Decision intelligence has been a tech buzzword for several years. Still, it wasn't until 2022 that Gartner named it a Top Trendand put a clear definition in place. By doing so, Gartner changed decision intelligence from a vague marketing term to an increasingly important business strategy.
The concept is grounded in the idea that companies should treat decision-making like other modern business processes and thus implement systems of record to model, track, learn from and improve specific management decisions. This decision-back approach starts with the critical decisions that drive company performance and then works back to the people, processes and insights needed to make consistent, high-quality decisions.
A New Strategy Rises Above The Marketing Noise
While blessed with a catchy name, the decision intelligence category still took a while to coalesce. Until recently, leaders prioritized data and analytics investments over direct improvements to the decision-making process itself. As a result, the promise of "better decisions" was used for decades as a universal marketing backdrop for all technology solutions, especially data-centric solutions like business intelligence dashboards, analytics and artificial intelligence (AI). The theory was that business people would naturally make better decisions if companies invested in better data and analytics.
Time and experience eroded that data-centric theory, revealing the reality that as much as 60% of such data investments are wasted. Decision-makers only use 22% of the jumble of data-driven insights they receive. The hype cycle for data, insights & analytics is maturing into the efficiency phase, bringing tough questions about how to optimize budgets and initiatives. At the same time, the sky-high AI promise of "soon you won't even need humans to make decisions" is falling back to earth. More and more executives are asking, "Will we ever see a concrete return on investment from all this data infrastructure?"
The emerging decision-back perspective is very different from past data-first strategies. Gartner's decision intelligence paradigm shift moves away from decisions as important but fuzzy marketing abstractions and towards decision-making as a concrete, measurable business process that needs to be managed. It changes decision intelligence from a clever rebranding of business intelligence and AI solutions to an entirely new transformation strategy.
The Pandemic Shakeup
The pandemic played a significant role in driving the decision intelligence tipping point. It is remarkably challenging to change decision-making behavior. However, the pandemic catalyzed decades of behavior change in months, tilling up old habits so that new ones could grow.
First, the massive shift to remote online work broke many thought patterns about how collaboration, and thus decision-making, happens. Almost overnight, leaders learned how to operate and make decisions without being able to pull everyone into the same room.
Second, the massive overnight changes to business strategy and operations accelerated the already rapid pace of decision-making in modern businesses to truly overwhelming levels. This forced companies to learn to decentralize decision-making and push decisions out into the organization.
Most importantly, companies learned that this new approach worked better than the old ways, driving sustained improvements in operating metrics and increases in stock prices after the initial lockdown disruptions.
CPG and Pharma Beach Heads
Geoffrey Moore's classic "crossing the chasm" framework for technology innovation emphasizes the difficulty of getting the first few pragmatic companies to implement new technology strategies. Such pragmatists are not enthusiastic about innovation in general. Instead, they have clearly defined needs that demand a technology solution. This chasm between enthusiastic early adopters and practical problem solvers played a significant role in holding back the decision intelligence trend.
In retrospect, it's not surprising that consumer packaged goods (CPG) and pharmaceutical industries have become the pragmatic beachhead for decision intelligence growth. In addition to pandemic-disrupted work practices, both industries were severely impacted by overnight transformations of consumer demands and widespread supply chain disruptions. As a result, these major global brands urgently need pragmatic solutions and are actively creating initiatives today.
As one example, my company works with a billion-dollar consumer goods company that has placed a moratorium on new data and analytics spending while implementing a decision intelligence strategy to optimize their investments to explicitly support and improve commercial decision-making. Theirs was not a small decision given the status quo, and more and more companies are making the same choice as awareness of decision intelligence grows.
A Technology Category Emerges, Decision-By-Decision
These market changes have formed a new category of technology companies over the past several months. Besides Cloverpop, several other growing tech companies, including Peak, Sisu Data, Domo, Tellius, Pyramid Analytics, Diwo and others, are shifting their focus from data- and tech-centric business intelligence and artificial intelligence to business-centric decision intelligence solutions.
Yet all of these trends still miss the underlying explanation of why decision-making is both the essential business process and the last one to get a digital makeover: today, most decisions are fundamentally irrational, happening intuitively in shadows and sidebars. They have to be cornered before business leaders can conquer them one by one. Large companies have a history of pioneering disciplined business processes, giving them the scale and expertise to track decisions and bring this sort of rationality to a significant number of people. These global brands move very slowly until they move all at once.