The ROI of AI: Why impact > hype

“Don’t start with what AI can do. Start with what your business needs to do better.” 

That quote captures the most important lesson I’ve learned from working closely with dozens of organizations implementing AI. While the headlines obsess over the latest breakthroughs in generative AI or agent-based models, the real question executives should be asking is: How will this help us solve the problems that matter most to our business?

We’re at a turning point. AI is no longer confined to innovation labs or proof-of-concepts. It’s being embedded in operations, products and customer experiences across every industry. But for all the excitement, many companies are still struggling to extract real value. Too many AI initiatives start with the tools, not the outcomes. And when that happens, hype overwhelms impact.

I want to share what I’ve seen work — and not work — when it comes to driving ROI from AI investments. I’ll draw from real-world customer experiences, third-party research and my own observations, helping organizations align AI to business goals. The good news? When companies focus on outcomes, not just algorithms, AI delivers extraordinary returns. 

The problem: When AI becomes a distraction

AI can be a powerful enabler, but only when deployed with intention and purpose. Too often, companies rush into AI projects without a clear problem to solve. The result? Initiatives that lack a path to production, are owned by no one and deliver little to no value.

I’ve seen the same failure patterns repeat: AI pilots that never scale, fragmented and disconnected tools introduced without alignment to existing processes and impressive demos that quickly gather dust. Research confirms this trend: many AI projects fail to produce ROI because they aren’t anchored to measurable business outcomes.

A better way: Start with outcomes, not algorithms

AI projects should begin not with the tool, but with the business problem. A more effective approach starts by defining the desired outcome and working backward to determine where AI can make a meaningful impact.

When evaluating potential AI initiatives, organizations should ask two core questions:

• First, understand the business impact. Will this improve speed, reduce cost, increase accuracy or enhance customer experience?
• Next, evaluate the business differentiation. Will it give us a competitive edge by enabling something better, faster or more intelligent than the status quo?

The most compelling opportunities lie at the intersection of operational efficiency and strategic differentiation. These aren’t proof-of-concepts; they’re business accelerators that deliver real value aligned against your strategic outcomes. Whether it’s shortening decision cycles, improving customer response times or optimizing resource allocation, the value lies in applying AI where it enhances performance and sets the business apart.

AI shouldn’t be deployed just to tick an innovation box. Its purpose is to eliminate friction, unlock new value and reinforce the workflows that matter most. When organizations begin with a clear understanding of the outcomes they want to achieve, they can move beyond tactical wins and toward scalable, sustained impact. That outcome-first mindset is what separates AI hype from genuine ROI.

The ROI of doing it right: What the data says

Recent research from Nucleus Research provides concrete evidence of the ROI possible when AI and no-code automation are tightly aligned to business priorities. Based on interviews with enterprises, Nucleus found that organizations adopting this approach achieved substantial and measurable business results.

Organizations reported an average 37% reduction in total technology costs, driven by simplified integrations, reduced IT overhead and a more predictable pricing structure. These cost savings were complemented by a 70% reduction in implementation timelines, allowing organizations to go live faster and realize value sooner compared to traditional platforms.

Operational efficiency also improved significantly. One key area was lead management: customers cited a 61% decrease in lead response times, supported by real-time routing and automation, which led to an 11% average increase in conversion rates. In parallel, AI-enabled workflow automation reduced manual data entry by 17%, freeing up employee time and increasing productivity.

Perhaps most importantly, customers reported that these gains helped them become more agile in responding to market conditions and sustaining continuous improvement, reinforcing that AI success is not just about savings, but about enabling scale, speed and adaptability across the business.

The organizations that follow these 5 principles maximize AI ROI

The difference between hype and impact often comes down to execution. In my experience, the organizations seeing the strongest ROI from AI share five habits:

1. Start with a business goal

Before you write a line of code, align AI with a specific operational outcome

The most successful AI initiatives start with clarity. That means defining exactly what needs to change, whether it’s reducing customer churn, speeding up internal workflows, improving forecasting or enhancing user engagement. Without a clear goal, even a technically sound AI solution may fail to gain traction.

I always encourage teams to avoid jumping straight into building or buying solutions. Instead, pause to align on KPIs. What will success look like? How will we measure improvement? That clarity keeps projects grounded.

Example: A sales organization wanted to improve forecasting accuracy and reduce the time spent on manual pipeline updates. By applying AI use against these priority outcomes, they began by having AI analyze sales activity data and automatically score deal likelihood, they reduced forecast variance by 25% and freed up reps to spend more time selling.

2. Don’t automate for the sake of it. Target friction

Prioritize augmenting high-friction processes, don’t chase novelty

Not every process needs AI and not every AI use case creates real value. The best returns come when AI addresses bottlenecks that were previously too manual, error-prone or inconsistent. That’s where AI adds tangible speed, scale and intelligence.

A good litmus test is this: If a process already runs smoothly and quickly, automating it with AI may yield minimal ROI. But if it involves repeated back-and-forth, time-consuming review or judgment-based decisions, AI can drastically improve throughput and consistency.

Example: Marketing teams often have access to large amounts of fragmented data but lack the ability to rationalize it and analyze it effectively.   This missed opportunity led a bank’s marketing team to use AI to optimize campaign targeting by analyzing historical performance and real-time engagement data. The result was a 20% increase in click-through rates and fewer wasted impressions across digital channels.

3. Make AI transparent, trackable and tied to metrics

Start with explainable, measurable use cases and track improvements

The ability to track AI’s contribution isn’t just important for ROI reporting — it’s essential for trust. Business users are more likely to embrace AI when they understand what it’s doing and why. This means surfacing decision logic, offering override options and building a feedback loop.

At the same time, measurement must be built in from the beginning. Don’t wait until after launch to define success criteria. Know upfront how you’ll measure efficiency gains, quality improvements or time saved.

Example: A customer service team for a regional manufacturing firm implemented AI to suggest next-best responses and assist with case summarization. By measuring reduction in average handle time and improvements in first contact resolution, they built internal confidence in the use of AI models and justified broader rollout.

4. Think beyond the pilot. Design for real-world use

Ensure adoption through UX + training and not just deployment 

AI must be easy to use and deeply integrated into the tools people already rely on. That requires thoughtful UX and a rollout plan that includes not only training, but context: why the AI exists, how it helps and what users can expect.

Too many AI pilots fail not because the model is inaccurate, but because the experience is disconnected. It feels bolted on, unfamiliar or hard to access. The best implementations remove steps, not add them.

Example: A city government integrated AI into their case system and 311 processes. With minimal training, adoption surged because the AI was actually simpler and easier to use and actually saved staff time.

5. Build for change, not one-off wins

Design for adaptability. Processes and AI will evolve

Your first version of an AI solution won’t be your last and it shouldn’t be. Business priorities evolve, data changes and models drift. That’s why adaptability is critical.

Rather than locking in hard-coded logic or static integrations, use configurable no-code platforms that allow adjustments without heavy engineering. Equip your teams with tools to fine-tune processes over time. The goal isn’t just initial success, but rather sustainability.

Example: A customer success team used AI to monitor account health and proactively flag churn risks. Over time, they continually adjusted the model using no-code tools to include new behavior patterns and feedback from account managers, ensuring the system remained relevant and accurate.

AI that works for the business, not the hype

The companies seeing real returns from AI aren’t chasing trends but rather solving real problems. They treat AI not as a novelty, but as a lever for operational scale, decision velocity and competitive edge.

When done right, AI becomes a multiplier. It sharpens execution, accelerates learning and personalizes at scale. The takeaway? Success doesn’t start with the model. It starts with a business problem worth solving.

So, ask yourself: Where is your ROI hiding? Where is your untapped value? That’s where AI belongs.

This article is published as part of the Foundry Expert Contributor Network.
Want to join?