Gartner acknowledges growth of Decision Intelligence Platforms with inaugural Magic Quadrant
The business world is on the cusp of a profound shift, moving away from the “data-driven” mantra to one that is “decision-centric,” powered by Decision Intelligence Platforms (DIPs). This emerging category, which recently saw its inaugural Magic Quadrant from Gartner signifies that the focus is shifting from simply analyzing data to actively augmenting and automating the decision-making process itself.
Prior iterations of this type of platform, back in the late 1990s and early 2000s, were called digital decisioning platforms, which Gartner analyst Kjell Carlsson told SD Times were all about decision automation. Later came the notion of software intelligence platforms, based on AI observability and value stream management to detect and remediate bottlenecks in the software process, as well as if workers are assigned to the proper tasks to achieve business value. “So the the the opportunity here is to go in and take what has been a pretty traditional market around effectively, business rules engines … and now we have the opportunity to go in and infuse more machine learning and more generative AI capabilities and be able to really change how we’re doing decision making in a lot more areas of the of the organization,” he explained.
The goal, he said, is aiming to prevent catastrophic, value-destroying decisions—like the infamous AOL Time Warner or HP-Compaq mergers—by structuring the decision process and ensuring the right information is bubbled up. “Surely, if we had been able to bubble up the relevant information and structure the decision-making process in a logical fashion, we would have been able to avoid those,” Carlsson said. “And that’s at the top level. You cascade that down to all of the decisions that we’re making in an organization that don’t have the right information. You’re not doing sufficient analysis of it. You’re not able to look at decisions that were that happened before and learn from them.”
Decision-making augmentation involves platforms ensuring a human has processed, integrated, and contextualized information, while also managing the approval workflow (like coordinating sign-offs). Full automation is reserved for lower-risk, highly standardized processes, such as small credit decisions or immediate auto insurance quotes, where the process is heavily regulated and speed is critical.
Carlsson noted that Decision Intelligence Platforms can track prior outcomes, point out flaws and biases in the decision-making process to make organizations better. “And now, with generative AI, we can tap into unstructured data,” he pointed out. “We can go in and use these tools to formalize that logical decision-making process, and even be able to track and follow up on the outcomes of it.”
In determining which companies make it into the Magic Quadrant, Carlsson explained that Gartner looks at organizations from two levels: the product or service capabilities, and at the overarching organization itself, but admitted more weight goes into the critical capabilities.
The vendor landscape is a blend of the old and new. Long-time digital decisioning leaders like FICO represent the establishment, leveraging maturity and proprietary data for regulated use cases. In contrast, new, pro-code platforms like Quantexa offer flexibility with features like proprietary knowledge graphs for building complex, custom analytics applications. Straddling both are analytics giants like IBM and SAS, where decision modeling is a strong component of their advanced analytics portfolio.
Yet, Carlsson noted, the market is young, and the adoption of generative AI into these platforms is not yet robust. The market is vulnerable to potential disruption from large agentic AI companies, like OpenAI, should they decide to focus on decision-specific tooling. A key challenge, however, may be less about technology and more about human nature: the inherent reluctance of leaders and managers to adopt tools that track, compare, and judge the outcomes of their personal decisions.
Here are some statistics on this space from Gartner:
By 2027, 25% of ungoverned decisions using large language models (LLMs) will cause financial or reputational loss due to human biases, insufficient critical thinking, and AI sycophancy.
By 2027, 50% of business decisions will have been augmented or automated by AI agents for decision intelligence.
By 2028, 25% of CDAO vision statements will become “decision-centric,” surpassing “data-driven” slogans, with human decision-making behaviors explicitly addressed to improve D&A value.
By 2030, explicitly modeled business decisions will be five times more trusted and 80% faster than ungoverned decisions, enabled by decision intelligence platform adoption.
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