Why the smartest people in the room keep getting the future wrong, and what it means for your business.
Predicting the future is notoriously difficult.
In 1980, IBM’s senior leadership sat around a table and estimated they might sell 241,000 personal computers over five years. They thought so little of the PC’s potential that they didn’t bother developing their own operating system or processor. Instead, they handed the operating system to a young Bill Gates in Seattle and bought the chip from Intel. The actual number? Over 25 million. That single miscalculation, born of indifference to the product’s future, created two of the wealthiest companies in history and eventually forced IBM out of the PC business entirely.
In 1975, Kodak engineer Steve Sasson built the world’s first digital camera, a toaster-sized contraption that captured grainy black-and-white images at 0.01 megapixels. He demonstrated it to Kodak’s executives. Their response, as Sasson later recalled, was to ask why anyone would want to take a picture that way when there was nothing wrong with conventional photography. Kodak patented the technology but buried it to protect film sales. By 2012, Kodak had filed for bankruptcy. The technology they invented and ignored had destroyed them.
In 2000, Reed Hastings and Marc Randolph flew to Dallas to offer Blockbuster the chance to buy Netflix for $50 million. Blockbuster’s CEO, John Antioco, turned them down flat. Netflix was a money-losing startup with 300,000 subscribers mailing DVDs. Blockbuster had 9,000 stores, 60 million customers and $6 billion in annual revenue. Why would they pay $50 million for a company that didn’t even make a profit? A decade later, Netflix was worth $13 billion. Blockbuster was bankrupt. Today, Netflix has a market capitalisation north of $400 billion.
Three stories. Three patterns. Three lessons.
The pattern behind the failures
What connects IBM, Kodak and Blockbuster is not stupidity. These were smart, well-resourced companies led by experienced people. The problem was something more subtle, and psychologists have a name for it. Several names, in fact.
Status quo bias is the deeply ingrained human preference for keeping things as they are, even when change would clearly be beneficial. It is rooted in loss aversion: the pain of losing what you have feels roughly twice as powerful as the pleasure of gaining something new. When IBM’s executives looked at the personal computer, they were not weighing the opportunity objectively. They were unconsciously measuring it against what they might lose, their dominance in mainframes.
Then there is confirmation bias, the tendency to seek out, notice and remember information that supports what you already believe, while ignoring or downplaying evidence that contradicts it. Kodak’s leaders had decades of evidence that film was a brilliant business. Every quarterly report confirmed it. When Steve Sasson showed them a grainy digital image, they did not see a revolution. They saw a crude toy that confirmed their existing belief: there was nothing wrong with conventional photography.
And finally, there is what psychologists call the endowment effect, the tendency to overvalue what you already own simply because you own it. Blockbuster did not just have 9,000 stores. They were 9,000 stores. Their identity, their processes, their revenue model, their staff, their real estate, everything was built around physical retail. Abandoning that felt like losing something real. Investing in a money-losing DVD-by-mail startup felt speculative and abstract.
These biases do not operate in isolation. They reinforce each other. Status quo bias makes you resistant to change. Confirmation bias filters out the evidence that change is needed. The endowment effect makes your existing model feel more valuable than it is. Together, they create what McKinsey has described as “cognitive brakes”, a set of mental defaults that prevent even very capable leaders from seeing what is happening around them.
IBM’s leaders were mainframe people. They measured success in room-sized machines sold to Fortune 500 companies. A small box on a desk was a curiosity, not a strategy. Kodak’s executives were film people. Their entire business, from manufacturing to processing to printing, depended on chemistry. A digital image had no film, no processing, no prints. It didn’t fit. Blockbuster’s leadership were retail people. Their business was built on physical stores, shelf space and, crucially, late fees.
In each case, their expertise in the world as it was became a liability when the world changed. Not because they were foolish, but because the same mental habits that made them successful, pattern recognition, commitment to proven models, trust in experience, stopped them from seeing what was coming next.
Why this matters now
If you are a business owner in 2026, you are living through exactly this kind of moment. Artificial intelligence is not a future technology. It is here, it is working, and it is already changing the economics of how businesses operate.
Consider the scale of what is happening. Just as the shift from mainframes to personal computers transformed knowledge work, and the internet reshaped commerce and communication, AI is now resetting the assumptions that have governed how businesses are built and run for decades. The constraints that once defined business creation, team size, capital requirements, time to market, are all being rewritten.
A recent McKinsey study of hundreds of ventures founded between 2018 and 2024 found that businesses launched in the AI era are achieving higher output with faster timelines, on both a per-person and per-pound basis. A separate survey by early-stage venture capital firm Antler found that 93 percent of companies reported AI accelerated their execution, with nearly half citing speed increases of up to five times.
These are not incremental improvements. They represent a structural shift in what a small team can achieve. And the dividing line is becoming clear: those who treat AI as an add-on will capture incremental benefits at best. Those who build it into the foundations of how they operate, with human expertise and judgement at the centre, will pursue more ideas, validate them faster and scale winners earlier, often with fundamentally different economics.
What AI actually changes for a small business
Forget the hype about robots replacing people. The practical reality for a small business owner is more specific and more useful than that. AI creates value along three dimensions that matter directly to you.
It expands your capacity to explore and test ideas. What once required weeks of market research, customer interviews and concept testing can now be accomplished in hours. You can test more ideas, validate them faster and kill the weak ones earlier, before they consume time and cash.
It compresses your timelines. Tasks that used to take weeks, drafting proposals, analysing data, building marketing campaigns, researching competitors, can now be done in days or hours. One wealth management venture doubled its delivery speed by using AI to handle the preparation work while human experts focused on judgement and client relationships.
It transforms what a small team can produce. This is the one that matters most for businesses like yours. AI allows a handful of the right people to achieve what once required entire departments. A construction company that introduced AI to automate its lead generation saw outreach volume increase 25-fold. Click-through rates more than doubled compared with the previous manual process.
In many cases, aspiring to double productivity in venture output is no longer unrealistic.
The real question is not “should I use AI?” It is “how?”
The businesses that capture the most value from AI are not those that simply bolt it on to their existing processes. They are those that use it to ask better questions earlier, fail faster on weak ideas and concentrate resources on opportunities with genuine potential.
This distinction matters. Automating a bad process just gives you a faster bad process. The opportunity is to rethink how you work.
Three shifts stand out.
First, reset your expectations. If you are still thinking about AI as a way to save a few hours a week on admin, you are thinking too small. The question is: what could your business look like if your capacity to research, analyse, plan and communicate doubled? What decisions would you make differently? What opportunities would you pursue that currently feel too time-consuming to explore?
Second, build AI into your workflows, not around them. AI delivers its full impact only when it is embedded end to end, from product development and customer discovery through to go-to-market, operations and finance. That means every role in the business needs to be designed to work alongside AI rather than around it. Isolated use cases, a chatbot here, an automated email there, will not transform your business. Integrated deployment will.
In practice, this means redesigning how work actually flows through your business. Humans orchestrate, supervise and intervene. AI executes the research, the analysis and the coordination. The human brings judgement. The AI brings speed and scale. When you raise expectations uniformly across every function, the gains compound: faster validation enables quicker iteration, which accelerates the scaling of what works.
Crucially, the goal is not simply to do the same things faster. The businesses that capture the most value from AI are not those that merely automate existing processes. They are those that use AI to ask better questions earlier, fail faster on weak ideas and concentrate their resources on the opportunities with genuine product-market fit. That is a fundamentally different way of operating, and it is available to any business willing to think about its workflows with fresh eyes.
Third, encode what your best people know. This is where it gets interesting for advisory-led businesses. Through AI systems, the tacit knowledge embedded in experienced people can be extracted, structured and reused. The judgement of your best salesperson, your most experienced project manager, your sharpest financial analyst, can be captured and scaled. Not replaced. Scaled.
A global manufacturing company applied this approach when launching a new digital marketplace. They paired a senior executive with an AI team, mapped how pricing decisions were made and suppliers were evaluated, and translated that logic into AI-supported workflows. Decisions that once depended on the availability of a single leader could now be executed consistently and at scale.
The Kodak trap for small businesses
Here is where it gets personal.
Every one of the failure stories I opened with has a small-business equivalent. The accountant who refuses to move beyond spreadsheets because they have always worked. The retailer who dismisses online sales because their shop does fine. The consultant who won’t use AI because “my clients want the personal touch.”
They are not wrong about the value of what they have. They are wrong about the speed at which the alternative is improving.
Kodak’s executives were right that digital cameras were rubbish in 1975. They were wrong to assume they would stay that way. Blockbuster was right that DVDs by mail was an inferior experience in 2000. They were wrong to assume streaming would never arrive.
The lesson is not that you should panic and adopt every new tool that appears. The lesson is that you should be paying attention, experimenting deliberately and asking yourself one question on a regular basis: if a competitor half my size used AI to do what I do, but faster and cheaper, how would I respond?
If you do not have a good answer, that is your signal to act.
It is not just about how you work. It is about what you offer.
Most of the conversation about AI focuses on execution: doing things faster, cheaper, with fewer people. That matters. But there is a strategic question that is just as important and easier to miss.
Does your offer still reflect the world your clients are living in?
Their situation has changed. They are dealing with more complexity, more data, faster-moving markets and rising expectations. If you are still selling the same service, positioned the same way, at the same price, delivered at the same speed as you were two years ago, you are not standing still. You are falling behind. Because someone smaller and leaner is already using AI to deliver something similar, faster and cheaper.
This is not about slapping “AI-powered” onto your marketing. It is about asking hard questions. If AI halves your preparation time, does your pricing model still make sense? If a client can get a basic version of your service from a tool, what is the premium version that only you can deliver? If your competitors are using AI to respond to enquiries in minutes rather than days, what does that do to your conversion rate?
The businesses that will stand out in 2026 are not just using AI internally. They have updated what they sell and how they position it to reflect what their clients actually need right now.
Audit your business as a set of jobs to be done
Here is a practical starting point. Your business, whatever it does, is a collection of jobs to be done. Client onboarding. Proposal writing. Financial reporting. Lead generation. Follow-up communications. Quality checks. Scheduling. Each of these jobs has steps, and many of those steps do not require your judgement. They require your time.
The exercise is simple. List every recurring job in your business. For each one, ask: which steps are preparation, and which are judgement? AI handles preparation. You handle judgement. The gap between the two is where your leverage sits.
When clients go through this exercise, they typically find that 30 to 50 percent of the time spent on any given task is preparation that AI could handle. Client onboarding that consumed three hours of a founder’s time runs itself. Weekly KPI reports assemble themselves instead of someone updating a spreadsheet every Monday. First-draft proposals are ready for review before the meeting, not after it.
This is not about replacing people. It is about redirecting their attention from tasks that consume time to decisions that create value.
What to do next
You do not need to become a technology company. You do not need to understand how large language models work. You do need to understand what they can do for your specific business.
Start with the work that consumes your time but does not require your judgement. Data entry, first-draft documents, research, scheduling, routine communications. These are the tasks where AI delivers immediate returns with minimal risk.
Then move to the work that benefits from your judgement but is constrained by preparation time. Client analysis, market research, financial modelling, competitive intelligence. AI can do the preparation in a fraction of the time, freeing you to focus on interpretation and decision-making.
Finally, think about what your business would look like if you could run twice as many experiments with the same resources. More marketing tests. More product ideas explored. More customer segments investigated. More small bets placed, with weak ones killed early and winners backed hard.
McKinsey’s research confirms the payoff: 67 percent of companies that prioritise business building outgrow the market, and each pound of new-venture revenue creates roughly twice the enterprise value of a pound generated in the core business.
The cost of experimentation has collapsed. The question is whether you will use that to your advantage, or whether you will be the one future business writers use as a cautionary tale.
The one prediction I will make
I am not going to tell you what the world will look like in five years. Nobody knows. IBM didn’t know. Kodak didn’t know. Blockbuster didn’t know. Google’s own VP of Ads and Commerce, writing in February 2026, describes this as “an expansionary moment” requiring “a new playbook” but is careful not to predict exactly where it leads.
What I will tell you is this: the businesses that thrive will not be those that predicted the future correctly. They will be those that built the capacity to adapt when the future arrived.
That means staying curious. Testing. Learning. Adjusting. Treating your business not as a fixed structure but as an ongoing experiment.
The smartest people at IBM, Kodak and Blockbuster all had one thing in common. They were certain they understood what was coming. They were wrong.
The safest position in a world of uncertainty is not certainty. It is readiness.
Sources and Further Reading
McKinsey & Company, ‘How to build businesses faster and better with AI,’ March 2026.
McKinsey & Company, ‘Overcoming status quo bias in the age of transformation,’ October 2025.
Boca Raton Historical Society, IBM PC history.
Long Now Foundation, ‘Scenario Planning for the Long Term’ (IBM internal forecast slide).
Snopes / PetaPixel / National Inventors Hall of Fame, Steve Sasson and the Kodak digital camera.
Marc Randolph, That Will Never Work: The Birth of Netflix and the Amazing Life of an Idea (2019).
Google / Think with Google, ‘Digital advertising trends for 2026,’ February 2026.
About the Author
Noel Guilford FCA is a chartered accountant and business adviser who works with a small number of deeply engaged business owners through a structured Virtual Board advisory model. He is the founder of Guilford Accounting and writes on practice design, advisory strategy, and the intersection of AI with professional judgement.
To discuss how this approach might work for your business, book a discovery call at calendly.com/noelguilford
