The Adoption Curve is Compressing – And You’re on the Clock
The promise of emerging technologies reshaping businesses is not new. The adoption framework popularized in the 1990’s by Geoffrey Moore’s book “Crossing the Chasm” grouped businesses into Innovators, Early Adopters, Early Majority, Late Majority and Laggards. Considering some of the major technology introductions of the last 50 years – personal computers, cell phones, the internet, WiFi and smart phones – studies show that each one took between 6 and 20 years to reach over 50% usage. What we see as ubiquitous today was not always so. Artificial intelligence (AI) is compressing these timeframes and with that, putting increasing pressure on businesses to find effective routes to adoption and impact.
The combination of the speed of adoption and the overall penetration level (number of active users and applications) are primary determinants of successful outcomes. Numerous studies have shown that there can be significant competitive and financial advantages to be gained from early adoption. Properly applied, firms can re-engineer their processes and upskill or re-skill their employees to drive increases in productivity, faster time-to-market and measurable cost reductions through digital innovation. First-mover advantages can attract new customers, help retain top quality employees and create higher product profitability. Laggards tend to resign themselves to competing on cost or ‘milking their customer base’, ending up in a race to the bottom and, ultimately, obscurity.
AI is the latest phenomenon and it is already changing the game like nothing that has come before. It’s the first technology that is not just a tool but actually has the potential to outthink us and to improve itself. AI algorithms and models are actively used to design and build the next generation of themselves. Autonomous, recursive self-improvement is not there yet but it’s on the near horizon. Second, the magnitude of investment across government, industry, private equity and capital markets is absolutely staggering and unprecedented. Unicorns with billion dollar valuations pale in comparison to companies like OpenAI and Anthropic, which are already valued in the hundreds of billions. And looking beyond the financials, we see a concentration of top scientific and engineering talent being pulled into serving corporate ambitions that exceed anything in human history. Finally, it’s not just a commercial story. The race for supremacy amongst nations, especially the USA and China, has never been so necessary or so urgent, given its ability to shape the future.
And lest we forget, while the potential for good is immeasurable, AI has a dark side and we are totally unprepared for it. More on that in a future instalment.
From Curiosity to Competitive Pressure
When ChatGPT launched in late 2022, it was novel and sparked our curiosity. Immediately the internet was filled with courses and videos on how to get the most from it, with a huge focus on learning prompt engineering. Following quickly, software and SaaS companies began proclaiming the inclusion of AI-enabled features in products ranging from marketing platforms to call center systems and analytical tools. For the most part, when you pulled back the covers, the actual capabilities of these early efforts were underwhelming. Fast forward to today, just 3 years later. The combination of multiple, highly available, reasoning large language models (LLMs) – also referred to as foundational models - improvements to natural language processing (NLP) and the introduction of agentic AI using sophisticated APIs and workflows, have started to make AI indispensable and hugely productive across a variety of industries and occupations.
Strategic Inertia versus Strategic Adaptation
As expected, AI benefits are not spread evenly across industries or segments. Small and Medium-sized Businesses (SMBs) struggle with AI adoption for a variety of reasons: limited in-house expertise, security and privacy concerns, unclear costs and unproven ROI models. Strategic inertia describes a state of ‘wait and see’ that, if not addressed, leads businesses to become laggards. For SMB owners, getting up every day and wondering “What should I be doing about AI?” and then doing nothing, is still the norm. Most simply don’t know where to start. Waiting to act feels safe, but failure to act can be costly and the “fear of missing out” (FOMO) is strong. Conversely, diving in without well-defined objectives, proper planning and appropriate guardrails is a risk many SMBs can’t afford to take.
Strategic adaptation involves recognizing and embracing opportunities for understanding and responding to external influences like changing market conditions, innovations and competitive pressures. While AI could be considered just another external influence, it’s one that is impossible to ignore. As SMBs contemplate the right entry point, context is everything. If there are parts of the business that are underperforming or seen as low value but necessary, they could be targets for the introduction of AI. Identifying these opportunities for improvement are a good place to start. Success will depend on the willingness of the management team to begin the learning process and to disrupt the status quo.
Questions Every Business Owner Should Ask Themselves
One of my favorite quotes comes from Morris Chang, the former CEO of Taiwan Semiconductor Manufacturing – the world’s largest chip maker. “Without strategy, execution is aimless. Without execution, strategy is useless.” AI adoption needs to be a strategic imperative, integrated into daily routines and decision-making. It also needs to be a tireless, tactical assistant that facilitates continuous improvement in everything you and your people do.
The use cases for AI are nearly limitless, even at this early stage. As you consider next steps, here are the questions you should ask yourself.
What are our most pressing business problems today? Is there a place for AI to help us solve them?
How do we get started in a way that limits our risk but provides enough upside to justify the investment? How can we accurately measure results?
How will this affect our people? What do we need to do to ensure successful adoption?
This is only the starting point.
In part two, I’ll review the currently available foundational models, clarify some of their differences, and highlight practical use cases where businesses are already seeing measurable returns.
In part three, I’ll propose a design-led readiness process that will allow you to create an action plan that will avoid the strategic inertia outlined earlier.
I hope you’ll join me for the rest of the journey. Please provide your feedback. I’m a strong believer in hearing what others have to say, as a path to self-improvement. Thanks for reading.
© 2026 by Roy Gowler. All rights reserved.
This article was originally published in November 2025 and posted on Medium.com. As its author, I have updated it and posted it to my own website to increase visibility and reach.
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