Key Takeaways
Most analytics programs still begin with available data rather than the business decision that needs improvement.
A decision-first approach starts with the desired outcome, then works backward to the choices, processes, people, and data required to support it.
This reduces analytical waste because models and dashboards are tied to specific business actions rather than vague reporting needs.
Decision Model and Notation gives teams a structured way to map business logic, decision rules, data inputs, and workflow requirements.
Most companies have become comfortable producing insights from data. Business intelligence teams build dashboards, analytics teams create forecasts, and AI teams develop models that identify patterns in customer behavior, risk, operations, pricing, and demand. The harder problem is turning those insights into better business outcomes.
The gap is that many organizations still work from the bottom up. They start with the data they already have, search for patterns, and then try to attach those patterns to a business question later. A decision-first approach reverses that sequence. It begins with the outcome the business wants to improve, then identifies the specific decisions that influence that outcome, the people who own those decisions, and the data required to support them.
This matters because insights often fail when they are not connected to an operational workflow. A churn model may identify at-risk customers, but it only creates value if the business knows who should act, when they should act, what offer or intervention to trigger, and how the result should be measured. A pricing forecast may be technically accurate, but it has limited impact if pricing managers do not know which decisions it should change. In both cases, the insight is useful only when it is tied to a decision point.
The decision-first method also reduces analytical waste. Many organizations invest in sophisticated dashboards and models that never change a business action. The issue is that the work was not anchored to a clear decision. By defining the desired business goal first, teams can avoid building analytics assets that look impressive but do not influence revenue, cost, risk, compliance, or customer experience.
The core operating question is simple: what decision are we trying to improve? Once that question is clear, the organization can map the decision requirements, identify the knowledge owners, define the relevant data and analytics, and connect the decision logic to the business process. This creates a clean path between data work and business performance.
Decision Model and Notation is one way to make this shift more concrete. It gives business and technical teams a shared structure for modeling decisions, rules, dependencies, and data inputs. This is especially useful in regulated industries such as finance and healthcare, where teams need transparency, auditability, and a clear explanation of how a decision was reached.
The implementation challenge is mostly cultural. A decision-first approach requires teams to stop treating analytics as a reporting function and start treating it as part of how work gets done. That means defining the metric, identifying the decision-maker, mapping the workflow, and then building the data infrastructure around the decision. The data pipeline comes after the business decision is understood, not before.
The broader lesson is that enterprises do not get better outcomes simply by becoming more data-driven. They get better outcomes when data is attached to the decisions that move the business. The next stage of analytics maturity is not more dashboards or more models. It is clear ownership of decisions, strong links between insight and action, and continuous review of whether those decisions still match current business conditions.
Market Pulse
A new IBM report covering more than 2,000 organizations found that 76% of the organizations surveyed have established a new executive office, that of the chief AI officer, up from 26% in 2025. The report frames the CAIO mandate as distinct from the CIO, CTO, and CDO roles, with a remit focused on how AI is applied across the enterprise to change how work, decisions, and execution are done. The same report found 59% of respondents expect the influence of the chief human resources officer to grow.
Broadridge announced production-ready agentic capabilities that chain data, context, and workflows to automate exception resolution across post-trade and client services, offered either as managed services or a standalone platform. The approach combines ontology-backed data normalization with supervised-agent workflows, illustrating how vendors are packaging agentic automation to meet regulatory and audit requirements in finance.
Generative AI has moved from a side experiment to a genuine capability, and by early 2026, it will have evolved into systems that craft detailed scenario narratives, synthesize information from heterogeneous data sources, and support iterative, multi-step decision exploration. Organizations should build internal capability through hands-on experimentation rather than relying entirely on opaque vendor solutions, and stay focused on faster decisions, better decision quality, and outcomes that hold up under risk.
Resources and Events
📅 World Summit AI 2026 (Amsterdam, Netherlands - October 7-8, 2026)
One of Europe’s largest enterprise AI events, bringing together business leaders, AI practitioners, policymakers, and enterprise technology teams. The 2026 program focuses heavily on AI governance, operational deployment, enterprise adoption, regulation, and the responsible scaling of AI systems across industries such as finance, healthcare, retail, and manufacturing. Details →
📅 The AI Summit London 2026 (London, UK - June 10-11, 2026)
An enterprise AI conference focused on practical implementation, governance, infrastructure, and operational adoption across large organizations. The summit brings together enterprise technology leaders, analytics teams, regulators, and AI vendors to discuss deployment challenges, responsible AI practices, and the integration of AI into business operations and decision systems. Details →
📊 Report Spotlight: The State of Data Quality for 2026 (Monte Carlo)
Monte Carlo’s State of Data Quality for 2026 examines how enterprises are managing data reliability as AI systems move deeper into production environments. The report draws on operational data from hundreds of organizations using data and AI observability systems and notes that more than 1,000 data quality incidents are detected and resolved daily across the platform. The report focuses on operational reliability, incident response, workflow monitoring, and the growing importance of observability as organizations embed AI into business processes and automated decision systems. Read →
The DecideWise Edge
Benjamin Baer, Co-Founder of the DecideWise community, argues that enterprise decision strategy should begin with the decisions a business needs to improve. The core point is that better outcomes come from understanding who owns a decision, what changes if that decision improves, where AI can assist, where human judgment still matters, and which metrics will be used to judge the effectiveness of decisions and outcomes. A decision-first strategy puts data in its proper role by connecting analytics, rules, human expertise, and AI to specific business actions. For enterprises, the discipline is not about collecting more data. It is about making every data source, rule, model, and workflow serve the same purpose of facilitating better decisions that produce measurable business outcome. Read More →
For the Commute
Why Context Is the Missing Layer in Enterprise AI (Decision Intelligence Lab)
Dr. Ram Bala joins Vijay Mehrotra and Michael Watson to explain why enterprise AI needs context, not just access to large language models. The discussion focuses on how generic AI answers break down inside companies because they miss role-specific knowledge, historical decisions, contract patterns, incentives, and workflow constraints. Bala uses examples from procurement, legal, pharma sales territory planning, and cross-functional coordination to show how AI can help teams align on better decisions rather than simply making individuals more productive. The episode also warns against agentic chaos, in which too many AI agents create noise, duplicate work, and produce low-quality outputs without clear accountability.
