The Biggest Mistake Businesses Make with AI Isn’t Choosing the Wrong Tool
Artificial Intelligence has quickly become one of the biggest priorities for business leaders.
Walk into almost any growing business today and you’ll hear similar conversations.
“Should we switch on Microsoft Copilot?”
“Can Monday.com AI automate more of our workflows?”
“Should we build AI agents?”
“Are we falling behind our competitors?”
These are sensible questions.
But they’re rarely the first questions businesses should ask.
The pressure to adopt AI has become so intense that many organisations are beginning with technology before fully understanding the business problems they’re trying to solve.
That is creating a new kind of commercial risk.
Businesses are becoming better at implementing AI than understanding where AI will create the greatest value.
Almost every major software platform now promotes AI as the next step in improving productivity. Microsoft has Copilot. Monday.com has AI. HubSpot, Salesforce, Notion, Asana and many others are embedding AI into the tools organisations already use.
The technology is advancing rapidly.
The challenge is ensuring businesses improve the way they work—not simply the tools they work with.
AI adoption is accelerating. Business transformation is not.
The pressure to adopt AI is real.
Boards are asking for AI strategies. Employees are experimenting with generative AI. Competitors are announcing new AI initiatives almost weekly, while software vendors continue to release increasingly capable AI assistants, copilots and agents.
These developments represent genuine progress.
However, they can also encourage organisations to believe that implementing AI is, in itself, a transformation strategy.
It isn’t.
Recent research from McKinsey found that organisations creating the greatest business value from generative AI are not simply deploying more AI. They are redesigning workflows, strengthening governance and changing how work is organised alongside technology implementation. Workflow redesign showed one of the strongest relationships with achieving measurable EBIT impact from generative AI.
The implication is straightforward.
Technology creates value when it improves a well-designed organisation—not when it attempts to compensate for a poorly designed one.
Why this is particularly relevant for SMEs
Large organisations often have transformation teams, business analysts and dedicated change programmes to evaluate where AI should be introduced.
Most SMEs do not.
Instead, responsibility usually falls to the founder, managing director or a small leadership team already balancing customers, sales, marketing, operations and finance.
That makes it tempting to start with the software.
If Microsoft has released Copilot or Monday.com now includes AI, switching it on feels like progress.
Sometimes it is.
But without understanding where commercial friction already exists, businesses can spend months improving activities that were never the real constraint to growth.
For many SMEs, the biggest opportunity isn’t adopting more AI.
It’s understanding where work slows down, where decisions become inconsistent and where information gets trapped between teams.
Businesses often confuse AI adoption with business transformation
Installing AI software is relatively straightforward.
Transforming how an organisation operates is considerably harder.
Those are two very different challenges.
AI can summarise meetings.
It can draft proposals.
It can analyse documents.
It can search knowledge bases.
It can generate reports.
It can identify patterns across large volumes of information.
These capabilities save time.
What they cannot do is determine whether the underlying work creates value.
If every customer discount requires six management approvals, AI may accelerate the approval process.
It won’t ask why six approvals are needed.
If marketing qualifies leads differently from sales, AI may process information faster.
It won’t align commercial teams.
If customer information is duplicated across multiple systems, AI may update every record.
It won’t solve poor data governance.
Technology improves execution.
It rarely redesigns organisations.
A scenario we’ve seen repeatedly
Imagine a growing B2B business employing around thirty people.
Marketing is generating leads.
Sales is following up.
Customer Success manages onboarding.
Operations keeps projects moving.
Everyone is busy.
Leadership decides to enable AI within the CRM.
Meeting summaries become automatic.
Proposal writing becomes faster.
Email drafting improves.
Dashboards are generated in seconds.
Six months later, revenue growth has barely changed.
Not because the AI failed.
Because the workflow never changed.
Marketing still qualified leads differently from sales.
Account ownership remained unclear.
Customer handovers depended on individuals rather than a consistent process.
Forecasts varied depending on who updated the CRM.
Knowledge remained trapped inside people’s inboxes, spreadsheets and conversations.
The technology accelerated existing work.
It didn’t improve how the organisation worked together.
This is where many AI initiatives quietly lose momentum.
Business understanding should come before AI implementation
Before deciding where AI belongs, organisations should first understand how work actually flows through the business.
Questions worth asking include:
Where do projects consistently slow down?
Which workflows create the most customer friction?
Where are leads lost because qualification is inconsistent?
Where are proposals delayed by manual approvals?
Where is customer information duplicated?
Which decisions depend on individual knowledge rather than shared processes?
Where does revenue leak because of inefficient workflows?
These are not technology questions.
They are business questions.
Ironically, AI itself can help answer them.
Large language models can analyse CRM activity, customer feedback, meeting transcripts, support tickets and operational documentation to identify recurring bottlenecks, duplicated work and hidden patterns.
Used this way, AI becomes an analytical partner before it becomes an automation tool.
The Tenon Growth AI Readiness Framework
At Tenon Growth, we believe organisations create the greatest value from AI when they follow four stages.
1. Understand
Map workflows.
Interview teams.
Analyse commercial performance.
Identify constraints.
Understand how work actually happens—not how people assume it happens.
2. Decide
Prioritise the problems worth solving.
Not every operational issue requires AI.
Some require clearer ownership.
Others require simpler processes, better governance or improved communication.
Technology should support business priorities—not define them.
3. Redesign
Improve the workflow before introducing automation.
Remove unnecessary approvals.
Reduce duplication.
Clarify responsibilities.
Simplify how information moves between departments.
Only then should automation become part of the discussion.
4. Augment
Finally, introduce AI where it genuinely strengthens the redesigned operating model.
AI becomes an enabler of better work rather than a workaround for inefficient work.
The framework deliberately places technology last.
Competitive advantage rarely comes from using more AI than everyone else.
It comes from combining better workflows, better decisions and better use of technology.
Human judgement remains the competitive advantage
Microsoft’s 2025 Work Trend Index describes the emergence of “Frontier Firms”—organisations redesigning work around collaboration between people and AI rather than simply embedding AI into existing processes.
That distinction is important.
The organisations creating the greatest long-term advantage are unlikely to be those deploying the largest number of AI tools.
They will be those that understand:
where AI genuinely creates value
where human judgement remains essential
where processes need redesign before automation
how people, technology and workflows work together.
AI should support judgement, not replace it.
Leadership still decides priorities.
People still interpret context.
Managers still balance commercial trade-offs.
Technology makes good organisations better.
It rarely fixes poorly designed ones.
Frequently Asked Questions
Should businesses implement AI before reviewing their processes?
Usually not. Understanding where work slows down and where commercial friction exists helps businesses prioritise where AI can create measurable value.
Can AI help identify workflow problems?
Yes. AI can analyse CRM data, meeting notes, customer feedback and operational documentation to identify patterns and bottlenecks. People still need to interpret those findings, prioritise opportunities and decide what should change.
Does every workflow need AI?
No. Some workflows benefit more from simplification, clearer ownership or improved governance than automation. AI should solve meaningful business problems, not be added simply because it is available.
What is AI readiness?
AI readiness is an organisation’s ability to introduce AI into well-understood, well-managed workflows where technology supports clear business objectives rather than compensating for weak processes.
Final thoughts
Every business will almost certainly use more AI over the next decade.
That isn’t the difficult part.
The difficult part is understanding which work should change, which work should disappear and which work should remain firmly in human hands.
Before investing in another AI platform, review one important workflow.
Understand how information moves.
Where decisions slow down.
Where ownership becomes unclear.
Where customers experience unnecessary friction.
Where revenue leaks through avoidable inefficiency.
Those conversations often reveal opportunities that no AI platform can identify on its own.
Only then ask where AI belongs.
Businesses rarely become more competitive simply by using more AI.
They become more competitive by understanding how work creates value—and using AI to strengthen it.
AI doesn’t create competitive advantage on its own.
Well-designed organisations do. AI simply amplifies the quality of the organisation behind it.
Related Reading
Most Businesses Already Have AI. They’re Just Not Using It.
The Messy Middle of AI Adoption.
AI is Bringing Enterprise Capabilities to SMEs.
Commercial Workflow Diagnostics.
AI Opportunity Mapping.
References
McKinsey & Company. The State of AI: How Organizations Are Rewiring to Capture Value (2025).
Microsoft. 2025 Work Trend Index: The Year the Frontier Firm Is Born (2025).