Beyond Predictive: How Decision Intelligence is Unlocking New Enterprise Value Streams

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To see real business value from AI, companies need to escape the trap of thinking of it as a cost-cutting tool alone. By creating a full data-driven strategy around AI, companies can do so much more. They can forecast demand and see where risks are developing before they become full-blown problems. AI can also help to anticipate customer behaviour and improve how operational issues are detected.

But there’s a step further than just prediction, too, and that’s where the highest value lies. In real-time decision support. 

Decision intelligence for enterprises looks at what happens after the prediction. When enterprises operationalize AI insights by connecting them to their decisions and workflows. And where AI moves from an analytics tool to an operational one. This is where many companies find new opportunities. This may be in extra value streams, or improving how the business works.

Most of all, AI-driven decision making supports faster, better responses to changing conditions. You no longer need to guess what might happen. 

Prediction Only Creates Value If It Changes What Businesses Do

The latest data from McKinsey and Co highlights a puzzling divide:

• Most companies are seeing poor returns from AI investment

• Yet, some are seeing up to a 20% uplift in EBITDA

That’s a massive gap. Digging deeper, what becomes clear is that the difference lies in how successful companies are using AI. Instead of simply layering AI tools onto what they already do, they’ve redesigned how they work to use the best of both AI and human insight. 

McKinsey ties this back to three ways they generate business value from AI:

1. Faster, data-backed action

2. Better use of existing assets

3. Improved opportunity capture

This connects with what IBM reports many executives see as the top obstacles to high-ROI AI. Namely, "processes, data, and decision rights that haven’t been optimized for AI.”

Simply having AI analytics is not the same as true decision intelligence for enterprises. Primarily because traditional analytics starts and ends at offering insight. You know what might happen, but there’s no action behind it. Real-time decision support, however, connects that insight to what should happen next, and how it could benefit the business.

This creates two benefits. It shortens the time between insight and action. But it also improves the quality of that insight. And that’s where the greatest gains happen. 

Decision Intelligence for Enterprises: Turning Data into Smart Operational Decisions

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Let’s look at what this means in practice. Currently, many businesses are investing in AI infrastructure without fully operationalizing AI insights. They have the data gathered. They may even have models to offer accurate predictions based on that data. But everything else still relies on isolated, often manual, processing and decision-making.  

The result is often a lack of “joining the dots.” Potentially valuable insights are missed, especially when departments stay siloed from each other. This gap must be closed  with business-wide decision intelligence. Only then can enterprises escape retrospective analysis and move towards real-time decision support. 

However, AI-driven decision making doesn’t mean automating every decision. That itself can create new risks. The best results arise when human oversight and final analysis are supported by relevant intelligence AI can surface quickly. 

AI excels at pattern finding, even in vast amounts of data. It can connect seemingly unrelated data. It ties together everything from customer demand to how assets are being used. And it finds novel ways to use that information to generate business value from AI.

This could look like:

• Finding areas to offer more personalized services, engaging more customers

• Optimizing pricing in response to changing conditions

• Identifying new areas of customer interest for cross-selling or product launches

• Highlighting early changes in how a production line is working. Issues can be addressed long before they become problems

Real-time decision support then moves to action these findings:

• A new product or service is launched ahead of competitors. The customer base and revenue increases

• Pricing stays fair and relevant, increasing customer retention and income streams

• Cross-selling increases revenue per customer, while remaining relevant to the end user

• Risk of offlining or waste is reduced with early action, saving money

In each of these situations, there’s the same pattern:

Data is moved into intelligence that supports a faster and timely decision. The action from that decision leads to higher-value business outcomes. Or identifies new value streams.

Making Decision Intelligence in Enterprise a Strategic Capability

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AI-driven decision-making unlocks real-time decision support. Companies can move away from setting plans based on periodic analysis. Instead, they can respond as new information emerges. They benefit from faster, higher quality decisions. This then creates an advantage over slower competitors, driving further business value from AI. It must, however, be paired with firm understanding of which AI recommendations should be actioned. And, of course, where human judgment is still essential. 

Decision intelligence for enterprises bridges AI prediction and insight-backed human action. This moves it away from “a tool” or “a reporting exercise”. Instead, it becomes part of a real data-driven strategy for success. 

The companies seeing the greatest business value from AI are those that connect intelligence to decisions and actions that benefit the business. In turn, supporting innovation and new value creation. 

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