The Enterprise AI Adoption Cycle: Understanding YOUR Journey

TL;DR: AI is like that fancy new coffee machine you bought. You want to impress your friends with a latte, but the instructions are too mathematical and complex for an earth-bound being, and you end up with a burnt mess. Well, I'm here to be your AI barista! I've been through the adoption cycle, and let me tell you, it's a wild caffeine-fueled ride. So, grab your mug, and let's brew some AI reality!

My Take on the Current State of Enterprise AI Adoption

Artificial Intelligence (AI) is everywhere these days. But getting it to work inside a company? That's a different story. I've been evaluating and educating on AI for a while now, and it's fascinating to see how it's evolving, and how SLOW (in reality) it has been to be adopted top-down in an organization. Investments have been measured in thousands not millions for everyone but the outliers.

One thing I've noticed is that there's an obvious and consistently recognizable cycle to how any innovation gets adopted in the enterprise, and AI is no exception.

As evidence during my frequent talks on the subject, I often refer to the latest from Gartner including two key perspectives: 1) AI-tools have regressed back into the trough of disillusionment and 2) there are two AI races, the providers racing to build toolsets/models for consumer and/or outlier patterns and in parallel enterprise adoption with positive investment return; the recent Google and AI “12 days of December” showcases the first race.

Let me know if you recognize these stages in any IT innovation in the last several decades: Cloud, Analytics, Data Platforms, Blockchain, etc

The Technology Introduction Cycle, AI as a lens.

• Original Invention: This phase, often spanning decades, is driven by scientific and engineering breakthroughs. Silicon to chip, DNA to storage, mathematics to model. It starts with engineering and science. Key advancements in machine learning, neural networks, and data processing lay the foundation for AI's potential.
• Experimental Application: The potential of AI becomes apparent, but practical applications and full implications remain unclear. Early adopters (enterprises with spare R&D cash) experiment with AI in specific use cases, while others observe with cautious optimism.
• Compelling Event: A major breakthrough, like a successful consumer AI product (Chat GPT) or a groundbreaking research project, the first non-governmental rocket launch, sparks widespread interest and investment. This event often catapults AI from research labs into the public consciousness.
• Media Hype: Media attention explodes, and AI is hailed as a revolutionary force. Predictions of AI's transformative impact abound, sometimes exceeding realistic expectations. Every blog you read, every press release picked up, and every updated website focused on AI is exponential
• Business Model Evolution: Existing business models from technology vendors are disrupted as AI-driven solutions emerge. New companies and products challenge traditional industries, forcing incumbents to adapt or risk obsolescence.
• Space Race: Three fronts: 1) Competition intensifies as multiple players vie for AI dominance. Tech giants and startups invest heavily in AI research and development, leading to rapid advancements and market consolidation. 2) Enterprise giants (telco, financial services, insurance) invest their own time, and build their own practice while simultaneously investing in marketplace technologies. 3) The Private Equity and Venture Capital Investment thesis changes from the last invention to this invention.
• Risk Identification: Concerns about AI's ethical implications, security vulnerabilities, and potential for unintended consequences surface. Governments, organizations, and researchers grapple with developing responsible AI governance frameworks. Multiple frameworks and regulatory boundaries are created, none of them enforced out of the gate. The bad news is highlighted and mass awareness becomes the norm: bad actors, security flaws, governance flaws, too many or too few standards, inappropriate deployments, unmitigated production use cases
• Enterprise Lags: While the AI space race accelerates, enterprises struggle to keep up even though the demands from the Board to the Administrative staff accelerates. Legacy systems, risk aversion, and lack of AI expertise hinder adoption, leaving many businesses behind the curve. It's difficult to learn the language, acquire the skillset, and invest in innovation when there is PLENTY to do in IT already. Enterprise IT is left to its own devices, with spartan interest usually at the individual contributor level. The net result is adoption/enablement is slow.
• Innovation Accelerates: In the meantime, the space race gets FASTER. Winners get decided, but change places monthly. Innovations/features/releases happen more frequently, often before the adoption of the previous feature is solidified. Each innovation solves more complex and more niche use cases. Arguably new use cases are “invented” as a precursor to the new feature. The speed of innovation and the race to be in the top right corner of the MQ ensures that new unknown risks are created, and will get discovered as it’s attempted to be released.
• Adoption by Regular Order: Actual enterprise technology adoption remains the same, cycling from innovators to early adopters to early majority to late majority to laggards. Corporate Innovation requires a budget. POC budgets can be allocated quarterly, but production budgets require full business cases on BUILD and RUN, and happen in annual planning increments. Skillsets are acquired, policies are drafted, and technology is tested and deployed. As usual. Technology Invention can not escape the regular order of adoption.

Understanding the phases of the technology introduction cycle is crucial, but it also highlights the unique challenges that enterprises face. While the AI space race unfolds at a rapid pace, with new innovations and disruptions emerging constantly, enterprises often find themselves grappling with legacy systems, risk aversion, and a lack of AI expertise. This creates a significant dilemma: how to navigate the complexities of AI adoption and harness its transformative potential without falling behind or succumbing to the risks.

The Enterprise Dilemma: Navigating the AI Adoption Maze

Enterprises and their leaders often find themselves adrift in the tumultuous sea of AI advancements. The breakneck speed of innovation, coupled with the hype and uncertainty surrounding AI, can leave them feeling overwhelmed and unsure of how to proceed. They need support, both in expertise and experience, to navigate this complex landscape and make informed decisions about AI adoption.

External support can help organizations develop a clear AI strategy, identify relevant use cases, select appropriate technologies, implement AI solutions effectively, and manage the associated risks. By partnering with external experts, organizations can leverage their knowledge and experience to accelerate their AI adoption journey, avoid costly mistakes, and achieve their desired business outcomes.

These engagements encompass several key areas:

• Understanding the AI Landscape: Enterprises need to grasp the intricacies of the AI adoption cycle, recognizing the different phases, potential pitfalls, and opportunities. This understanding helps them anticipate challenges and make informed decisions about when and how to invest in AI.
• Appreciating the Potential and Limitations: AI is not a magic bullet. Enterprises need to appreciate both its potential to transform their operations and its limitations. This realistic assessment helps them set achievable goals and avoid costly missteps.
• Managing Expectations and Risks: The hype surrounding AI can lead to unrealistic expectations and a fear of missing out. Enterprises need support to manage these expectations, assess risks objectively, and develop strategies that align with their business goals and risk tolerance.
• Prioritizing AI Applications: With numerous potential AI applications, enterprises need to prioritize those that offer the greatest potential for value and align with their strategic objectives. This prioritization ensures that AI investments are focused on areas that deliver tangible business benefits.
• Building AI Capabilities: Successful AI adoption requires the right skills and tools. Enterprises need support to develop their AI expertise, whether through internal training, hiring, or partnerships with external experts. They also need to invest in the necessary AI infrastructure and tools.
• Implementing, Evaluating, and Managing AI Solutions: AI adoption is an ongoing process. Enterprises need support to implement AI solutions effectively, evaluate their performance, and manage them over time. This includes monitoring for emerging risks, adapting to changing business needs, and continuously optimizing AI systems for maximum value.

The Good News

The good news is that enterprises don't have to navigate this cycle alone. Experienced partners can provide invaluable support and guidance. By collaborating with experts who understand the AI landscape and have a proven track record of successful AI implementations, enterprises can accelerate their AI journey and avoid common pitfalls. These partners can offer tailored solutions, from strategy development and technology selection to implementation, training, and ongoing support.

Conclusion

The Enterprise AI Adoption Cycle is a complex and ever-evolving landscape. However, by understanding the cycle's stages, recognizing the associated challenges, and proactively seeking expert guidance, enterprises can position themselves for success. Embracing AI is not just about staying ahead of the curve; it's about harnessing the power of this transformative technology to drive innovation, optimize operations, and unlock new business opportunities. With the right approach and support, enterprises can navigate the AI adoption cycle with confidence and achieve meaningful results.

Remember, AI adoption is like trying to make a latte art masterpiece with a shaky hand - it takes practice, patience, and maybe a few extra shots of espresso.