AI is accelerating decision-making in business, but is it really improving it?
In short, not quite yet. The problem is many businesses are adopting AI faster than they’re improving how decisions are made. When decision-making processes are already fragmented, adding AI tools on top doesn’t fix the problem but amplifies it. Any gaps in alignment, context and clarity are widened, at scale.
As we start to see agentic AI adoption sweeping across the enterprise, the risk is only going to intensify, as businesses prioritise speed to action, while the quality of insights and processes behind unearthing those insights gets left behind.
The leadership challenge
When leaders implement AI without rethinking existing processes, it creates a false sense of confidence. AI outputs are often presented with certainty and authority, masking the fact that they are often built on a multitude of incomplete, siloed or misaligned inputs.
Decisions may appear more data-rooted and be faster, but they are still shaped by confusion. As a result, there is a much wider gap between insight and impact, felt most acutely by customers. Teams spend more time than ever analysing huge volumes of data yet make fewer decisions that make a meaningful difference to the customer experience.
Addressing data overwhelm
As data usage and reliance increases, many organisations still rely on strategies that don’t scale, such as spreadsheets, dashboards and manual reporting processes. Departments analyse a narrow view of information and make decisions without collaboration or interrogation of this information in relation to the bigger organisational context.
Leaders are faced with an ever-growing number of insights, but struggle to piece together the trends and act on them.
It’s unsurprising business leaders estimate just 45% of business data is fully utilised in decision-making, meaning nearly half a company’s data is not used when making strategic decisions. No matter how many data points are consulted, leaders are effectively driving with one eye closed.
AI is a double-edged sword when it comes to this challenge too. At the same time as it unlocks more valuable insights and can improve efficiency, it also risks exacerbating underlying issues around insights overwhelm. As organisations look to implement AI tools, siloed data streams are creating isolated pockets of information that limit AI models from accessing the breadth of insights they need to operate effectively. All these issues contribute towards slower decision-making, missed opportunities, and inefficiencies.
Choosing AI that improves decision making, not just speed
The challenge is not a lack of data or insight, but a lack of context around it.
Working in silos isn’t inherently an issue. The real issue is when data, insights and information cannot flow between those silos, harming the accuracy of analysis and meaning critical dashboards and insights that teams rely on to make decisions live in a vacuum. What’s needed is a fresh approach that allows businesses to turn insights into actionable strategies, and decisions into outcomes and sustainable success.
Journey Management plays a crucial role here. It helps departments access, share, and act on insights across the business, even when their systems, metrics or goals differ. It aligns cross functional teams around a shared understanding of customer journeys, bringing together existing dashboards, data and insights into one place.
Crucially, it reframes data as a decision-making tool, not just a monitoring and reporting one. Doubling down on using data to inform decision-making ensures every decision is more intentional, customer-centric and grounded in real context, leading to a bigger impact long-term.
So, how does this look in practice?
Companies in finance, fintech, e-commerce, and professional services are already using journey-led “decision maps” to prioritise more effectively and invest with greater confidence. By visualising the impact that decisions have on the end customer, leaders can more easily identify the key areas to focus on.
Not only does this give more clarity, but it shifts how decisions are made. Data is no longer a simple reporting tool, but a clear guide for next steps. In turn, this means leaders feel better equipped to balance priorities, support their teams and make data-based decisions that stand up over time.
AI can be a powerful role to play in this process, but only when it’s built on a foundation of clear and connected decision-making. Without it, AI simply accelerates decisions that were heading in the wrong direction to begin with.
