"If you're not climbing, you're sliding.
That's how Shopify's CEO Tobi Lutke framed it in an internal memo to employees, declaring that effective AI usage is no longer a competitive edge. It's the baseline. AI is now an expected skill, a default tool in every knowledge worker's toolbox. The shift from optional to expected is happening in real time.
It's a sentiment I've begun to hear echo across boardrooms, client strategy calls, and executive workshops. And yet, behind the noise and momentum, there's a growing risk many businesses aren't seeing.
The real danger isn't doing nothing. The real danger is doing something without a plan.
Outperforming the average in today's AI-flooded economy, what I once described as 'beating the bell curve' for the individual, won't come from buying a shiny platform or deploying a headline-grabbing tool. It will come from building the internal capability: the thinking, the frameworks, the architecture that allows your organisation to adapt as fast as the technology itself evolves. And especially in this space, change will be constant and far more rapid than we have seen in 20 years.
Falling Behind the Business Adoption Curve
In a previous article, I explored how individuals could outperform the average by combining their lived domain expertise with AI's leverage. But my thoughts are now evolving to what this means for business in a version of this very same predicament. What started as a conversation about individuals is now a high-stakes question for entire organisations.
There is a new curve at play, the business adoption curve, and the gap between early movers and hesitant followers is widening. Organisations that have already begun integrating AI into their systems and processes are accumulating compound advantage. Every test run, policy refinement, and use case validated today builds future momentum.
By contrast, those that have delayed adoption face a reverse compounding effect. It becomes harder to catch up. Capabilities expected by customers and employees begin to feel like technical debt. AI-native competitors operate faster, leaner, and with clearer confidence. What's normal for them starts to feel unattainable for you.
The result is an exponential gap in productivity, innovation, and talent retention. And for those still sitting on the sidelines, the longer the wait, the steeper the climb.
Why Tech-First Thinking is a Strategic Trap
In times of change, the instinct to “just do something” is strong. But in AI, that instinct often leads to the wrong kind of action.
Many organisations assume that buying into a single AI platform means they’ve made a strategic leap. But not all AI integrations are equal, and many introduce more rigidity than value.
At the rigid end of the spectrum are bespoke AI tools and wrappers. These are often built around a single model or function. While they can feel impressive in a demo, they’re brittle, hard to scale, and disconnected from broader business systems.
Next are existing SaaS platforms that have added AI features as an afterthought. CRMs, ERPs, and ticketing systems now offer predictive fields or summarisation, but these capabilities are typically vendor-driven and may not align with the workflows that actually matter to your business.
Then comes the most commonly adopted tool: ChatGPT. It offers wide-ranging utility, but even within the OpenAI ecosystem, different models perform better at different tasks. The user has limited control over which model is being used. While future iterations like GPT-5 will likely introduce more dynamic orchestration, today's experience remains constrained.
Microsoft Copilot has just offered a more integrated environment. Initially reliant on OpenAI models, it has recently expanded to include models from Meta and others, allowing for more flexible reasoning across use cases. This is a positive shift, but orchestration logic remains locked inside the Microsoft ecosystem.
Further along the spectrum are orchestration-layer platforms like Manus and Genspark.ai. These tools abstract model and tool selection entirely. Users engage with a single interface, while multiple models are used behind the scenes to complete more complex tasks. The experience is powerful, but users typically have little visibility or control over how orchestration decisions are made.
At the most flexible end of the spectrum are the integrators: tools like Workato, Boomi, Make.com, and Zapier to name a few. These platforms allow teams to define how systems, tools, and AI models interact. They enable true composability and give organisations control over how intelligence is embedded across workflows.
In reality, organisations often operate with a mix of layers across this spectrum. But where the centre of gravity sits determines whether AI becomes an asset or a liability.
This is the real risk: not just picking the wrong tool, but building around the wrong idea. AI is not something you install. It’s something you design into your operating model. It needs to evolve with your people, your systems, and your priorities.
The businesses gaining the most from AI today are not those moving the fastest. They are the ones building with flexibility, composability, and the clarity to avoid short-term decisions that lead to long-term constraints.
What Businesses Actually Need: Composable AI Frameworks
We've seen this pattern before. In cloud adoption. In marketing technology. In ERP transformations. The lesson is always the same: platforms don't solve problems, systems thinking does.
Here's what leading organisations are doing instead.
Architect for change
Design your stack to be model-agnostic. Prioritise interoperability and avoid long-term vendor lock-in. This doesn't just protect you, it sets you up to experiment and evolve without structural overhaul.
Embed AI literacy across the business
You don't need every employee to be a prompt engineer. But you do need everyone to understand where AI can augment their decisions, workflows, and outcomes. Skills enablement should be continuous, embedded, and role-relevant.
Build learning loops, not fixed projects
The most successful AI programs behave more like agile experiments than enterprise rollouts. Build in feedback, share learnings across teams, and make iteration your core capability.
Apply a governance mindset from day one
Ethics, security, and explainability must be part of every conversation, not bolt-on compliance later. Build frameworks for safe experimentation and responsible deployment.
Align AI with business value, not hype
Focus on real problems: customer experience gaps, efficiency gains, decision-making acceleration. Use cases should be prioritised not by technical possibility, but by strategic relevance.
Why Frameworks Beat Big Platforms
There's a fundamental difference between building for adaptability and buying for convenience. Big AI platforms promise integration, automation, and acceleration. But what they often deliver is rigidity.
They're hard to replace. They limit optionality. They centralise decision-making in systems rather than people. And they rarely keep pace with open model innovation.
By contrast, a composable framework gives your organisation the ability to swap, integrate, and extend capabilities as needed. It's the same logic behind modern integration platforms and composable MarTech stacks, applied to AI.
You don't build around one vendor's roadmap. You build for your own.
Operationalising AI Without Overcommitting
Some will argue that doing something is better than doing nothing. But rushed adoption without architecture creates more problems than it solves.
The goal is not just to introduce AI into the business. It's to make it operational. That means:
- Mapping use cases to workflows
- Equipping teams to use the tools effectively
- Measuring real impact, not superficial engagement
- Ensuring responsible usage with built-in guardrails
You don't need to choose a model today. You need to build the capacity to choose wisely tomorrow, and again the day after that.
Final Thought: The Curve Is Moving
The organisations moving today with clarity, strategy, and humility are setting themselves up for a decade of advantage. Those stuck in a holding pattern or placing big bets on singular platforms are gambling their agility.
AI is not a passing trend. It is a new operating environment.
The businesses that thrive in it will not be those who acted fastest. They'll be the ones who acted most intelligently, with frameworks that adapt, systems that scale, and teams who know why they're using it in the first place.
Next Step: Start With the Right Conversation
If you're ready to move beyond AI noise and into structured, scalable adoption, we can help. Our Spark Sessions are designed to help businesses build clarity around AI strategy, without the pressure to buy into platforms too early.
You don't need to pick the perfect model today. But you do need to begin building the right foundation.
Let's get that started.
Book a Spark Workshop
