Decision intelligence has been a tech buzzword for several years. Still, it wasn't until 2022 that Gartner named it a Top Trend and put a clear definition in place. By doing so, Gartner changed decision intelligence from a vague marketing term to an increasingly important business strategy.
Decision intelligence is an emerging category that uses AI and machine learning to help organizations make better, faster, insights-driven decisions. The concept is grounded in the idea that companies should treat the decision-making process like other modern business processes. This means implementing 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.
While blessed with a catchy name, the decision intelligence category still took a while to come together. Until recently, decision-makers 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 data-driven decisions if companies invested in better data and analytics.
However, 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, and 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 and decision-makers 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.
Decision intelligence empowers organizations to make informed and confident data-driven decisions and then treat the decisions themselves as data for analysis and insight This decision-centric approach optimizes results and impacts business behaviors, ultimately driving better business outcomes.
Today, decision intelligence is enabling organizations to make more informed decisions. But the shift started back in 2020 with the onset of the pandemic.
The effects of COVID-19 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, transforming decision-making processes.
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.
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 large organizations in the 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. Leveraging customer data they already have, they aim to optimize their investments to 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.
These market changes have formed a new category of technology companies over the past several months. Companies like Cloverpop, along with many other growing tech companies, are shifting their focus from data- and tech-centric business intelligence and artificial intelligence to business-centric decision intelligence solutions.
These decision intelligence platforms are drawing the attention of a broad range of professionals, spanning data scientists, marketing experts, IT specialists, and executives.
Decision intelligence platforms, like Cloverpop, consolidate information from various data sources and provide actionable insights to support more informed decision-making. This, in turn, promotes alignment and collaboration across various departments, ensuring that marketing, IT, and data science teams are on the same page. This alignment enhances cross-functional collaboration, leading to more cohesive operational decisions and better business outcomes.
With that said decision intelligence platforms offer significant advantages that go beyond making informed decisions in the here and now. By creating detailed decision records that turn decisions into data, they can leverage AI to make recommendations to optimize future decisions. This also enables organizations to forecast results and make better strategic choices. This predictive analysis allows businesses to see future outcomes before they happen. With these insights, they can stay ahead of market trends and respond proactively to changes.
The rise of decision intelligence marks an exciting new era in business strategy. As companies increasingly recognize the value of treating decision-making as a critical business process, we're witnessing a fundamental shift in how organizations approach problem-solving and strategic planning.
Decision intelligence platforms, like Cloverpop, are at the forefront of this transformation, leveraging AI and machine learning to provide actionable insights and drive better outcomes. By consolidating data points from various sources and offering predictive analysis that derives insights from past decisions to optimize future decision-making, these platforms are empowering businesses to make more informed, data-driven decisions faster than ever.
Looking ahead, decision intelligence promises to revolutionize the way organizations operate, fostering a culture of informed decision-making and continuous improvement. As this field continues to evolve, it will undoubtedly play a crucial role in shaping the future of business strategy, driving innovation, and creating new opportunities for growth and success.