How to Build a Decision Intelligence Strategy That Improves Business Decisions

Many organizations still struggle to turn data into consistently better decisions. Recent research shows that a large share of high-value decisions are made with incomplete information, and a significant portion of enterprise data remains unused for analysis or action. At the same time, decision intelligence is moving from concept to practice: nearly 88% of enterprises have either implemented or plan to pilot decision intelligence initiatives, and by 2027, 50% of business decisions will be augmented or automated by AI agents for decision intelligence.

Decision intelligence (DI) goes beyond traditional business intelligence. It combines data, analytics, and AI to support, augment, or automate the decisions that drive outcomes while keeping human judgment and accountability in the loop. If you’re looking to optimize your decision-making and adopt AI-enabled analytics with some tried-and-tested practical guidance, this topic is for you.

Start with Low-Risk Workflows and Priority Decisions

Like all new digital transformations, employing an effective DI strategy requires planning and foresight. The first step is canvassing your existing processes to best determine where automation or AI-supported augmentation can easily take over. As a sort of trial run, select a low-risk and basic business process that your team will find easy to start with.

Additionally, look to implement DI in the business functions where critical decisions are weak and need improvement, or where more data is needed to make more informed decisions. You’ll gain a quick win, as well as gain expertise and practice executing the DI method on processes where you can better mitigate the risk. Stay focused on one workflow at a time, and then take on other more extensive processes later once you gain some experience and a better understanding of what’s involved.

It can be tempting to try to implement a full decision intelligence approach immediately because you’re eager to enjoy the good things that come from it: better, faster, smarter insights and greater business agility. We caution against this though. We don’t want to overwhelm you or your staff, and you need time to introduce the concept and its benefits to everyone. Like the hare and the tortoise, “slow but steady wins the race” is the perfect DI modus.

Optimize the End-to-End Decision Process

Apply process optimization or improvement cycles when executing DI in the workplace so you can drive performance improvement. The goal is to optimize every step of your end-to-end data-driven decision process. From identifying the key areas to targeting the relevant data and engaging the right stakeholders, everything should be analyzed and optimized to ensure you’re continuously drawing conclusions that support business longevity and growth.

Process optimization isn’t a new concept to most of our clients. If you’ve implemented an Enterprise Resource Planning (ERP) software system with us, you know all about it. Your DI framework requires the same type of optimization strategy. It can’t be implemented and then forgotten about. It must be an ongoing effort that is reviewed and improved upon when necessary, and there’s always room for improvement. Implementing DI without process optimization hinders true cost savings and limits the ability to maximize productivity.

Reporting and analytics initiatives that support process optimization help a lot. Implementing effective reporting makes managers’ lives easier by clearly tracking and highlighting the information and KPIs needed to make informed decisions. Set up, monitor, and trust the numbers to be your guide, especially as you begin layering in AI-enabled insights.

Learn from Your Data, Build Trust, and Be Willing to Change Course

Estimates indicate that around 55% or more of enterprise data is considered “dark,” stored but unused for analysis or business decisions, with some organizations reporting even higher shares. One reason could be that businesses don’t have a solid data foundation in place to handle the increasingly high volumes of data. Another reason could be that businesses don’t fully trust or easily access their data, so they avoid making decisions with it. Whatever the reason, understanding and learning from your data is critical to successfully working with DI and AI-enabled analytics.

Adjusting course because the data (or an AI-supported recommendation) is telling you something different than originally planned isn’t anything to be afraid of or deterred by. You’re learning from your data and using it to challenge corporate “defaults” to try new, possibly better, things. Start with augmentation, where AI surfaces insights or options and humans retain final judgment, before moving toward broader automation on lower-risk decisions. Clear ownership and simple governance keep the approach practical and accountable.

More and more businesses today are looking to accomplish more with less using artificial intelligence. To gain a competitive advantage and stay relevant, the choice is clear. Decision intelligence continues to gain momentum as organizations move from insight to action.

DI is no longer in its infancy. Its potential is enormous for modern, tech-focused businesses. If you’re interested in hearing more about building a practical decision intelligence strategy that improves data-driven decision-making and supports AI-enabled analytics adoption, talk to our subject matter experts at BT Partners. Data-driven decisions are our cup of tea.