Artificial intelligence is opening new possibilities across almost every part of the business, with agentic AI expanding the opportunity further. Alongside using AI to find and interpret information, generate content, analyze data, and support decisions, organizations can increasingly explore AI agents that take action across tasks, processes, and applications.
The potential business value is becoming clearer too. PwC‘s 2025 Global AI Jobs Barometer, based on analysis of close to one billion job advertisements across six continents, found that industries most exposed to AI experienced 27% growth in revenue per employee, compared with 9% in industries least exposed to AI. PwC also reports that AI usage is expanding across all industries analyzed.
For business leaders, this expanding opportunity creates an important strategic question: where should you focus first?
Potential AI use cases can emerge across every business function, each offering different benefits and requiring different levels of investment and capability. Building an effective AI strategy starts with understanding those opportunities and deciding where AI can create the greatest value for your organization.
What is an AI use case?
An AI use case connects an AI capability to a specific business need, process, task, decision, or opportunity.
AI use cases can take many forms, from helping employees find and interpret company knowledge to analyzing data, processing documents, supporting business decisions, or automating actions within core business systems.
The level of AI capability involved can vary too. Some scenarios focus on retrieving, reasoning over, and summarizing information. Others involve taking actions and automating tasks, while more advanced AI agents may plan and orchestrate activities with greater autonomy. As agentic AI develops, use cases can extend further into connected business workflows and processes.
This breadth gives organizations considerable scope to explore AI business opportunities. The value of an individual use case depends on your objectives, processes, people, technology environment, and customers.
Look beyond the most obvious AI opportunities
Once people understand what AI can do, ideas can emerge quickly. Teams may identify opportunities around repetitive tasks, access to information, customer interactions, analysis, forecasting, or process automation.
These visible opportunities provide useful starting points. AI opportunity discovery can then take a wider view across business processes and functions.
Consider sales, for example. Opportunities could span customer engagement, lead generation, sales enablement, forecasting, or post-sale activity. Finance presents a different landscape, including quote-to-cash, record-to-report, planning and analysis, risk management, and procure-to-pay.
Looking systematically across the organization helps teams examine how work happens, where value is created, what customers and employees need, and where AI could contribute to better outcomes. It can also reveal where an AI agent could take on a defined task or action within a wider process, creating opportunities that may be less obvious when AI is considered only at an individual productivity level.
A broad opportunity landscape also creates a stronger basis for AI use case prioritization, helping organizations decide which ideas deserve further attention and investment.
Deloitte‘s State of Generative AI in the Enterprise, based on a survey of 2,773 director-to-C-suite respondents across 14 countries, found that more than two-thirds of respondents expected 30% or fewer of their generative AI experiments to be fully scaled over the following three to six months. Nearly three-quarters said their most advanced generative AI initiative was meeting or exceeding ROI expectations. Deloitte highlights the importance of focus and prioritizing demonstrated use cases with measurable ROI.
This makes systematic comparison important: understanding which opportunities combine meaningful business value with a realistic path to implementation.
Prioritize AI use cases around value and feasibility
Identifying potential AI use cases creates a pool of opportunities. The next step is understanding how they compare in terms of impact, feasibility, investment, and time to value.
At Prodware, we analyze every shortlisted use case through our Technology, Processes, and People (TPP) methodology. Evaluating these three interconnected perspectives provides a consistent basis for prioritization, balancing business value and process impact with the needs of people and the technical feasibility of each opportunity.
Business processes and value
Consider the outcome the use case could create. Which process or objective does it support? What could improve? Can the benefit be measured? Factors such as business impact, efficiency gains, return on investment, process improvement, and time to value help establish its potential contribution.
People and everyday work
Consider who will interact with the solution and how it fits into everyday work. User needs, skills, process changes, and any training or support requirements can influence how successfully an opportunity translates into practical value.
Technology and feasibility
Assess what will be required to make the use case work: the expertise involved, integration with existing systems, architectural fit, technical dependencies, and the wider technology environment. For agentic AI use cases, this may also include considering the systems, information, and tools an agent needs to interact with to fulfil its intended purpose.
Together, these insights allow opportunities to be compared consistently and prioritized according to their potential value and fit for the business.
Make AI prioritization a shared business decision
Different stakeholders naturally approach AI investment from different perspectives. Leadership teams consider strategic impact, investment, return, and company direction. Functional teams bring detailed knowledge of processes, priorities, and everyday work. IT adds essential context around architecture, systems, resources, security, and technical feasibility.
Bringing these perspectives together creates a stronger basis for AI use case prioritization. Business knowledge establishes whether an idea addresses a meaningful opportunity, technical insight adds feasibility and dependency context, and leadership connects individual investments with wider business objectives.
This shared perspective helps organizations identify where AI could create the greatest value and build consensus around which opportunities should take priority.
Turn AI opportunities into a strategic roadmap
An effective AI strategy can extend beyond selecting a first project. As use cases are assessed, a broader portfolio begins to emerge.
Some opportunities may combine strong business impact with a relatively straightforward path to implementation. Others may offer considerable strategic value but require further preparation. Assessing their potential impact and operational fit helps position each opportunity within the wider AI portfolio.
This assessment results in a prioritized backlog of AI use cases, with each shortlisted opportunity supported by TPP analysis across Processes and business value, People, and Technology. The backlog provides a tangible foundation for the AI roadmap and subsequent investment decisions.
PwC‘s 2025 Global AI Jobs Barometer encourages businesses to develop plans for capturing AI opportunities at enterprise level, highlighting the potential for AI to contribute to wider business transformation and value creation.
Prodware AI Advisory helps you turn this opportunity landscape into an actionable direction.
Together, we identify potential AI use cases, including opportunities for AI agents, and analyze each shortlisted use case through our Technology, Processes, and People methodology. The service delivers a prioritized backlog of analyzed use cases, an AI strategic plan and roadmap, and the detailed design of a selected priority use case, giving you concrete outputs to guide investment and next steps.
Together, these deliverables provide a practical path from discovery to action, connecting your priority use cases with your wider AI strategy.
