AI adoption rates have grown dramatically in recent years, and today this growth continues. According to McKinsey’s 2025 survey, 88% of respondents stated that they already use AI in at least one business function, up from 78% the year before. However, most companies adopting AI struggle to achieve strong ROI and sustained business value. In the 2025 study by BCG, 60% of participants said that despite substantial investments, they could not achieve any material value from AI.
While the root causes of this problem can vary from company to company, a frequent contributing factor is incorrectly selected AI use cases. For instance, many companies base their choices primarily on AI vendors’ showcases, rather than identifying and solving real organizational pain points with AI, which results in projects with little to no business value. However, selecting the right AI use cases alone does not guarantee success. Companies must also efficiently implement their selected use cases to be able to translate transformative AI potential into actual, sustainable value.
In this article, experts from Itransition, a company offering artificial intelligence advisory and AI development services, provide a structured algorithm that you can follow to identify and implement AI use cases efficiently, while avoiding common pitfalls.
It is critical to begin AI implementation from the clear identification of process bottlenecks and areas in which AI can create real business value, as otherwise, a company risks ending up with expensive but underperforming or completely useless AI tools.
To begin, members of the CTO office or other teams responsible for AI initiatives should engage with representatives from different departments to better understand their workflows and operational challenges. During these conversations, it is worth asking the following questions:
- “Which work processes or specific tasks feel the most inefficient, and why?”
- “What are the biggest challenges to executing processes more efficiently?”
- “How would you redesign or improve slow or problematic processes?”
- “What additional resources or support would help you work better?”
In addition to interviews, teams responsible for AI implementation can also use process mining tools to create workflow maps. By analyzing event logs from ERP, CRM, and other business systems, modern process mining solutions can visualize how processes actually operate. This allows teams to link high-level processes to individual user tasks and identify the underlying causes of process delays or errors. Understanding how employees bypass inefficient processes can also help generate ideas for meaningful process improvements.
After locating specific pain points, bottlenecks, and improvement opportunities within business workflows, the AI implementation team should focus on problems causing the most pain and opportunities with the highest potential efficiency gains. For each of them, teams should define whether AI implementation is generally feasible and then determine possible scenarios of how technology can be used in practice to help a company’s departments work better.
To facilitate and accelerate the ideation process, it is advisable that teams use the “six primitives” conceptual model popularized by OpenAI. By covering and systematizing most real-world AI applications across all business functions and departments — content creation, research, coding, data analysis, ideation, and automation — this model offers a quick way for teams to understand how AI can be applied to specific processes and tasks.
To maximize the ROI of AI implementation, companies should focus on use cases that are high-impact, feasible, and fast-to-implement, which requires careful use case prioritization. There are multiple prioritization techniques that can come in handy in this regard:
- The impact-effort matrix
- This technique categorizes potential AI use cases into several groups: “Quick Wins” (high impact, low effort cases), “Big Bets” (high impact, high effort cases), “Fill-ins” (low impact/low effort cases), and “Money Pits” (low impact/high effort cases).
- The ICE scoring model
- With the help of this technique, teams can prioritize AI use cases by ranking them based on such parameters as Impact, Confidence, and Ease, typically on a 1–10 scale, to identify the most high-value and feasible AI implementation scenarios.
- The weighted scoring model
- Teams can leverage this technique to evaluate all AI use cases against multiple weighted criteria (e.g., impact, required technical effort, risk) and rank them in accordance with these criteria.
After selecting the most promising AI use cases, a company must validate that it is fully prepared for project implementation to increase the likelihood of successful AI adoption. During the validation process, here are the factors that are worth the most careful consideration:
- Technical infrastructure
- A company should ensure that its existing IT ecosystem can efficiently support the training and deployment of AI solutions.
- Data readiness
As data quality determines the accuracy and reliability of AI operations and output, before adopting AI, a company should assess data quality, improve it if needed, and establish a data governance framework.
- People and skills
- Organizations should evaluate whether both IT teams and business users are ready to adopt AI tools. Addressing AI skill gaps through hiring, training, or outsourcing is often necessary.
To validate both the technical feasibility and business value of AI use cases before investing in full-scale deployment, companies should first run a small-scale, controlled AI implementation, such as a proof of concept. Before launching the PoC, it is critical to define clear quantitative and qualitative success metrics, since without them, it will be difficult to prove whether the initiative delivers tangible business value. Once the PoC is underway, business users should be actively engaged to provide feedback on whether the solution addresses real operational challenges and helps them work more efficiently. Throughout the project lifecycle and during final evaluation, teams should compare the PoC’s results against predefined business KPIs to determine whether further AI investment is justified.
Treating AI as a “set-and-forget” technology is a common mistake, and to sustain long-term AI ROI, companies should continuously review AI solutions they deploy. Teams must monitor the AI solution’s health, AI model performance, and other AI-related technical parameters to ensure AI systems’ reliability. They should also regularly confirm that AI solutions continue to meet their original business objectives, whether it is improving process efficiency or reducing their execution costs.
Practice shows that companies’ ability to secure ROI from AI adoption largely depends on their capacity to identify the right AI use cases and implement these use cases effectively. The structured six-step algorithm described in this article can help your company discover the high-impact AI applications, implement them efficiently, and eventually, streamline overall AI adoption.
Since maximizing AI ROI is complex, resorting to experienced AI consultants is also advisable. These experts can assist with everything from discovering the most promising AI use cases and prioritizing them based on impact and technical complexity to assessing organizational readiness and developing a practical AI implementation strategy.
