AI value depends on closing cost, cybersecurity and trustworthiness gaps. Here’s how CIOs can take action.
- Gartner client? Log in for personalized search results.
Scaling AI exposes hidden risks to value
CIOs face a stark reality: AI won’t deliver value at scale until you close three critical risk gaps — cost, cybersecurity and trustworthiness. Gartner Distinguished Vice President Analyst and Chief of Research Rita Sallam explains, “Piloting AI in limited, controlled scenarios creates a dangerous illusion of safety, masking the severe operational, financial and security vulnerabilities that will inevitably explode at scale.”
Once you move from pilot into production, unmanaged risks can sabotage both your budget and your reputation. Gartner insights show only 23% of CxOs report confidence in their organization’s GenAI outputs. If you miss even one risk pillar, you fail to protect the value you intend to create.
To realize AI value at scale, close these risk gaps
Scaling AI requires more than technical deployment. It demands a new operational playbook to manage risks that compound as adoption grows. Here’s how each pillar can either undermine or unlock enterprise value.
Cost risk: Why AI cost optimization is mandatory
Most organizations underestimate AI’s true cost. While model prices might seem to drop, your cost per completed task keeps rising. Usage-based pricing, reprompting and token waste drive up expenses, often depleting savings. Gartner data shows only one in five organizations realizes high ROI from their AI initiatives. The most satisfied organizations invest four times more in data, governance and people than those that are least satisfied. To protect value, dedicate at least twice as much budget to these foundations as you spend on AI tools.
Cost optimization is an architectural requirement. Build controls into agentic workflows: Match models to tasks, route simple queries to cheaper models and use smaller, domain-specific language models when possible. Break tasks into reusable parts, and set dynamic budgets and safeguards. Mandate AI token financial literacy for all users. Don’t wait for perfect AI FinOps tools that are currently immature — deploy active cost prevention now, including usage quotas and strict access boundaries.
Cybersecurity risk: Why legacy defenses can’t enable scale
Scaling AI accelerates cyberthreats. Autonomous agents expose new vulnerabilities overnight, and attackers use the same tools to automate reconnaissance and exploitation. Gartner insights show vulnerability reports have surged nearly 500% on major platforms. Legacy defenses can’t keep up.
CIOs must engineer active defenses into agentic workflows. Lock down APIs and sensitive data, enforce strict access boundaries and use machine identity management. Deploy automated guardian agents as they mature. Shift focus from pure prevention to damage control and fast recovery. Invest in advanced tools, but because they often cost multiples of base frontier models, restrict their use to the highest-risk cases. Prepare for independent certification and risk insurance as markets evolve.
Trustworthiness risk: Why policy must become code
AI agents become dangerous the moment their outputs lose predictability or accountability. Traditional governance can’t keep pace with autonomous systems. AI engineers are now your first line of defense. They must translate policy directly into code.
Business context is your last true moat. Agents need a deep understanding of your rules, data and real-time operations to act reliably and cost-effectively. Rich context layers can cut reasoning token use by two to three times and boost accuracy up to 50%. Still, human oversight remains essential. Mandate operational stewardship and upskilling for decision makers to ensure sound judgment in automated decision workflows.
Action steps: What executives must do to close the AI value gap
For CIOs: Mandate policy as code. Replace manual checklists with machine-executable controls to govern operational risk and engineer active cost prevention directly into agentic workflows.
For CISOs: Engineer continuous, active defenses and shift your strategy away from pure prevention to prioritize damage control and fast recovery.
For CDAOs: Build your context layer immediately on top of a high-quality data foundation to ensure that agents can reason safely and accurately. Deploy human decision stewards to oversee critical workflows, tying their performance directly to the integrity of the agent’s outcomes.
For CFOs: Prioritize transformative top-line growth use cases. Fully account for the heavy foundational investments required to derisk these systems. Rigorously simulate and assign probabilities to extreme variances to protect your enterprise value.
For CHROs: Treat AI token financial literacy as a mandatory foundational skill across all departments, and develop dedicated upskilling paths for human decision stewards.
For assurance leaders: Collaborate with IT leadership to translate risk management and regulatory rules into executable code. Mandate human accountability for all autonomous operations.
For technology providers: Compete on absolute trustworthiness by offering transparent trust metrics, embedded governance capabilities, real-time agentic AI FinOps tools for cost and value management, and seamless integration into client context layers.
AI value gap FAQs
What is the AI value gap and why should CIOs care?
The AI value gap is the difference between potential AI value and what you actually realize at scale. Gartner insights show this gap is really a risk issue tied directly to cost, cybersecurity and trustworthiness. If you fail to close even one, you risk budget overruns, security breaches or unreliable outputs. CIOs must lead on closing these gaps to protect and scale enterprise value.
How can CIOs optimize AI costs while scaling AI?
CIOs must treat cost optimization as an architectural requirement, not a finance afterthought. Build controls into workflows, match models to tasks and use smaller models for simple queries. Invest at least twice as much in foundational elements — data, governance, people — as you do in AI tools. Mandate AI token financial literacy for all users to prevent budget drain.
Why does policy as code matter for closing the AI value gap?
Policy as code means translating governance and compliance rules directly into machine-executable controls. As AI agents scale, manual checklists can’t keep up. Your AI engineers become the first line of defense, ensuring agents act within business rules. This shift is critical for closing the trustworthiness risk and protecting enterprise value.
Attend a Conference
Experience CIO and IT Executive conferences
With exclusive insights from Gartner experts on the latest trends, sessions curated for your role and unmatched peer networking, Gartner conferences help you accelerate your priorities.
21 – 23 Sep 2026
Gartner CIO & IT Executive Conference
São Paulo, Brasil
Drive stronger performance on your mission-critical priorities.