After years of KPI programmes and dashboard roll-outs, more and more organisations are discovering that insight does not automatically produce action. In 2026, what makes or breaks an organisation’s business analytics strategy will be the effectiveness of its decision flows.
Repeatable, auditable paths from signal to recommendation to execution are pivotal to data analytics success. Gartner’s latest prediction is that by 2027, half of all business decisions will be determined by decision intelligence, either fully automated by AI agents or augmented by them. This push is often framed as the prescriptive layer, or analytics that answer not only what happened and what might happen, but also provide recommendations on what to do next.
“Decision intelligence”, however, is used to mean different things, depending on who’s talking and in what context they’re using it. Sometimes it involves rule-based automation triggered by thresholds. Sometimes it is analytics enhanced by AI modelling and embedded in the contexts where people make decisions.
In any given organisation, decision flows might encompass any number of processes, and clarity matters because organisations can only operationalise what they know works well for them.
Why dashboards struggle to carry decisions to the last mile
Dashboards are not going away, but the limitations of their usefulness become more visible as teams try to act faster. Information overload is only part of the story. The harder issue is co-ordination. Different teams optimise for different KPIs, definitions vary and “the truth” becomes a debate. This can be a significant challenge right when the business needs a decision. Even when the numbers are correct, teams still have to translate insight into action across different perspectives, tools and incentives.
“Dashboards aren’t meant to be passive displays; they should frame the data in ways that allow users to act quickly and confidently,” writes Luzmo’s Mieke Houbrechts. “When this doesn’t happen, even teams with strong data cultures risk losing momentum at critical moments.
“Such disconnect between availability and usability has real consequences,” she continues. “Decision-making slows down when users hesitate or second-guess the data in front of them. Instead of finding answers, they’re left piecing together fragments, relying on experience or gut feeling to fill the gaps.”
Houbrechts has found that 37% of users say dashboard data isn’t clear or actionable; 34% say they spend too much time navigating dashboards; and 40% say dashboards don’t consistently help them make better decisions. When that happens, teams compensate with workarounds like spreadsheets, side analyses and informal approvals. Rather than help, these slow decisions and blur accountability.
Using analytics for prescriptive modelling and execution
Decision flows aim to close that gap, bridging the journey between data signals and action. This includes what inputs matter, how they are interpreted, what recommendation is produced, who approves it, what system executes it and how outcomes feed back into the next decision. It turns analytics from a destination into a control loop, reducing “decision drift” when different teams interpret the same insight in different ways.
This is the point where AI goes beyond describing performance and proposes the next step. Avi Perez, co-founder and CTO of Pyramid Analytics, frames prescriptive analysis as the killer use case of AI-enhanced business intelligence.
“This is not a new thing, but if I can ask the question through generative BI through the large language models and it can work out from the question that I want to do a prediction, go and predict the data for me and then tell me the answer, it got more interesting,” Perez says. “It’s actually just to answer the question, ‘What should I do tomorrow morning to improve my sales in the UK?’ What is the ultimate use case for AI and generative AI? The idea that I don’t even need to see a bar chart. I don’t need to see the data. You just tell me what I need to do tomorrow.”
The implication is that decision-makers should not evaluate AI analytics by the charts it can generate to make sense of historical trends, but whether it can model actions that are usable on a regular basis and when deciding organisational strategy on a broader scale.
Demand is shifting to dependable, explainable recommendations
As AI becomes commonplace in analytics products, trust becomes the differentiator. Organisations want recommendations that are dependable, explainable and aligned with strategy and risk appetite. This goes beyond “smart suggestions” that cannot be defended when outcomes are questioned. A prescriptive layer that cannot be explained, constrained or audited tends to be treated as optional advice rather than operational guidance.
According to Dataiku’s Global AI Confessions Report, 80% of surveyed data leaders say that an accurate but unexplainable AI decision is riskier than a wrong but explainable one. That means prescriptive recommendations need to express trade-offs, respect policy constraints and provide evidence, especially when the recommendation conflicts with intuition or short-term targets.
For this to work, decision flows require a step that many analytics stacks still lack: standardised metrics and business logic that can be executed, not just reported. Informed by uniform definitions and priorities, AI-enhanced decision flows can support complex logic.
That “missing layer” is where work becomes repeatable, with escalation paths and integrations with operational systems (CRM, ERP, ticketing, finance). These capabilities enable approved decisions to be executed and logged for efficiency and transparency.
AI raises the ceiling, but guardrails define success
AI agents make decision flows more feasible because they can execute steps rather than just present insight. They gather context, run analyses, draft options and trigger workflows. But it’s autonomy that raises the stakes.
Gartner analyst Carlie Idoine cautions that AI agents for decision intelligence are not infallible and must be paired with governance and risk management, which means that human expertise, data and AI literacy remain essential. “Human decisions still require proper knowledge, as well as data and AI literacy,” she notes.
The guardrail challenge is not theoretical. In Dataiku’s survey, only 19% of data professionals say they always require AI agents to “show their work” before approval, and 52% say they have delayed or blocked agent deployments due to explainability concerns. In practical terms, this calls for scoped authority, approval tiers for high-impact actions, monitoring and rollback when outputs go wrong.
The goal is speed with accountability, requiring recommendations that can be acted on quickly and decisions that can be justified later.
From dashboards to decisions
In 2026, analytics strategies will be judged less by how beautifully a dashboard visualises business performance and more by how reliably and quickly people are enabled to take the next step. The killer app of AI in analytics is the ability to ask a business question, receive a recommendation aligned with strategy and risk, understand the underlying logic and tradeoffs, and execute it consistently inside the systems where work gets done.
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