For AI Value, Focus on Your Use Cases

In the 2024 Gartner AI Survey, 57% of CIOs said that they are tasked with leading their organization’s AI strategy, but to define and deliver on AI outcomes, you must first understand which race you’re running — and at what pace.

Tech vendors are driving and financing much of the current hype over AI as they relentlessly innovate and flood the market with AI-embedded technologies. As a CIO, you’re not in that race; you are in a race to deliver AI outcomes safely and at scale. 

The Gartner AI Roadmap Tool maps a slew of AI workstreams to help you turn your AI ambitions into valuable, feasible use cases that you can implement. Use a framework like this to make sure you track your AI strategy path, from ideation through AI readiness to successful execution. 

Also make sure to pace yourself on your path to scaling AI:

• Move at an AI-steady pace if you’re focused on using GenAI to maximize productivity gains for individual employees.

• Move at an AI-accelerated pace if you’re aiming to use GenAI to deliver additional business outcomes beyond individual productivity.

You may also like this webinar: Measure and Realize Value From AI Investments: Impactful Stories.

Whatever pace you’re pursuing, you will want business outcomes like gains in employee productivity and streamlined business processes, and technology outcomes that protect the organization’s data and govern AI outputs while still providing enough flexibility to capitalize on new opportunities.

But CIOs need to consider a third factor: behavioral outcomes.

As AI goes from a tool to a teammate, humans are bound to have intense reactions. Some employees may feel a strong affinity for AI. Others may feel threatened or resentful. 

These deeply human, emotional reactions to AI can lead to unintended behavioral outcomes that negatively impact employee performance. CIOs need to use AI in a way that encourages positive behaviors — and they must manage the impact on employees intentionally.

The 2024 Gartner AI Survey results suggest no one is yet doing a great job of this. Just one in five CIOs said they focus on mitigating the potential negative impacts of AI on either employee work or well-being.

At Gartner, we urge clients to manage behavioral outcomes with the same rigor they do technology and business outcomes. If you’re moving at an AI-steady pace, for example, adapt your change management approaches to focus on employee behaviors and be intentional about who owns which behavioral outcomes. Otherwise, you’re inviting accidental ownership of key outcomes.

Pinpoint high-impact AI opportunities with Gartner’s AI Use Case Insights for IT Leaders. Discover, evaluate, and prioritize AI opportunities to accelerate IT transformation and demonstrate value to the business.

Productivity can be difficult to measure and even more difficult to “bank.” Consider generative AI: It has the potential to revolutionize productivity, but not all workers will benefit equally.

Mainstream assumptions suggest that GenAI either lifts the performance of only less-skilled workers or uniformly escalates everyone's productivity. In reality, if two people in the same role use the same GenAI tools, one may become more productive and the other less so. 

Gartner research surfaced two key factors that you may not have considered in your productivity equations: career experience and the complexity of the function in which GenAI is deployed. 

The Gartner Deep Productivity Matrix tool shows a zone of “deep productivity” stretching from low experience/low complexity to high experience/high complexity. Map the zone for your organization to identify where employees will get the biggest benefit from using GenAI.

• For low-complexity roles, focus on augmenting less experienced employees with GenAI. For roles such as call center agents, less experienced employees get the biggest productivity gain from GenAI because they are less adept at performing routine tasks. Highly experienced employees in these roles get little benefit because they have already mastered the job.

• For high-complexity roles, focus on augmenting more experienced employees with GenAI. For roles such as software engineers or lawyers, these employees get the biggest productivity gain because they know what good looks like and can validate GenAI outputs effectively.

AI cost is one of the greatest near-term threats to AI and generative AI success. More than half of organizations abandon their efforts due to cost-related missteps, and Gartner warns clients that the cost of AI is as big of a risk as hallucinations or security vulnerabilities to their AI strategies.

It’s also easy to waste money on GenAI, because costs can be so unpredictable. If you don’t understand how your GenAI costs will scale, Gartner estimates that you could make a 500% to 1,000% error in your cost calculations.

In the 2023 Gartner AI in the Enterprise Survey, organizations that had already deployed GenAI reported spending an average of $2.3 million in fiscal year 2023 — just in the proof-of-concept phase. Small enterprises reported spending an average of $300,000, while large global enterprises reported spending $2.9 million.

These costs will only continue to rise. Gartner predicts that by 2027, the cost of most enterprise applications will increase by at least 40% due to GenAI product pricing and packaging.

Regardless of your pace in the AI outcomes race, understand your AI bill. If you’re moving at an AI-accelerated pace, continuously monitor your AI costs, including understanding your AI pricing model options. For example, you may find that using an API with your own web front end could be far more cost-effective than buying a packaged GenAI product.

To discover potential benefits and effectively manage costs, use proofs of concept to understand how costs will scale. It’s not enough to prove that the tech works and employees like it. Use the proof of concept as a proof of value — in other words, weigh the benefits achieved against the AI costs incurred.