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?
2026 won’t be short on data — but it will be short on clarity.
AI adoption continues to accelerate: according to McKinsey, 71% of organisations now use generative AI in at least one business function. Yet scaling remains a challenge. Gartner predicts that at least 30% of generative AI projects will be abandoned after proof of concept by the end of 2025, often due to unclear business value, weak governance, or poor data foundations.
The lesson is clear: AI doesn’t fail because of a lack of intelligence. It fails because organisations struggle to turn signals into decisions.
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, AI can detect fraud patterns in seconds — but only if models are explainable and auditable. In manufacturing, predictive maintenance only delivers value when sensor data is standardised and continuously monitored. In retail, demand forecasting improves margins only when decision-makers understand model confidence levels and risk thresholds.
The common denominator is not automation. It is decision intelligence — the ability to combine reliable data, transparent models, and accountable processes into actionable insight.
- 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.
Organisations are not suffering from a lack of information. They are suffering from fragmented systems, inconsistent definitions, and unclear ownership. Research from IBM suggests that poor data quality costs organisations millions annually — not only in financial impact, but in delayed or misguided decisions. When AI is trained on inconsistent data, it scales confusion instead of clarity.
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.
Trust is not a soft factor — it is a scaling requirement. McKinsey reports that 40% of organisations cite explainability as one of the top AI-related risks, yet only 17% actively work on mitigation strategies. Without explainability, AI recommendations remain suggestions. With transparency, they become decisions.
- 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.
In Europe, 2026 is not only about capability — it is about compliance readiness. The EU AI Act introduces phased obligations for high-risk AI systems, with major enforcement milestones beginning in 2025 and 2026. Organisations deploying AI without structured governance frameworks may soon face not only operational risk, but regulatory exposure.
Governance is therefore not a constraint. It is a strategic enabler for sustainable AI deployment.
- 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
Organisations that will succeed with AI in 2026 will not be those with the most pilots — but those with the clearest decision frameworks.
A practical starting point?
- Identify one critical decision process and assign ownership.
- Assess data quality and governance maturity.
- Introduce explainability standards before scaling automation.
AI does not remove complexity. It makes complexity manageable — when built on clarity.
At Getronics, we see that successful AI initiatives combine technical implementation with governance design and organisational enablement. Decision support is not just about deploying models — it is about building systems that people trust and use.
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.
