not worked well enough to deliver meaningful value from previous attempts at industrial AI—either from seeing first-hand more noise than value (unreliable models, too many false positives, constant retraining), or outright failure of AI deployments to create meaningful change in operator workflows. The Digital Maverick knows there’s a solution to the age-old industrial challenge of operating complex environments with limited insights and sub-optimal decision-making.

Digital Mavericks: This is your moment

Everything these digital mavericks have been implementing with incremental gains to-date (Industrial DataOps infrastructure, skillset investments, and other foundational capabilities) have become 10X more valuable almost overnight as industry adopts high-trust, secure, hallucination-free generative AI to harvest more business value from digitaloperations.

The inflection point could not be more real, obvious, or urgent from both a technology and strategic perspective. True digital mavericks know that the game has been changed forever and that they carry a new responsibility to drive and govern a new playbook that includes a new set of guidance:

1. Realign digital KPIs and skill sets to key business drivers

Digital Mavericks know that their organization’s charter and KPIs must be ever more tied to business value and operational gains. Instead of deploying proofs of concepts, their KPIs must reflect business impact, successful scaling, and other product-like metrics:

How can business value be measured, quantified, and directly attributed to the digital initiative? What 'portfolio' or 'menu' of value is being developed?
How is adoption measured and user feedback implemented in the feedback loop? How many daily active operations users are actually in the tools delivered? How is the workflow changing as a result?
How much do these business applications and solutions cost to deploy and maintain? What do they cost to scale to the following asset, site, or plant?
What new business capabilities for multiple stakeholders are gained due to deploying a solution? Are these capabilities short-term, or will they persist (and drive value) over the long term?

Additionally, the portfolio of skills needed to develop and run analytics departments is also positioned to change. Gartner estimates that “by 2030, the number of traditional descriptive analytics dashboards will decrease by more than 50% in most modern digital businesses3.” Generative AI will have successfully abstracted away the traditional complexity of creating and managing specialized analytics in favor of more business-ready insights.

The clear message to legacy CIOs and want-to-be mavericks? Stop investing in IT and data and analytics skills and focus more on connecting AI-powered use cases to business impact.

3. Gartner Report: Maverick Research: Data and Analytics Roles Will No Longer Be a Priority

2. Land & expand with business value faster than the competition

Technology can change in an instant, but operational KPIs are evergreen. Tackling digital transformation holistically across the enterprise has yet to prove to move the needle fast enough to deliver a meaningful competitive edge in dynamic markets.Instead, digital mavericks are pursuing “land and expand” approaches that integrate new technology in the context of high-value use cases, with clear pathways from initial test concepts into scaled (and 10x more valuable) deployments.

Whether the team is focused on driving down OPEX, increasing production capacity from shorter turnarounds (TARs), or eliminating waste from a production process, think beyond siloed processes. Can this new use case be deployed across sites? How valuable is it compared to other business requests? What is the cost and effort to scale?

Here, the digital maverick puts a premium on flexible technology, approaches, and teams so that they can be prepared to shift strategy- either to take advantage of market opportunities or new technology like Generative AI- at a moment's notice. For example, GPT appeared in a decent state of maturity within just a few quarterly business cycles, disrupting product roadmaps and, just like the pre-internet days, sending the world to a new level that will never come back. Organizations equipped to mobilize and execute on these trends can capitalize on market opportunities much faster than their competitors.

3. Challenge traditional thinking around DIY (do-it-yourself) projects and technology

Digital mavericks have always been tempted to go down the DIY path. Still, more have started to realize the significant opportunity costs that come with this approach's strategy, implementation, and change management - especially when it comes to time. Is your largest competitor looking for off-the-shelf Generative AI components for their digital transformation while you invest resources in more cloud-based building blocks?

Instead of making a name based on “completeness and sophistication of tech stack,” consider building a reputation based on “time to scaled value,” a far more critical metric. Here are a few other long-standing myths that digital mavericks are starting to challenge:

They recognize that DIY is not usually the least expensive option when considering headcount and long-term total cost of ownership of infrastructure and deployed solutions.
They are wary of going all-in on one cloud provider's capabilities, knowing that the only way to avoid vendor lock-in is by investing in multi-cloud ecosystems that include DIY and SaaS.
They understand that differentiation in technical and go-to-market abilities is a function of speed and agility, not having a potentially cumbersome, custom home-grown data platform.
They know that SaaS is also a valid enterprise-grade path and appreciate that mass-market software spreads development risk and comes with self-service documentation, support SLAs, and other entitlements that minimize costs.
They have seen that job security and department prestige is a product of delivering actual value in operational workflows, not being seen as a high-headcount PoC factory with little quantifiable value to show.
They acknowledge that their industrial company will never be in the software business and that, to be true innovators, they can't repeat pre-cloud, pre-SaaS-era bespoke software development practices.