Welcome to Two-for-One, our monthly series where we explore a common business challenge and present two practical solutions to address it.
Artificial intelligence has moved beyond hype. In many industries, it’s already reshaping customer service, supply chains, finance, and product development. For organizations willing to explore its potential, AI offers real efficiency gains, faster decision-making, and more personalized customer experiences.
But here’s the sticking point: even tech-forward businesses often struggle with AI opportunity identification. Which processes should be prioritized? Which ones are ready for AI today — and which should wait until the data or systems catch up?
The reasons for this challenge are familiar: unclear process documentation, siloed departments, and the absence of a clear strategic roadmap. Without the right approach, identifying AI opportunities becomes a guessing game.
AI adoption without a deliberate plan can feel like “throwing technology at problems.” The risk is twofold: wasted investment in projects that don’t deliver measurable ROI, and lost opportunities in areas where AI could have had a transformative effect.
Consider a mid-sized logistics company that invests heavily in an AI-powered chatbot for customer service. While the chatbot improves response time, the company overlooks AI for route optimization — a solution that could have saved substantial fuel costs and improved delivery times.
This example illustrates a common trap: focusing on high-visibility tools rather than high-impact processes. Successful AI integration begins with identifying data-rich, repeatable, and strategically important processes where AI can make a measurable difference.
Below, we outline two approaches that help organizations identify where AI can add the most value. Both methods are grounded in practical steps and can be implemented independently or in combination.
Description: Before committing resources to AI initiatives, organizations should evaluate their current data maturity, technology infrastructure, and process clarity. An AI readiness assessment is a structured review of business processes, mapping each step to its data inputs and outputs, decision points, and recurring bottlenecks.
Example in practice: A regional retail chain conducts an AI readiness assessment of its supply chain operations. It discovers that restocking decisions are still based on manual spreadsheets. The assessment flags this as an ideal candidate for AI-driven demand forecasting — a shift that could improve inventory turnover and reduce stockouts.
Benefits:
Long-term impact: By repeating the assessment annually, businesses can track readiness improvements, respond to changing market conditions, and identify emerging AI opportunities. Over time, this creates a cycle of continuous improvement and informed decision-making.
Description: Looking outward can be just as valuable as looking inward. Benchmarking against industry leaders provides a curated list of tested AI applications that have been proven to deliver results. By learning from others, businesses can reduce uncertainty and accelerate adoption.
Example in practice: A manufacturing company reviews AI case studies in its sector and finds that several competitors use computer vision to detect defects on production lines in real time. By adopting a similar system, the company improves quality control without going through the lengthy trial-and-error phase of developing a custom approach.
Benefits:
Long-term impact: Consistent benchmarking helps organizations stay competitive, spot new trends early, and adapt to changes in customer expectations and operational standards.
While both solutions can accelerate AI opportunity identification, they also raise important strategic and operational questions:
For a deeper exploration of these questions, see our paper on process-oriented decision-making, where we detail a framework for aligning AI projects with business priorities.
Identifying where AI can have the greatest impact is a challenge worth solving. Businesses that take a structured approach avoid costly missteps and position themselves to capitalize on real opportunities.
In summary:
If you’re at the beginning of your AI journey, start with one of these two methods. Each will help you move beyond the hype and into practical, high-impact AI integration.
A business can identify AI opportunities by evaluating current workflows for data availability, process repeatability, and measurable impact potential. Two effective methods are conducting an AI readiness assessment and benchmarking against proven industry use cases.
An AI readiness assessment is a structured review of an organization’s processes, data quality, and technology infrastructure to determine where AI can be most effectively applied.
Benchmarking reveals AI applications that have already been tested and refined by peers or competitors, reducing implementation risks and offering proven models to adapt.
They help businesses focus resources on processes that are both technically feasible and likely to deliver measurable returns, avoiding wasted spending on unprepared areas.
Starting with one AI proof of concept allows for learning, testing, and refining the approach before scaling to multiple initiatives, minimizing risk and improving adoption.