As artificial intelligence becomes increasingly embedded in enterprise operations, business leaders are facing a critical challenge: how to accelerate AI adoption without allowing risks to outpace the value it delivers. Addressing this growing concern, Gartner has introduced its Value-Risk Flywheel framework, designed to help Chief Information Officers (CIOs) make more informed decisions about AI investments, governance and organisational transformation.
The framework offers a structured approach to understanding how AI creates business value while introducing new operational, technological and governance risks. It emphasises that successful AI adoption is not simply about moving faster or deploying more advanced technologies. Instead, organisations must understand which forces accelerate progress and which constraints help keep innovation under control.
Gartner’s Value-Risk Flywheel: A New Approach to AI Decision-Making
According to Gartner, the Value-Risk Flywheel revolves around two fundamental forces that influence AI outcomes: momentum and drag. Both can have positive or negative consequences, depending on how organisations manage them.
Momentum represents the forces that accelerate AI adoption, improve productivity and generate business value. However, excessive or poorly managed momentum can also create problems, particularly when organisations rush to implement AI systems without sufficient oversight, security measures or accountability.
Similarly, drag refers to factors that slow down progress. While unnecessary bureaucracy and inefficient processes can restrict innovation, certain forms of drag are essential for responsible AI development. Governance checks, security assessments, human supervision and risk evaluations can prevent organisations from making costly mistakes.
Daryl Plummer, Distinguished VP, Managing VP and Chief of Research at Gartner, highlighted the importance of maintaining this balance.
“The best thing a CIO can do is to align value with risk and align safety with governance,” Plummer said.
He explained that high-performing CIOs understand how to generate positive momentum while deliberately introducing beneficial drag. Their approach involves reducing harmful momentum and unnecessary obstacles while preserving the controls required to ensure sustainable AI adoption.
Why Balancing AI Momentum and Risk Matters
The growing influence of AI across business functions has made this balance particularly important. Organisations are increasingly deploying generative AI and autonomous systems to improve efficiency, automate repetitive activities and accelerate decision-making.
However, faster implementation does not automatically translate into better business outcomes. Without appropriate governance, organisations may encounter data security vulnerabilities, unreliable AI-generated outputs, compliance challenges and accountability gaps.
Gartner’s framework encourages CIOs to evaluate AI initiatives beyond their immediate productivity benefits. They must also consider whether the technology is creating measurable business value and whether the associated risks remain manageable.
Software development provides one example of how the framework can work in practice.
AI-powered coding tools can generate substantial portions of software code, reducing the time developers spend on manual programming. This allows engineering teams to concentrate more on rapid prototyping, requirements-driven development, architectural planning and product engineering.
In this situation, AI creates positive momentum by accelerating development cycles and improving productivity. At the same time, organisations must introduce appropriate safeguards, including code reviews, security testing and quality assurance, to ensure that faster development does not compromise software reliability.
The Role of a Centralised AI Decision Authority
Another important element of Gartner’s Value-Risk Flywheel is the concept of a centralised decision-making authority, described as the framework’s governor.
This governor sits at the centre of the flywheel and determines when the potential business value of an AI initiative justifies the risks involved. It also establishes which organisational priorities should guide decisions and how businesses can maintain appropriate levels of safety and accountability.
The concept highlights the need for organisations to move beyond fragmented AI decision-making, where individual departments independently adopt technologies without a unified governance strategy.
Kristin Moyer, Distinguished VP Analyst and Gartner Fellow, described the governor as a centralised decision authority responsible for driving advice and guiding actions related to accountability, technology and economics.
Such an authority can help businesses establish consistent AI policies, assess investment priorities, allocate resources and determine acceptable risk thresholds. It also ensures that AI adoption remains connected to broader business objectives rather than becoming a collection of disconnected technology experiments.
Gartner Survey Highlights the Growing AI Governance Gap
The introduction of the Value-Risk Flywheel comes amid rapidly increasing interest in agentic AI across enterprises.
A Gartner survey conducted in the second quarter of 2026, involving more than 2,000 CIOs and technology executives, found that 71% of organisations expect to deploy agentic AI within the next 12 months.
However, only 21% of organisations have established governance mechanisms for agentic AI.
According to a Gartner Q2 2026 survey of more than 2,000 CIOs and technology executives, these are separate survey findings.
The findings reveal a considerable gap between enterprise AI ambitions and governance preparedness. While businesses are eager to introduce autonomous AI systems capable of performing tasks and making decisions with limited human intervention, many have yet to establish the necessary oversight structures.
According to Moyer, this imbalance makes it increasingly difficult for organisations to generate business value faster than AI-related risks are growing.
Agentic AI introduces additional considerations because these systems can independently execute workflows, interact with enterprise applications and make decisions based on their assigned objectives. Without clearly defined boundaries, monitoring systems and accountability structures, organisations may struggle to maintain control over their operations.
CIOs Face a New Responsibility in the AI Era
Gartner’s Value-Risk Flywheel reflects a broader shift in the responsibilities of technology leaders. CIOs are no longer expected to focus exclusively on technology infrastructure, digital transformation or IT efficiency. They are increasingly responsible for ensuring that emerging technologies deliver tangible business outcomes while supporting organisational resilience.
As AI adoption expands, CIOs must bring together technology teams, business executives, risk management professionals and governance specialists to establish a common approach to implementation.
The framework also reinforces an important principle: responsible AI governance should not be viewed as an obstacle to innovation. When designed effectively, it can create the confidence and structure organisations need to adopt AI at scale.
Ultimately, Gartner’s Value-Risk Flywheel provides CIOs with a way to rethink how their organisations approach AI transformation. Rather than pursuing speed at any cost, businesses must learn to distinguish between productive momentum and unnecessary acceleration, while recognising when additional controls are essential.
As enterprises move towards increasingly autonomous AI systems, the organisations most likely to succeed will be those that can turn technological momentum into measurable business value without losing sight of risk, accountability and trust.
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