01/01/2026
It’s a new year, but many business leaders are asking the same old question: how do we make clear decisions when everything around us is so unclear?
Between the AI boom, roller-coaster economic policies, intensifying regulations and ever-evolving business models, 2026 is set to be a wild ride. Many teams will start the year off with dashboards full of forecasts but still no clear view of where to begin.
Over the past few months, we’ve been breaking down the myths and talking about AI’s real impact on skills, trust, efficiency and personalisation. In this final chapter of our series, Five Actual Truths About AI, we’re diving into a topic that’s on a lot of leaders’ minds as we start this new year: how can AI really add clarity and drive better decision-making without becoming just another high-maintenance tech investment to look after? Experience shows that it depends on clean data, steady governance and teams who understand how the tool reaches its conclusions.
When data is more trouble than it’s worth
Across industries, people start their day facing operational data that doesn’t always line up with what they’re seeing in real life. Abundant data is a blessing. But it becomes a curse when teams are overloaded with information that lacks proper context and interpretation. This often just leads to confusion and unnecessary work. Here are a couple examples:
- In banking, an analyst might open their dashboard to find new, urgent-looking fraud alerts, but hardly any insight into what actually triggered them in the first place. How can they tell which alerts deserve closest attention, so they know what to prioritise?
- A manufacturer might see a rise in defects across several lines. They’ve got data on machine performance, supplier batches, operating conditions and more, but it’s all spread across multiple systems. How can they spot where the problem really lies and decide what needs to change?
These situations show how data can actually complicate things when it should really be helping people decide what to do next. AI is getting better and better at connecting information from different systems, identifying trends that develop slowly and alerting us when something doesn’t fit the pattern. It’s becoming a strategic decision-making aid everywhere from banking and insurance to manufacturing, retail and beyond.
From forecasting to enhancing decision-making
The more we work with AI, the more realistic we are about what it can and can’t do. Many organisations start their AI journey expecting for AI to be a crystal ball that accurately predicts future scenarios. But that expectation doesn’t hold up in markets that shift as quickly as the ones we’re moving into in 2026. The good news is: AI doesn’t have to provide your teams with a perfect forecast as long as it gives them a reliable sense of where they stand today, so they can respond to what comes next.
In retail, this comes up when teams try to understand sudden changes in customer behaviour. Online activity, store traffic and loyalty data often point in different directions, and the usual dashboards rarely explain why. When these sources are viewed together, the patterns become easier to interpret. Retailers can see whether they are dealing with a short-lived spike or a genuine change in demand, which helps them focus their time where it actually matters.
In insurance, the issue is the pace at which risks evolve. Claims patterns can shift quickly after severe weather or policy changes. A handler might see an unusual cluster of claims in one region and have no immediate context for why it’s happening. Tools that compare current cases with broader trends can highlight what stands out and why, but the reasoning needs to be visible. If a system flags a risk without explaining the factors behind it, the handler still has to do the interpretation manually.
Making AI work for you
No matter what the use case, to make AI really work for your organisation, it takes a three-layered approach:
- Transparency: People can only trust an output if they can see how the system reached its conclusions. In banking or insurance, for example, analysts cannot act on a flagged transaction or a rejected application unless they can explain the decision to customers. Without a reliable, logical explanation, the output will have to be rechecked by hand anyway.
- Governance: The data your AI tool works with must be clean, current and complete. It also takes continual monitoring to make sure the model is still behaving as expected. Otherwise, the AI’s output starts to lose touch with reality. In manufacturing, for instance, if production and supply chain systems are siloed, engineers may receive alerts based on old or incomplete data. When that happens, they’ll wind up spending more time investigating the source of an alert instead of addressing the problem itself.
- AI literacy: By now, it’s clear that AI is a supplement and not a replacement for human judgment. It takes dedicated training to ensure people are using AI to help them do their jobs better and not simply relying on its output without ever questioning it. We know that AI can speed up information-gathering and analytical tasks, but ultimately, it’s not about automating decisions. With careful implementation, AI serves as a clarity engine. It cuts through complexity so that your people are better equipped to make decisions for themselves.
Starting 2026 with a clearer view
In 2026, AI won’t remove uncertainty, but it can make day-to-day decision-making clearer and more efficient. It all comes down to solid data, steady governance and teams who are trained to deliver the best results. Here’s to a successful New Year, in which AI will bring more clarity to the tasks that matter most to your business.
Thanks for joining us in this series on the Five Actual Truths About AI. Be sure to check out our previous articles on how AI multiplies skills, builds trust at scale, powers workplace personalisation and drives efficiency under pressure.
