Key Takeaways
RACI works well for projects because it clarifies who completes the work, who owns the outcome, who provides input, and who receives updates. It does not clearly identify who has final authority when a decision involves disagreement.
Every major decision needs one person with the authority to make the call. Other participants may recommend, advise, approve specific conditions, execute the decision, or receive the outcome.
Bain’s RAPID framework separates these roles into Recommend, Agree, Perform, Input, and Decide. This prevents the Accountable role from absorbing all of them.
Most organizations use the same governance model for projects and decisions. Under RACI, someone is Responsible for completing the work, someone is Accountable for the outcome, others are Consulted, and the remaining stakeholders are Informed. The model gives teams a useful way to assign ownership across a project with defined tasks and deliverables.
The problem begins when the same structure is used to manage a decision. RACI can show who prepares the analysis and who needs to be consulted, but it does not clearly answer who has the authority to end the discussion and make the final call.
Consider a pricing decision involving finance, sales, and product. All three functions may be marked Accountable because each has a legitimate stake in the outcome. Finance wants to protect margins. Sales wants to preserve demand. Product wants pricing to reflect positioning and customer value.
Each group may interpret accountability as the authority to block the proposal. Participants discuss alignment, concerns, and next steps, but nobody has a clear mandate to choose an option and close the debate.
This is where RACI creates ambiguity. The Accountable role may refer to ownership of the outcome, approval of the recommendation, authority over execution, or the right to make the final decision. Those responsibilities are different, yet the framework often places them under one label.
Bain’s RAPID framework separates them.
Recommend identifies the person or team that develops the proposal, evaluates the options, and presents a recommended course of action.
Agree identifies the people whose formal approval is required because they control a legal, regulatory, financial, or policy constraint.
Perform identifies the people responsible for implementing the decision.
Input identifies the stakeholders whose expertise, experience, or operational knowledge should shape the recommendation.
Decide identifies the single person or formal governing body with final authority.
The distinction between Input and Agree is especially important. A stakeholder may have valuable expertise without holding veto power. Another stakeholder may need to approve a specific issue, such as regulatory compliance or budget availability, without controlling the entire decision. RACI often leaves these differences open to interpretation. RAPID forces the organization to define them.
The central discipline is assigning the Decide role. Every significant decision needs a clearly named person or formal body that can review the recommendation, consider the required approvals, weigh competing interests, and make the final call. That decision authority must also be respected. If a senior executive can reverse the outcome simply because they were not included, the original decision process was incomplete. The executive either needed an Agree role, needed to provide Input, or should have held the Decide role from the beginning.
Clear decision rights matter more as companies delegate authority across functions and management levels. Slow-moving organizations can rely on informal escalation. Someone eventually takes the issue to a senior leader, and the hierarchy resolves the uncertainty. That approach breaks down when teams must act within hours or days. Employees need to know whose input matters, which approvals are mandatory, and who has the authority to proceed. Without that clarity, delegation creates delay because people receive responsibility without secure decision rights.
When several people hold the Decide role, the organization has not assigned final authority. It has created a process where delay, vetoes, and reversals are likely.
Market Pulse
project44 has split into two businesses, with project44 remaining focused on its Decision Intelligence Platform for enterprise shippers and a new spinout, LSP44, dedicated to AI-agent infrastructure for logistics service providers and carriers. The move separates enterprise decision applications from the underlying AI infrastructure, allowing project44 to concentrate on orchestrating AI agents that detect, decide, and act on freight procurement and supply chain disruptions, while LSP44 scales the infrastructure already used by many of the world's largest logistics providers.
MDaudit added Auditor Assist to its continuous risk monitoring suite for healthcare revenue integrity, an AI companion that reviews medical records and coded claims to assess coding integrity while the experienced auditor keeps control of the final call. The tool sits between provider and payer AI systems as a defensibility layer over machine-coded claims, learns from each auditor decision, and keeps every AI output sourced and traceable so findings hold up under payer and regulatory scrutiny. The company reports that payer audit volume and dollars at risk climbed 30% year over year, with coding-related denials up as much as 26%, and that coding errors account for nearly seven in ten completed denials in its analysis of 2026 payer audit activity.
Blend Labs announced the first commercial deployments of Autopilot, its AI-powered mortgage pre-underwriting agent, following validation across more than 25,000 loans. The platform reviews borrower documentation, checks it against lending guidelines, and generates follow-up requests in real time, reducing one of the industry's biggest bottlenecks by accelerating lending decisions outside traditional business hours.
Aerospike introduced a real-time agentic fraud detection platform built with Google Gemini and AMD EPYC processors, designed to reduce fraud investigation workflows by up to 90%. The platform combines automated risk scoring, AI-generated case context, and human review into a single decision workflow, enabling financial institutions to evaluate transactions in milliseconds while keeping analysts in control of final determinations. Customers include Barclays, DBS Bank, HDFC Bank, Experian, and PayPal.
Resources and Events
📅 Data 2030 Summit 2026 (Stockholm, Sweden - October 21-22, 2026)
Part of Nordic Data & AI Week, the Data 2030 Summit brings together data, analytics, and AI leaders to explore the governance, operating models, and data foundations needed to scale enterprise AI responsibly. Covering topics from data strategy and AI governance to modern data platforms and organizational transformation, the summit offers insights for leaders building decision-ready organizations. Details →
📅 DACH AI, Data & Analytics Insights Summit (Munich, Germany - November 26-27, 2026)
This two-day summit at the Leonardo Hotel Munich City East brings together data, analytics, and AI leaders to discuss data quality, agentic AI governance, centralized AI platforms, data products, analytics strategy, and AI-driven forecasting and personalization. Speakers represent organizations including Roche, Mercedes-Benz, Allianz, Lufthansa, DHL, Merck, TUI, SIXT, and Collibra. A useful regional counterpart to the UK- and US-centric events. Details →
📊 Report Spotlight: How AI Decision Agents Transform Strategy (BCG)
Drawing on BCG's AI Radar 2026 survey of roughly 2,400 executives, this report examines how AI can support strategic choices by assembling cross-functional evidence, testing scenarios in real time, and generating recommendations against explicit business rules. Unlike execution agents, decision agents operate at the point of choice, helping leaders evaluate tradeoffs in areas such as investment allocation, market entry, and integrated business planning. Their value comes from faster analysis, more transparent assumptions, and a shared factual base that limits selective inputs and unchallenged bias. Read →
The DecideWise Edge
Lusine Poghosyan, a founding member of the DecideWise community, examines how workforce planning is becoming the most valuable layer in facility management contracts. Asset owners are beginning to question why they should outsource the intelligence behind scheduling, resource allocation, and maintenance when they already control the operational data and can contract out execution separately. Real-time dashboards, optimization engines, and predictive systems have made planning visible, measurable, and easier to govern, but bringing it in-house requires connected data, optimization capabilities, and adoption by the teams using these tools. Poghosyan points to Atalian’s partnership with DecisionBrain to modernize workforce scheduling through real-time optimization and argues that planning intelligence will define competitive advantage in the sector.
For the Commute
DecisionOps, Testing, and Iterative Development (Decision Intelligence Lab)
Carolyn Mooney, Co-Founder and CEO of Nextmv, joins Vijay Mehrotra and Michael Watson to unpack DecisionOps, her term for treating decision systems the way modern software teams treat code: versioned, tested, and iterated on continuously. Mooney argues that most organizations still treat their decision logic (routing rules, scheduling constraints, allocation policies) as something that gets built, shipped, and rarely revisited, even as the business conditions it was built for keep shifting underneath it. The conversation covers what it actually takes to test a decision system before deploying a change, why simulation matters as much for decisions as it does for engineering, and why iterative development, standard practice in software, is still rare in how organizations manage the rules driving their day-to-day decisions.
