How to Use AI in Business: Turning Experimentation Into Real Leverage
Scale-Up Sessions by Cynergy Bank, Yinka Fasawe
Artificial intelligence is everywhere, but for scaling businesses, the real challenge is not access to AI tools. It is knowing how to use AI in business to create meaningful impact. In this episode of Scale-Up Sessions by Cynergy Bank, Yinka Fasawe, AI go-to-market lead at Google Cloud, explains how leaders can move beyond fragmented experimentation and use AI to unlock genuine leverage.
For many organisations, AI adoption today can feel like a race to keep up. New tools are added quickly, teams experiment independently and costs begin to rise without clear outcomes. Without a structured AI adoption strategy, what should be a growth enabler can quickly become an expensive distraction.
Key takeaways:
Understand that more AI tools do not automatically create more business value.
Start with a specific business problem before choosing platforms or tools.
Use AI workflow automation to redesign how work happens, rather than simply speeding up existing processes.
Build a system-led AI strategy for scale-ups to avoid fragmented adoption and support sustainable growth.
Why more AI tools do not mean more value
A common misconception among growing businesses is that adopting more AI tools will naturally increase productivity. In reality, this often leads to complexity rather than clarity.
As Yinka explains, many organisations still approach AI in the same way as traditional software, starting with tools rather than outcomes. This can quickly result in what is often described as AI sprawl, where different teams use different solutions without alignment or shared goals.
I think it's usually better to work backwards from what you're trying to solve… and then figuring out what right platform or tools actually does the job.
Yinka Fasawe
Instead of asking which AI tools to adopt, leaders should start by defining what success looks like. By focusing on outcomes first, businesses can make more deliberate decisions about where AI will genuinely add value.
From siloed experimentation to system-led thinking
One of the biggest barriers to effective AI adoption is fragmentation across teams. When individuals experiment in isolation, duplication increases and opportunities for scale are lost.
In contrast, organisations that see results tend to take a more coordinated approach. They adopt a centralised platform where AI capabilities, workflows and agents can be shared and deployed across the business.
Where we see things work better is when you have a central platform… and then have repeatable workflows that are deployed across the company.
Yinka Fasawe
This shift changes how AI is used. Rather than acting as individual productivity tools, AI systems become part of a broader operational model. Businesses move from isolated use cases to integrated workflows that scale efficiently.
Where AI creates real, non-linear growth
The most powerful impact of AI is not incremental, but exponential. According to Yinka, this happens when organisations focus on three specific areas: repeatability, time constraints and data processing limitations.
When a process is repeated frequently, constrained by human capacity or involves large volumes of data, AI can transform the outcome rather than simply improve it.
In practice, this might involve automating document reviews, streamlining internal workflows or orchestrating AI agents to complete complex, multi-step tasks. Done well, this enables businesses to scale output without scaling headcount.
Rethinking work, not just speeding it up
A key theme from the discussion is that many organisations apply AI as an enhancement to existing processes, rather than an opportunity to redesign them entirely.
The real advantage comes when leaders rethink how work should happen in an AI-enabled environment. Instead of applying technology as a layer on top, they reimagine workflows from the ground up.
This approach requires stepping back from current processes and asking a simple question: if this task were designed today with AI in mind, what would it look like? In many cases, the answer is fundamentally different - and far more efficient.
Building the foundations for effective AI adoption
Beyond tools and workflows, successful AI adoption depends on leadership, culture and structure. Organisations need clear ownership, defined success metrics and a willingness to experiment.
Crucially, experimentation must be encouraged rather than penalised. AI is still evolving, and early initiatives will not always deliver immediate results. Creating an environment where teams can test, learn and refine is essential for long-term success.
I think there needs to be buy-in from the top… and we need to invest in the people… to actually adopt these tools to a certain goal.
Yinka Fasawe
Another important step is establishing visibility. Keeping a clear record of which tools are being used, what problems they are addressing and how success is measured helps prevent duplication and ensures efforts are aligned with business priorities.
A final thought for scaling businesses
AI has the potential to make businesses faster, smarter and more efficient, but only when applied with intent. The organisations that benefit most are not those adopting the most tools, but those using AI in a focused and structured way.
For leaders looking to scale, the priority should be clear: define the problem, design the workflow and then apply the technology.
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