Editorial
As the New Year approaches, CIOs are grappling with how to gauge the success of their AI initiatives, as revealed in a recent Gong survey of over 500 IT leaders.
While AI projects hold immense potential, the path to proving their value is far from certain. Author of "Driving Digital" and former Greenwich Associates CIO Isaac Sacolick observed, “There is clearly a lot of exploring and piloting — likely 10 times more than production deployments or scenarios where GenAI is firmly embedded into workflows.”
These facts — confirmed by Gartner and Salesforce Research — highlight a pivotal challenge: balancing experimentation with measurable outcomes.
Measuring the Success of AI Projects
CIOs say their organizations are placing high stakes on artificial intelligence, with productivity gains (53%) and revenue growth (53%) emerging as the most important benchmarks of success, according to Gong’s recent research. Worker satisfaction follows closely at 46%, reflecting a growing recognition of the human factor in AI adoption.
Despite these focuses, many organizations lack the tools to measure AI’s impact, leaving a gap between intention and execution.
The Gong study revealed that 61% of global CIOs believe increased revenue alone justifies the cost of AI investments, while 60% see time savings as a better justification. Yet only 32% actively measure both revenue and time savings, suggesting that most companies are not fully equipped to evaluate their AI initiatives against the metrics they consider critical. This disconnect underscores a need for better systems to capture and analyze the data necessary to demonstrate ROI.
When it comes to AI priorities, GenAI leads the way, with 54% of tech leaders focusing here. Automation (51%) and predictive AI (31%) are also top priorities, reflecting the varied use cases organizations are exploring. However, as AI adoption accelerates, success will depend on developing robust frameworks to track not just outcomes, but the tangible value AI delivers across these domains.
Nevertheless, to be successful, Sacolick said, “CIOs should work backwards: What's the objective? How do you make your results actionable? What are the benefits and risks? Then, you can start planning the most valuable and lower risk use cases.”
Tailoring AI to Business Goals
To bridge the gap between lofty AI ambitions and measurable outcomes, organizations must tune AI models to address specific business needs like workflow automation and predictive analytics. By aligning AI capabilities with practical applications, businesses can extract tangible value and improve operational efficiency.
Jim Russell, CIO of Manhattanville University, emphasized the foundational role of data in achieving these goals. “Robust data governance is essential,” he noted. “There needs to be a big tent approach to managing data standards and improving organizational data literacy and fluency. A mature practice will help both the technical and human side of your data assets.” This highlights the need for a cohesive strategy that empowers both systems and people to make the most of AI investments.
While nearly three-quarters of tech leaders rely on off-the-shelf large language models (LLMs), 58% are turning to domain-specific solutions. These tools, trained on industry- and function-specific data, enable more precise and measurable results, making them particularly attractive for businesses seeking to prove ROI.
By combining robust governance practices with targeted AI applications, organizations can position themselves to deliver meaningful outcomes and capitalize on the full potential of AI.
Related Article: Report Examines AI Readiness and ROI
Obstacles to AI Success
Security remains a pressing concern for 68% of tech leaders, with many prioritizing it as a top focus in their AI initiatives. Yet, 28% admit that security is where their AI projects most frequently fall short, underscoring a significant gap in implementation. Without the right frameworks and safeguards, organizations risk compromising sensitive data or, worse, deploying flawed AI models that erode trust and credibility.
Eric Johnson, CIO of PagerDuty, highlighted the importance of addressing these vulnerabilities early. “As a CIO, ensuring the privacy and security of data is a critical responsibility, especially as we implement AI solutions. CIOs need to develop solid frameworks for data management and security. This often involves working with cross-functional teams, including compliance, risk, legal and security departments. The goal should be to ensure that as we leverage data for AI, we're doing so in a way that protects privacy and maintains security while complying with relevant regulations.”
His comments point to the growing complexity of managing AI projects in a highly regulated and security-conscious world.”
Beyond security, data quality is another challenge that can make or break AI outcomes. Transition CIO Martin Davis emphasized the critical importance of trustworthy data. “Knowing the provenance of the data — where it came from, its validity and its source reliability — is critical,” Davis said. “Using unverified or poor-quality data can lead to flawed AI outcomes, encapsulated in the adage: ‘AI + bad data = Bad AI.’” This highlights the need for robust data governance to ensure AI models are fed with high-quality, unbiased inputs.
Despite these challenges, optimism remains high. Eilon Reshef, co-founder and chief product officer at Gong, observed, “One thing is clear: leaders are pursuing value and exploring different areas across the business where AI can have a transformative impact.” As organizations refine their approaches to security, data quality and governance, the potential for AI to drive measurable value across industries becomes increasingly within reach.
Related Article: The Need for Quality Assurance in the AI Rush
Parting Words: The Road Ahead for AI in Business
As organizations continue to invest in AI, defining success becomes a strategic imperative. While productivity, revenue growth and time savings dominate the metrics CIOs value most, the gap between ambition and measurement persists. Success in AI requires more than experimentation; it demands a clear vision, robust data governance and a commitment to aligning technology with tangible business outcomes.
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Security and data quality remain key challenges, but they also present opportunities for CIOs to lead transformative change. By addressing vulnerabilities in data management and fostering cross-functional collaboration, IT leaders can ensure their AI initiatives are both secure and effective.
As Johnson noted, this includes protecting privacy, maintaining compliance and enabling trustworthy AI outputs — all of which are essential to delivering value while preserving stakeholder confidence.
Ultimately, the path forward lies in building AI systems that are not only innovative but also actionable and sustainable. This means focusing on domain-specific solutions, embedding AI into workflows and prioritizing use cases with measurable impacts. As Sacolick advised, CIOs must plan strategically, balancing risk with potential rewards. By taking these steps, organizations can move beyond piloting AI projects to scaling solutions that drive real, transformative outcomes across the business.
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