Leaders feeling pressure to deploy AI should focus it internally first. Using AI to enrich and manage product data catalogs is a low-risk, high-reward application that improves efficiency and builds the necessary foundation for future, more complex customer-facing AI features.

To achieve clear ROI on AI initiatives, enterprises should focus on business problems that are already being measured. Without baseline performance metrics (like call volume or software delivery speed), it's impossible to quantify the improvement an AI system provides, often leading to "POC purgatory."

The 1 in 5 companies succeeding with AI target internal workflows where performance is already measured. This allows them to clearly attribute metric improvements to AI and calculate ROI, while also lowering data security risks compared to customer-facing applications.

Businesses should prioritize AI projects that can completely automate a recurring workflow. Transforming a multi-week manual process into an instantaneous one delivers transformative value, far exceeding the gains from projects that only offer partial assistance to a human user.

When launching internal AI tools, don't fixate on immediate ROI, which is a lagging indicator. Instead, monitor user adoption rates. A rapid increase in adoption is the strongest signal that a tool is genuinely solving a problem and that positive business outcomes will eventually follow.

The path to enterprise AI adoption follows a typical change curve. To bypass initial fear and rejection, organizations should first apply AI to transform familiar, high-friction workflows. This strategy builds momentum and demonstrates value before tackling entirely new, innovative business models.

Companies struggle to measure AI's return on investment because its value often materializes as individual productivity gains for employees. These personal efficiencies, like finishing work earlier, don't show up on corporate dashboards, creating a mismatch between perceived value and actual impact.

Quantifying the ROI of AI tools is difficult for creative product discovery. Instead, focus on a more measurable application: internal operations. By automating repetitive workflows like data extraction and reporting, you can calculate a clear ROI based on hours saved and operational efficiency gains.

Calculating the ROI of an AI initiative as a simple tool investment is a mistake. The true ROI comes from transforming entire systems and processes. This requires measuring the impact on organizational change, new workflows, and strategic alignment, which is far more complex than a basic software cost-benefit analysis.