AI revolution vs digital revolution: déjà vu
Every so often, a new technology arrives that changes not just how we work, but how the world itself operates.
Today, we’re living through one of those moments: the AI revolution. AI is being hailed as the most transformative technology since the internet, and in many ways, that’s true. But for those of us who worked in the technology sector during the 1990s, there’s something deeply familiar about it.
Because we’ve been here before.
The digital revolution of the 1990s reshaped industries, created new giants, and left others behind. And as I watch organisations wrestle with AI today, I see the same opportunities—and the same risks—playing out all over again.
The good news? If we learn from the digital revolution, we can accelerate adoption, avoid the mistakes of the past, and build sustainable value in the AI era.
Of course, there are many differences. But that doesn't mean we can't learn from the similarities.
9 lessons for the AI revolution from the digital revolution
Here are 9 lessons for the AI revolution from the digital revolution:
Clean data is essential for AI success
Back in the 90s, digitisation was all about moving from paper-based to digital records. Companies rushed to implement ERPs, CRMs, and data warehouses. But many discovered that poor-quality data undermined the promise of these systems.
The same is true for AI today. Generative AI and predictive models don’t just need lots of data—they need clean, well-structured, and accessible data. Without governance, integration, and quality assurance, AI won’t deliver reliable results.
π Lesson: Getting your data estate in order isn’t optional—it’s the prerequisite to success.
Process redesign is key to AI transformation
In the digital revolution, many firms started by simply digitising existing processes. They scanned documents instead of rethinking workflows, or put PDFs online instead of building real e-commerce platforms.
The breakthrough came when businesses redesigned around digital possibilities:
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Dell built its direct-to-consumer, build-to-order supply chain.
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Amazon pioneered one-click ordering and dynamic inventory systems.
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Netflix shifted from posting DVDs to streaming on demand.
AI requires the same mindset. Simply layering AI onto existing workflows is a missed opportunity. The real gains come from re-engineering decision flows, customer interactions, and business models around what AI enables.
π Lesson: Don’t just add AI—redesign with AI at the centre.
AI strategy must follow business strategy
During the 90s, too many companies started with: “We need an ERP” or “We need a website.” That technology-first approach often led to cost overruns and disappointment.
By contrast, those who began with a clear business strategy—whether to reduce costs, increase agility, or deepen customer relationships—were the ones who used digital to create real competitive advantage.
Today, I see companies rushing to “do something with AI” without first asking:
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What is our business strategy?
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Where do we want to grow?
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What risks must we mitigate?
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How can AI serve those goals?
π Lesson: AI is an enabler, not a strategy. Start with business goals, then develop a technology strategy to support them.
Not every AI use case is worth pursuing
In the digital era, some companies digitised everything simply because they could. But not all digital initiatives paid off commercially.
AI presents the same risk. Just because you can apply AI to a process doesn’t mean it creates value. For example:
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Automating low-value tasks with AI may cost more than it saves.
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Generating marketing content at scale might dilute brand voice if not carefully managed.
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Deploying AI in sensitive areas without strong oversight may increase compliance risks.
π Lesson: Apply a commercial lens to every AI investment. The goal is return on investment, not novelty.
AI adoption lessons from the dot-com boom
The dot-com boom of the late 1990s promised the world—and then the bubble burst. Yet, from the wreckage emerged enduring giants: Amazon, Google, Salesforce.
The same pattern is already visible in AI. Today’s explosion of AI startups and tools will narrow down to a smaller number of dominant platforms and sustainable use cases.
π Lesson: Don’t chase every shiny new AI tool. Focus on those with a viable ecosystem and long-term potential.
AI success depends on culture and skills
In the 90s, one of the biggest challenges wasn’t the technology itself—it was helping employees adapt. Entire workforces had to learn digital literacy, adopt new systems, and rethink workflows. Resistance to change often held organisations back.
Today, AI demands a new set of skills:
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AI literacy (understanding what AI can and can’t do).
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Critical thinking (assessing AI outputs for accuracy and bias).
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New workflows (integrating human and AI contributions effectively).
π Lesson: Successful adoption depends on people, not just machines. Invest in training, change management, and culture.
AI regulation and governance are inevitable
During the digital revolution, regulation lagged behind technology. It wasn’t until later that frameworks like GDPR, Sarbanes-Oxley, and cybersecurity standards reshaped how firms operated.
AI is following the same trajectory. Regulation is still emerging—the EU AI Act, the White House AI Bill of Rights, and sector-specific guidelines are just the beginning.
π Lesson: Expect regulation to tighten. Build governance into your AI strategy now, before you’re forced to.
Winner-takes-most dynamics in AI platforms
Digital platforms created near-monopolies. Microsoft dominated operating systems, Google search, Amazon e-commerce, Facebook social networking. These ecosystems became self-reinforcing, making it hard for latecomers to compete.
AI is showing the same tendency. A handful of foundation model providers are likely to dominate, and companies that embed AI deeply into their workflows will build moats that are hard to cross.
π Lesson: Be deliberate about which ecosystems you join—and move early enough to secure your place.
AI-native startups will disrupt incumbents
One of the great stories of the 90s was how startups born “digital-native” displaced incumbents: Netflix beat Blockbuster, Amazon overtook bookstores, Uber reshaped taxis.
The AI revolution will see the same. Startups built on AI from the ground up will outmanoeuvre traditional firms that only adopt it incrementally.
π Lesson: Incumbents must act with urgency. Waiting too long risks being outflanked by more agile competitors.
Becoming AI-native vs adding AI tools
The digital revolution forced organisations to ask:
π How do we become digital businesses, not just businesses with digital tools?
The AI revolution forces a similar question:
π How do we become AI-native organisations, not just businesses with AI add-ons?
The answer will define who thrives in the next decade—and who fades away.
Why I can help your organisation succeed with AI
I’ve lived through both revolutions.
In the 1990s, I was in the thick of the digital transformation—seeing both the successes and the costly mistakes. Today, I’ve carried those lessons into the AI revolution, integrating generative AI into StratNav to help organisations develop and execute strategy more effectively.
That combination of hands-on AI expertise and experience from the last great technological shift is rare. And it’s exactly what organisations need now: someone who can help you cut through the hype, align AI with strategy, and design processes that deliver real results.
Book a call: Let’s shape your AI strategy together
If you’re grappling with how to harness AI in your organisation, I can help you:
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Align AI initiatives with business strategy.
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Prioritise use cases that deliver commercial value.
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Build the culture and governance needed for success.
π Book a call with me today: https://calendly.com/chriscfox/discuss-your-needs
The AI revolution is here. The question is whether your organisation will repeat the mistakes of the digital revolution—or seize the opportunity to lead.
