Many enterprises rush into AI projects without establishing clear frameworks for measuring success. While the discipline promises significant returns, companies often struggle to demonstrate tangible business value from their investments. William McKnight, a global consultant who has worked with Fortune 100 companies worldwide, offers practical insights on bridging this gap between AI experimentation and measurable ROI through strategic project setup and focused implementation.
How to Find the ROI from AI in the Enterprise
Getting ROI from AI isn’t as straightforward as many executives think. Companies pour money into AI projects, run experiments, and then wonder why they can’t show concrete returns to their boards. McKnight has seen this pattern play out countless times across different industries and company sizes.
The Framework Problem
Most organizations approach AI backwards. They start with the technology and hope the returns will follow. “Often the project framework is not set up to show the ROI,” McKnight explains. “It’s something that you need to go into the project thinking about how you’re going to produce those returns.” This isn’t just about having good intentions or expecting positive outcomes. You need actual structures in place that track how AI impacts your bottom line. McKnight puts it bluntly: “You need the structural framework around the ROI that takes you all the way to how it’s going to affect cash flow because you need the numbers to demonstrate real returns.” Without these numbers, you’re just guessing about whether your AI investments make sense. The challenge gets tougher when leadership starts asking hard questions about project justification. ROI doesn’t come from feel-good metrics or intangible benefits. It comes from real returns that affect the business financially, minus what you spent to get those returns, including the people you hired.
Seeing Long Term Value in AI
Despite the measurement challenges, McKnight remains bullish on AI adoption. “There are numerous examples of great ROI being produced from AI-based projects. AI is definitely a keeper,” he says. The technology has proven itself across multiple business functions, but success requires the right approach. The key insight is that business goals haven’t shifted, but the methods to reach them have changed dramatically. “The goals of the enterprise haven’t changed, but the methods to get there have changed with AI,” McKnight notes. This creates opportunities to examine entire operations through an AI lens, looking for areas where the discipline can drive both efficiency and effectiveness. Companies should take a zero-based approach across their enterprise when considering AI applications. The possibilities have reached a point where AI can productively impact many business areas, making this comprehensive review worthwhile.
Identifying Factors Driving AI Success
The companies that actually achieve ROI from AI share one key trait: they know exactly where their returns come from. “The winners know where the returns are coming from and they target those returns to the business,” McKnight observes. This focus becomes critical when projects face scrutiny or budget cuts. Even successful AI projects can fail to get recognition if they haven’t established clear connections between their technology work and business outcomes. Sometimes projects actually produce ROI, but if the team hasn’t set up proper tracking, it becomes harder to prove that connection is real. McKnight’s advice is straightforward: “Set up the projects with ROI from the beginning so that not only do you have something to focus on, but you have a story for the tangible value that the project is creating for the business.”
Setting Clear ROI Metrics Early
ROI requirements vary significantly between companies. “The specific ROI number that you need to meet in order for it to be successful is going to be different in every company,” McKnight explains. The cost of money differs across organizations, and AI projects need to beat whatever return the company is already getting on their investments. Projects also compete with other potential investments, whether they use AI or not. Your AI initiative needs to promise better returns than alternative projects to secure resources and support. But there’s another factor that can tip the scales: if AI represents a strategic direction for the business, that can work in favor of AI projects. He sees AI delivering measurable returns across numerous business areas. “There are many examples of this in areas such as customer service, drug discovery, financial research, security, predictive maintenance, supply chain, healthcare for diagnosis and treatment, cyber security and risk management, content creation and so on.” The real power of AI lies in its ability to use comprehensive data for decision-making. “What AI can do is leverage all manner of data that it can reach and apply that information to any decision or any outcome that’s desired, any customer interaction,” McKnight explains. But there’s a catch. Your data infrastructure needs to be solid for AI to work effectively. The data needs to be of high quality, accessible, and organized within reasonable architectures. Without proper data management, AI becomes much more difficult to implement successfully.
Connect with William McKnight on LinkedIn or check out his website to explore more about AI ROI strategies.
