The shift to decision intelligence: Move beyond analytics to real time activation

Date:
Mon, 26 Oct 2026 10:31:13 +0000

Description:
AI is freeing up time specifically for strategic decision-making, not just automating routine tasks.

FULL STORY ======================================================================Copy link Facebook X Whatsapp Reddit Pinterest Flipboard Threads Email Share this article 0 Join the conversation Follow us Add us as a preferred source on Google Newsletter Subscribe to our newsletter UK organizations are already feeling AI's effect on the bottom line: a recent study found that 66% of UK enterprises are experiencing significant AI-driven productivity improvements, with leaders reporting that AI is freeing up time specifically for strategic decision-making, not just automating routine tasks.

That shift matters as, despite advances, analytics remained a retrospective
or heavy human driven insight tool for long reactive and slow to respond. With deterministic rule-based architecture, hard code, and static workflows. It worked well enough in predictable use-cases, but not in in dynamic, data-intensive scenarios. Rajan Padmanabhan Social Links Navigation

AVP, Unit Technology Officer for Data Analytics and AI at Infosys. This is changing rapidly, as AI re-imagines traditional analytics into decision intelligence, and autonomous AI goes even further, making decisions, scenario planning, and activating context aware actions with limited or no human instruction based on autonomy dial. Where traditional automation executed predefined workflows, agentic AI systems working in squads analyze data from varied sources to make a chain of complex decisions. Latest Videos From TechRadar Watch full video here:

For instance, an AI underwriting agent typically estimates a prospective borrowers creditworthiness, tweaks loan pricing, routes paperwork, alerts supervisors to compliance concerns. decisions once made by people, now made
by systems working in squads.

The scale of the opportunity is significant. The decision intelligence market in Europe, estimated to be $4.35 billion in 2025, is steadily advancing towards a projected value of $20.61 billion by 2035. UK organizations could
be well placed to capture a meaningful share of that value if they move deliberately. You may like Getting AI right requires more than a technology shift - it requires a mindset shift Beyond AI adoption: what it takes to deliver measurable business value Why AI success starts with teams, not tech Drawing the line between AI and human judgement The central challenge for any organization adopting decision intelligence is not the technology itself,
it's deciding who gets to decide what and when. Several organizations lack a formal mechanism for deciding the extent of autonomy they should allow their systems. This frequently leads to ad-hoc practices, without any limits enforcement or governance of autonomy.

The fix is to re-invent or re-imagine clear business decision flow rights
into the system from the outset - what an agent may act on independently, and what it must escalate to a person. Going back to underwriting agent example, it can reasonably price a loan or route paperwork on its own, but if it flags a compliance concern or encounters an applicant whose profile falls outside its constraints data , that decision should stop with a human, not get resolved by the agent guessing its way through an edge case. Are you a pro? Subscribe to our newsletter Sign up to the TechRadar Pro newsletter to get
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The future enterprise is not about replacing human judgment but augmenting it through intelligent delegation. Enterprise Decision Intelligence systems, fueled by domain aware foundational models and machine-consumable context,
can autonomously handle high-volume, full explainability, data-intensive decisions across supply chains, customer operations, pricing, and activation ecosystems.

Human expertise, meanwhile, remains indispensable for governance, ethical oversight, exception handling, and decisions where business context outweighs algorithmic certainty. Profound potential, profound risks The potential benefits are substantial. For instance, when beauty and fragrance retailers have implemented an AI decision engine that determined the right time and proposition for engaging each customer, it earned a double-digit ROI through strategic AI collaboration. What to read next Context, not correlation, will define successful AI implementation The last mile of AI: Closing the gap between insight and action 'The test isnt whether AI can do something. Its whether it can make the process measurably better': We hear why businesses need to be more selective about where theyre using AI

However, AI-based decision intelligence systems come with concerns such as cybersecurity risk, compliance failure, lack of transparency and accountability, and occasionally, improper outcomes.

Even the entry cost, especially when agentic AI is involved, can be quite
high and can be optimized through enterprise intelligence foundation models. Above all, full implementation needs to be iterative, involving not only a technology upgrade, but also operating model changes, deep integrations, specialized talent, and a robust ethical and governance framework to build transparency, accountability, and trust in these systems.

As organizations build experience and confidence, they will be able to progressively transition from reactive analytics to proactive decision intelligence. The ones that get the human-AI division of labor right first, not just the ones that move fastest, will be the ones that capture the advantage. We've featured the best AI tool. This article was produced as part of TechRadar Pro Perspectives , our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit



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