Tech ROI: Why Your People Matter More Than Your Tools

CFOs expect technology investments to pay off. Whether those tools reduce costs or drive revenue growth, there needs to be a clear ROI. This focus puts the CFO at the heart of their organization’s digital transformation efforts. Focusing solely on technology isn’t enough to ensure optimal ROI, however.  

Companies have been pouring money into AI in hopes of solid eventual returns on that spend, but more than half of CEOs haven’t realized returns yet.

AI initiatives require more than tech. They need a people-centric strategy for upskilling, process redesign, SME oversight, and change management. Finance leaders who follow best practices for managing the human side of AI and other technology initiatives can improve adoption, leverage, and ROI.

Slow implementation to accelerate transformation 

Moving quickly to implement new technologies may seem like the logical approach for fast time to value. But rushing implementation of AI-based transformations forces shortcuts that often end up costing more time and resources than a methodical, strategically paced implementation would. That’s because people and process readiness are the foundations of technology ROI, and they require time up front.

The push to move fast is especially common at startups looking to scale quickly and companies trying to catch up to a more technologically mature competitor. For example, a FinTech in hypergrowth mode might rush to orchestrate billing, reconciliation, and reporting across multiple platforms to cut labor costs and get the work done faster. If they simply layer their new technology on top of existing processes, they’re wasting the potential to fully leverage their agentic AI investment and setting their people up to fail.

Process redesign precedes automation 

Automating inefficient processes just creates faster inefficiencies, which can leave employees to deal with problems that are now piling up faster than ever because of automation. That’s a recipe for low productivity and disengagement. On the other hand, if our example FinTech company precedes its AI implementation with a comprehensive rethinking of its existing processes, it creates multiple pathways to better ROI.

• A redesign audit can identify inefficiencies in existing processes and data sources so they can be corrected instead of automated. This will save employee time on correcting automated problems after the implementation.
• Redesign sessions with all relevant stakeholders can lead to a complete overhaul of existing processes to fully leverage AI capabilities. This can result in better results from automation and orchestration projects.
• Redesign requires a fresh look at governance. Are the organization’s guardrails and explainability policies aligned with the new processes? Addressing governance considerations before implementation can result in more accurate outputs that employees can trust without manual review.
• Properly designed AI processes create less, not more, work for employees. For example, the hypothetical FinTech can reduce audit requests with a well thought out redesign of its processes before orchestrating them with AI agents. That creates opportunities for its employees to engage in other tasks that support the startup’s push to scale.

Digital fluency is a prerequisite for change 

Better-orchestrated processes drive ROI, but the potential for greater value doesn’t stop there. Your employees and managers need to use the new tools to their fullest extent. That requires intentional digital fluency development across the organization. Without data literacy and data storytelling skills, leaders and employees alike may avoid adopting the new tools you’ve invested in. If they stick to old tools or manual validation of all AI-generated results, fragmentation and work slowdowns are the result. If users don’t understand how to extract insights from new dashboards, value stays locked up and unused.

CFOs can help unlock that value by supporting data and analytics training across the organization. They can also advocate for embedding subject matter experts in teams during new technology rollout periods to help everyone get comfortable with using the new tools.

Build trust into new processes 

Understanding how to use technology builds confidence in the results, which reinforces adoption and encourages users to develop insights based on those results. Leaders, managers, and process owners also need to know how AI tools create their outputs and what rules govern the process. This builds trust in the results and can reduce the need for audits. Looping stakeholders into the planning stage makes all of this easier to accomplish.

For example, consider a bank that wants to modernize its close process. If employees and managers don’t understand how the AI automated close process generates results, they’re likely to ignore that output and stick with the old way of doing things. Instead, the bank can choose to redesign its close workflow with input from employees who’ll be using it. This strategy can result in better design and higher adoption because employees will know where the data comes from and how outputs are achieved. They’ll also know which controls are in place to govern those outputs.

Focus on leveraging talent with technology 

People’s understanding of new technology and processes is the key to maximizing return on tech investments in general and AI investments in particular. Whether your organization is considering new technologies or seeking to increase return on existing AI initiatives, a people-first approach to process design, digital fluency and change management can improve outcomes.