Identifying and Prioritizing Artificial Intelligence Use Cases for Business Value Creation

The Strategic Imperative of AI and Generative AI Use Case Identification and Prioritization — A Leader’s Blueprint.

The increasing accessibility and sophistication of AI technologies present significant opportunities for organizations across various industries. I usually gets asked the following questions by business leaders who are trying to make sense of it all — identifying and prioritizing AI use cases.

1. Alignment: Does this AI use case align with our core strategy? How do I find out

2. Impact: What measurable business value will this generate? How do I measure it?

3. Feasibility: Can we realistically implement this with current resources and infrastructure? How do I quantify this?

4. Data Readiness: Do we have the right data to support this initiative? How do I validate?

5. Risk Management: What are the risks, and how will we mitigate them?

6. ROI: How soon and clearly can we measure return on investment?

7. Competitive Advantage: Does this differentiate us from competitors?

8. Scalability: Can we easily scale this beyond a pilot?

9. Time-to-Value: How quickly will we see meaningful results?

10. Ethical Considerations: Does it meet ethical and regulatory standards?

11. Talent and Skills: Do we have or can we acquire necessary expertise?

12. Sustainability: What is the ongoing cost and maintenance requirement?

13. Adoption and Change: Will stakeholders adopt this solution readily?

14. Customer Impact: How will this improve customer experience or loyalty?

15. Future Growth: Does this position us strategically for future opportunities?

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I will try to provide a deep analysis of how enterprises systematically discover high-impact AI use cases and prioritize them for implementation. It draws on leading research and industry insights — from Harvard Business Review and MIT Sloan Management Review to Gartner, McKinsey, and academic journals — to distill proven frameworks, methodologies, challenges, and best practices. Historical perspectives are combined with recent developments (such as generative AI) to illustrate how use case identification and prioritization have matured. In-depth case studies from multiple industries (finance, manufacturing, healthcare, retail, etc.) highlight real-world approaches. The report is structured into clear sections for ease of reference, with key takeaways emphasized for management and decision-makers.

However, to fully realize the transformative potential of AI and avoid the pitfalls of aimless adoption, a structured and strategic approach is paramount. It is crucial to move beyond the general enthusiasm surrounding AI and focus on identifying specific applications that can generate measurable value and align with overarching business objectives

The initial excitement surrounding AI is now maturing into a more pragmatic focus on execution and the delivery of tangible business results Organizations are increasingly seeking clear strategies to identify valuable AI use cases and to ensure that their investments in this technology translate into demonstrable returns. Furthermore, the development and implementation of a well-defined AI strategy framework is essential to guide the adoption process and to ensure that AI initiatives are aligned with the long-term strategic goals of the business.

In the following sections, I try to provide a structured guide for business leaders and strategists on how to effectively identify and prioritize AI use cases, as well as how to measure the return on investment of these initiatives, drawing upon insights from management sources and industry advisories.

I have structured this blog into four parts.

Part 1 will explore various frameworks and methodologies for identifying potential AI use cases within an organization.

Part 2 will get into frameworks and methodologies for prioritizing these identified use cases based on their potential value and feasibility.

Part 3 will focus on methods for measuring the return on investment of implemented AI initiatives.

Finally, Part 4 will provide management perspectives and strategic recommendations for effectively leveraging AI to achieve business objectives.

Lets get started.

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Part 1: Identifying Potential AI Use Cases for Your Business

1.1 Frameworks for AI Use Case Discovery: A Structured Approach

Several structured frameworks can assist businesses in systematically identifying potential applications of AI. These frameworks provide a roadmap for exploring different areas of the business and uncovering opportunities where AI can deliver the most value.

The IDEAL Framework offers a five-step process for deploying AI use cases: Identify a Use Case, Determine Data, Establish a Model, Architect Infrastructure, and Launch the Experience. This framework emphasizes starting with a specific AI application that the business wants to implement. It underscores the iterative nature of AI deployment, suggesting that organizations should always begin by clearly defining the use case they aim to address. A critical aspect of the IDEAL framework is its focus on data; the viability of any identified AI use case is heavily dependent on the availability and quality of relevant data. Without sufficient and appropriate data, even the most promising AI applications cannot be effectively developed and deployed.

The Business, Experience, Technology (BXT) Framework provides a more holistic approach by evaluating potential AI use cases across three key dimensions: business viability, user experience, and technological feasibility

1. Under the ‘Business’ dimension, the framework considers the financial viability of the AI solution, its alignment with the organization’s overall strategy, and the timeframe required for change management. The ‘Experience’ dimension focuses on the desirability of the AI solution from the perspective of the users who will interact with it. Finally, the ‘Technology’ dimension assesses the technical feasibility of implementing the AI solution given the organization’s current infrastructure and resources. By considering these three interconnected dimensions, the BXT framework ensures that identified use cases are not only technically possible but also valuable and desirable from both business and user perspectives. This helps to filter out use cases that might be strong in one area but weak in others, leading to more well-rounded and successful AI initiatives.

The Horizon-Based Framework categorizes AI initiatives into three horizons based on their strategic goals and potential impact:

Horizon 1 focuses on optimizing core business operations for quick wins and measurable returns;

Horizon 2 aims at improving market position through enhanced products, services, or customer engagement; and

Horizon 3 targets transforming business models and creating new sources of value.

This framework helps businesses balance their immediate needs with their long-term strategic opportunities and facilitates effective resource allocation across different time horizons. The Horizon-Based Framework enables a phased approach to AI adoption, allowing businesses to build confidence and gain experience with lower-risk, high-return projects in Horizon 1 before venturing into the more transformative initiatives of Horizon 2 and

1. This gradual integration reduces the risk of overwhelming the organization with overly ambitious projects at the outset.

1.2 Methodologies for Uncovering AI Opportunities within Your Organization

Beyond structured frameworks, several practical methodologies can be employed to uncover specific AI opportunities within an organization. These methodologies involve a systematic examination of various aspects of the business to identify areas where AI can be effectively applied.

A comprehensive evaluation of current operations involves a thorough audit of existing business processes to identify inefficiencies, repetitive tasks that consume valuable time, and areas that are prone to human error. The focus should be on pinpointing pain points where the automation or optimization capabilities of AI can yield significant benefits. Starting with menial and repetitive tasks is often recommended as these use cases are typically easier to automate and can generate a faster return on investment, thereby building trust in AI among stakeholders.

Analyzing customer experience is another fertile ground for identifying AI opportunities. This involves examining how customers interact with the company to identify friction points in their journey and opportunities for personalization and improved service. AI can be leveraged to provide personalized marketing messages, product recommendations based on past behavior, and enhanced customer support through tools like chatbots and virtual assistants. By providing personalized and timely interactions, AI can significantly enhance the customer experience, potentially leading to increased satisfaction and loyalty.

Prospecting for innovation and competitiveness involves actively seeking areas where AI can be used to create entirely new products, services, or business models, thereby providing the organization with a significant competitive edge. This can involve benchmarking against competitors to understand their AI strategies and identify potential opportunities that the organization might be missing. Observing competitors’ AI initiatives can provide valuable insights and inspiration for identifying high-value opportunities within your own organization.

Business envisioning is a methodology that utilizes structured questions to thoroughly define potential AI use cases. This involves clearly articulating the problem to be solved, the opportunity that AI presents, the specific business objective that the use case aims to achieve, how success will be measured, and who will be accountable for the outcomes. This process helps to evaluate and prioritize the most viable use cases for development and execution

1. Business envisioning ensures that the identified use cases are clearly defined, directly aligned with business goals, and have measurable success criteria established from the outset.

Brainstorming techniques can also be highly effective in generating a wide range of potential AI use cases. Various methods can be employed, such as asking open-ended questions to encourage diverse perspectives, role-playing with AI to explore different viewpoints, making lists of potential applications, creating mind maps to visually organize ideas, and exploring “what if?” scenarios to think outside the box

1. Notably, AI tools themselves can be leveraged to assist in the brainstorming process, offering diverse perspectives, overcoming human biases, and generating a large quantity of ideas quickly

1.3 Best Practices for Effectively Identifying High-Potential AI Use Cases

To maximize the chances of identifying truly valuable AI applications, businesses should adhere to several best practices throughout the discovery process.

First and foremost, it is crucial to define clear business objectives before exploring AI use cases. AI initiatives should be driven by specific business goals, such as reducing operational costs, improving efficiency, or enhancing customer experience

1. Secondly, businesses must assess the quality and quantity of their available data. AI models require high-quality, well-structured, and a sufficient volume of data to perform effectively

2. Gathering input from stakeholders across different departments is also essential. Involving various teams helps to identify use cases that align with the broader strategic goals of the company and ensures that the necessary buy-in is secured for successful implementation

3. A cross-functional approach to identifying AI use cases is crucial for ensuring alignment with overall business strategy and securing support from different departments.

Considering technical feasibility early in the process is another important best practice. Businesses should evaluate whether their team possesses the necessary skills and if their existing infrastructure can support the implementation of AI tools. If gaps exist, they should consider outsourcing or partnering with specialized vendors

1. Furthermore, all potential AI use cases must be aligned with relevant regulatory and ethical considerations, particularly in sensitive areas like data privacy and security

2. Finally, the focus should be on identifying high-impact areas within the business — processes that are time-consuming, repetitive, and have the potential for significant improvement through AI-powered automation or optimization.

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Part 2: Prioritizing Identified AI Use Cases

2.1 Frameworks for Ranking and Selecting AI Initiatives

Once a pool of potential AI use cases has been identified, the next critical step is to prioritize them effectively to ensure that resources are allocated to the initiatives that offer the greatest potential value and are most likely to succeed. Several frameworks can guide this prioritization process.

The Impact and Feasibility Matrix, also known as the Value vs. Effort Matrix, is a widely used tool for prioritizing tasks and projects, including AI initiatives

1. This framework involves plotting use cases on a 2x2 matrix based on their potential business value (impact) and the ease with which they can be implemented (feasibility or effort)

2. Initiatives that fall into the “high-value, low-effort” quadrant are typically prioritized as they offer the potential for quick wins. Use cases in the “high-value, high-effort” quadrant may be considered for future implementation, while those in the low-value quadrants are generally deprioritized. The Impact and Feasibility Matrix provides a simple yet effective visual tool for prioritizing AI use cases based on their immediate potential and the resources required.

Risk-Reward Analysis offers another perspective on prioritization by explicitly assessing the potential benefits of each AI project against the risks associated with its implementation. This involves identifying and quantifying both the potential rewards, such as efficiency gains and revenue increases, and the potential risks, such as cost overruns and technology adoption hurdles

1. Each project can then be evaluated based on its net value (rewards minus risks), and scenario analysis can be conducted to understand how changes in risk factors might impact the project’s potential rewards

2. A risk-reward analysis helps businesses make informed decisions by explicitly considering the potential downsides alongside the expected benefits of each AI initiative.

The Business, Experience, Technology (BXT) framework, previously discussed for identifying use cases, can also be used for prioritization

1. By utilizing the scores assigned to each use case across the three dimensions of business viability, user experience, and technological feasibility, initiatives can be ranked based on their overall strategic business impact and executional fit

2. This involves calculating a score for both the degree of strategic business impact and the degree of executional fit, and then using these scores to visualize the overall viability of each use case

3. The BXT framework extends beyond simple value and feasibility by incorporating user desirability and strategic alignment into the prioritization process.

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2.2 Methodologies for Prioritizing Based on Business Value and Feasibility

Several methodologies focus specifically on evaluating the business value and feasibility of AI use cases to inform prioritization decisions.

A Return on Investment (ROI) Assessment is a fundamental methodology for prioritizing AI initiatives based on their potential financial returns relative to their costs

1. This involves considering both the tangible benefits, such as cost savings and revenue generation, and the intangible benefits, such as improved customer satisfaction and brand image

2. Projects that are expected to deliver a clear and quick ROI are often prioritized, especially when an organization is in the early stages of adopting AI

3. Prioritizing AI projects based on their potential ROI allows businesses to focus on initiatives that are likely to deliver the most significant financial benefits and build a strong business case for further AI investments

Strategic Alignment Scoring involves assigning scores to potential AI use cases based on how well they align with the company’s overarching strategic objectives and priorities

1. This ensures that AI initiatives directly support key business goals and contribute to the long-term vision of the organization

2. Prioritizing use cases that strongly align with strategic objectives ensures that AI investments are focused on areas that will have the greatest impact on the company’s success

A Technical and Operational Feasibility Analysis assesses the practicality of implementing each AI use case by considering the availability of data, the existing infrastructure, the skills and expertise of the team, and the overall resources required

1. This includes evaluating the quality, quantity, and accessibility of the necessary data, assessing the current technology stack and identifying any need for new tools or infrastructure, and evaluating the skills of the team and determining if external partners or expertise are required

2. A thorough feasibility analysis is crucial for avoiding projects that are technically too challenging or require resources that are not currently available, thereby ensuring a higher likelihood of successful implementation

2.3 Best Practices for Successful AI Use Case Prioritization

To ensure that the prioritization process leads to the selection of the most promising AI initiatives, businesses should follow several best practices.

It is essential to involve key stakeholders from across different departments and levels of the organization in the prioritization process to ensure buy-in and alignment

1. Businesses should also focus their efforts on a few high-priority opportunities rather than diluting their resources across numerous projects

2. A balanced approach that considers both short-term wins and long-term strategic goals is crucial. Prioritizing some quick-win projects can demonstrate early value and build momentum, while also investing in more transformative initiatives that may take longer to yield results

3. When evaluating use cases, it is important to consider the ongoing costs associated with maintenance and support, including the expenses of training, updating, and maintaining AI models

4. Starting small with pilot projects to test assumptions and gather feedback before committing to full-scale implementation is also a recommended best practice

5. Given the rapidly evolving nature of AI and changing business needs, the prioritization process should be viewed as ongoing, with regular reassessments and adjustments as necessary

6. A balanced portfolio of AI initiatives, including both quick wins and strategic long-term projects, is essential for demonstrating immediate value while building towards future transformation

Part 3: Measuring the Return on Investment (ROI) of AI Initiatives

3.1 Frameworks and Methodologies for Quantifying AI ROI

Measuring the return on investment (ROI) of AI initiatives is crucial for justifying the investments made and for demonstrating the value that AI is bringing to the business. Several frameworks and methodologies can be used to quantify AI ROI.

The traditional ROI formula, calculated as (Net Profit / Investment Cost) x 100, provides a basic framework for assessing the financial return of any investment, including AI

1. This requires clearly defining and quantifying both the net profit generated by the AI initiative (financial benefits minus the total costs) and the total investment cost, which includes expenses related to development, infrastructure, personnel, and ongoing operations

2. While this formula is straightforward, measuring AI ROI often necessitates considering a broader range of both tangible and intangible benefits that may not be immediately reflected in traditional financial statements

Emerj’s Trinity Model offers a more nuanced perspective on AI ROI by categorizing it into three types: measurable financial ROI, measurable non-financial ROI, and strategic ROI

1. This model recognizes that AI can deliver value beyond just monetary returns, including operational improvements, enhanced customer experiences, and strategic advantages that contribute to long-term business success

2. Emerj’s Trinity Model acknowledges the multifaceted nature of AI ROI, encompassing not only financial gains but also operational improvements and strategic advantages.

The AI ROI KPI Framework utilizes a comprehensive set of Key Performance Indicators (KPIs) to measure the impact of AI initiatives across various dimensions of the business. These dimensions typically include financial impact (e.g., ROI, cost savings), operational efficiency (e.g., time to market, process efficiency), customer experience (e.g., satisfaction scores, retention rates), workforce productivity, AI adoption rates, and risk management metrics. By tracking specific metrics that are relevant to the objectives of the AI initiative, organizations can gain a more granular and holistic view of its impact.

3.2 Key Metrics for Evaluating the Success and Impact of AI Deployments

A variety of key metrics can be used to evaluate the success and overall impact of AI deployments on a business. These metrics can be broadly categorized into financial, operational, customer-centric, and employee-related measures.

Financial metrics often include cost savings achieved through automation, efficiency gains, and optimized resource allocation 31, as well as revenue growth resulting from increased sales, the creation of new income streams, and improved conversion rates driven by AI-powered tools and insights

1. Operational metrics focus on improvements in process performance, such as reduced cycle times, increased automation rates, and overall efficiency gains

2. Customer satisfaction is a critical area often measured through metrics like improved customer experience scores, reduced customer churn rates, and higher Net Promoter Scores (NPS)

3. Employee productivity can be assessed by tracking increases in output, reductions in manual effort, and improvements in job satisfaction resulting from AI augmentation

4. Accuracy and error reduction are also important, particularly in AI applications involving data processing, prediction, and task completion

5. Finally, time to market, which measures the acceleration of product development and deployment cycles, can also be a key indicator of AI’s impact.

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This table illustrates how different AI applications can be linked to specific business goals and measured using relevant KPIs.

3.3 Best Practices and Considerations for Accurate ROI Measurement

To ensure that the measurement of AI ROI is accurate and meaningful, businesses should adhere to several best practices.

It is crucial to define clear objectives and identify the relevant KPIs upfront, before the AI initiative is even implemented. Establishing a baseline by measuring the chosen KPIs before AI implementation provides a point of comparison for quantifying the impact of the AI solution. When calculating ROI, businesses must track all costs associated with the AI project, including both direct expenses like technology and personnel, and indirect costs such as training and system integration. It is also important to consider both the tangible financial benefits and the intangible benefits, such as improvements in customer satisfaction and brand image, to get a comprehensive view of the value created. Businesses should set a realistic timeframe for evaluating the ROI, recognizing that AI projects may take time to fully deploy and to demonstrate measurable results. A comprehensive approach that combines quantitative data with qualitative feedback provides a more holistic understanding of AI’s impact on the business. Finally, the process of measuring AI ROI should be ongoing, with continuous monitoring and refinement of metrics to ensure they remain relevant and effective in reflecting the evolving impact of AI initiatives.

Part 4: Management Perspectives and Strategic Recommendations

4.1 Insights from Leading Management Consulting Firms on AI Use Case Strategy

Leading management consulting firms offer valuable insights and perspectives on developing effective AI use case strategies.

McKinsey & Company emphasizes the importance of a top-down approach to AI adoption, with strong commitment and involvement from the C-suite. They advise organizations to redesign workflows to maximize the bottom-line impact of AI deployments and to focus on identifying and implementing high-impact use cases. McKinsey also highlights the value of establishing a centralized, cross-functional AI platform team to support AI initiatives across the organization and stresses the need to balance efficient resource allocation with broad empowerment in AI deployment strategies. Their perspective suggests that successful AI adoption requires strong leadership involvement and a focus on integrating AI into core business processes to drive significant value.

Boston Consulting Group (BCG) advocates for businesses to set ambitious targets for their AI initiatives and to strategically focus their investments on a limited number of high-priority opportunities that have the potential to deliver significant and transformative impact. BCG promotes a three-pronged approach to AI value creation: Deploying AI in everyday tools for immediate productivity gains, Reshaping critical functions by re-engineering workflows with AI, and Inventing new revenue streams by developing novel AI-powered products and services. They also stress the importance of adopting a people-first lens when implementing AI and effectively managing the organizational change that often accompanies AI-driven transformations.

Deloitte emphasizes the critical role of aligning AI initiatives with the overall business strategy and ensuring robust governance and ethical considerations throughout the AI lifecycle. They advise businesses to focus on building bridges to sustained ROI by prioritizing high-impact use cases in proven areas and offer specific methodologies for selecting Generative AI use cases. Deloitte also underscores the importance of managing uncertainty associated with AI adoption and prioritizing the preparation of the workforce for AI-driven changes. Furthermore, they advocate for the implementation of a Trustworthy AI™ framework that encompasses ethical safeguards across various dimensions, highlighting the importance of responsible AI implementation and maintaining stakeholder trust.

4.2 Advisories on Integrating AI into Business Objectives and Governance

Integrating AI effectively into a business requires a holistic approach that goes beyond simply implementing technological solutions. Several key advisories can guide organizations in this process.

First, it is crucial to align the AI strategy with the overall corporate strategy and specific business objectives, ensuring that AI initiatives are driven by clear business needs and goals. Establishing clear data governance frameworks that address data quality, privacy, security, and regulatory compliance is also essential for building trust and ensuring responsible AI practices

1. Furthermore, organizations should develop a comprehensive ethical framework to guide the development and deployment of AI technologies, addressing potential biases and ensuring fairness and transparency. Fostering a culture of innovation and continuous learning around AI is vital for encouraging experimentation, adaptation, and the ongoing improvement of AI capabilities within the organization

2. Investing in upskilling and reskilling the workforce is necessary to equip employees with the skills needed to work alongside AI and to adapt to AI-driven changes in their roles

3. Implementing a robust AI risk management framework is also critical for identifying, assessing, and mitigating potential risks associated with AI deployments, such as security vulnerabilities and ethical concerns. Integrating AI effectively requires not only a strategic vision but also a strong focus on governance, ethics, data management, and talent development across the entire organization.

4.3 Actionable Recommendations for Businesses to Effectively Leverage AI

Based on the insights and advisories discussed, several actionable recommendations can help businesses effectively leverage AI to achieve their strategic goals and drive value creation.

Businesses should begin by focusing on well-defined business problems rather than simply adopting AI for its own sake or chasing the latest technological trends

1. It is advisable to prioritize use cases that offer a clear and measurable return on investment, particularly in the initial stages of AI adoption, to demonstrate the value and build momentum. Building a strong data foundation is crucial, which involves ensuring the quality, accessibility, and effective governance of the data that will be used to train and operate AI models

2. Fostering close collaboration between business teams, who understand the organizational needs and challenges, and technical teams, who possess the AI expertise, is essential throughout the entire AI lifecycle, from ideation to deployment and ongoing maintenance. Adopting an agile and iterative approach, starting with small-scale pilot projects to test assumptions and gather feedback, and then scaling successful initiatives based on the results, is a pragmatic way to minimize risk and maximize learning. The performance of AI solutions should be continuously monitored, and strategies should be adapted as needed based on the insights gained from this monitoring. Finally, businesses should remain informed about the evolving AI landscape, including new technologies, emerging best practices, and potential risks and ethical considerations. A pragmatic and iterative approach, focusing on solving specific business problems with a strong emphasis on data and collaboration, is key to successfully leveraging AI and achieving a positive return on investment.

The effective integration of Artificial Intelligence into business operations hinges on a strategic and well-executed approach to identifying, prioritizing, and measuring AI use cases. By leveraging structured frameworks like IDEAL, BXT, and the Horizon-Based Framework, businesses can systematically uncover opportunities for AI application. Methodologies such as comprehensive operational evaluations, customer experience analysis, innovation prospecting, business envisioning, and collaborative brainstorming further enrich the discovery process. Adhering to best practices, including defining clear objectives, assessing data readiness, engaging stakeholders, and considering feasibility and ethical implications, ensures that high-potential use cases are identified.

Prioritization frameworks like the Impact and Feasibility Matrix, Risk-Reward Analysis, and BXT prioritization, combined with methodologies focused on ROI assessment, strategic alignment scoring, and technical feasibility analysis, enable businesses to select the most promising initiatives. Best practices in prioritization emphasize stakeholder involvement, a focus on key opportunities, balancing short-term and long-term goals, considering ongoing costs, starting small, and continuous reassessment.

Measuring the success of AI initiatives requires a robust approach to ROI calculation, utilizing frameworks like the traditional ROI formula, Emerj’s Trinity Model, and AI ROI KPI Framework. Tracking key metrics across financial, operational, customer, and employee dimensions, and adhering to best practices for accurate measurement, provides valuable insights into the impact of AI deployments.

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The perspectives of leading management consulting firms like McKinsey, BCG, and Deloitte underscore the importance of strong leadership, strategic focus, ethical considerations, and a pragmatic approach to AI adoption. Integrating AI into business objectives and governance requires a commitment to data management, talent development, and risk mitigation. By following actionable recommendations, businesses can navigate the complexities of the AI landscape and unlock its transformative potential to create sustainable value and achieve a significant competitive advantage.

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Discovering Impactful AI Use Cases

Identifying impactful AI opportunities is a critical first step in any enterprise AI strategy. Rather than randomly experimenting, leading organizations adopt systematic discovery frameworks to surface use cases with genuine business value. This section examines effective methodologies for AI use case discovery, along with common challenges and best practices.

Frameworks and Methodologies for Use Case Discovery

Identifying pain points or strategic objectives where AI can make a difference ensures initiatives are demand-driven. For example, an Economist case study described how a UK hospital pinpointed missed patient appointments as a specific problem and partnered with an AI provider to solve it. The result was an AI chatbot that freed up 700

• appointment slots per week, directly addressing a real business need (improved service utilization) impact.economist.com. This problem-first mindset helps avoid “solution in search of a problem” syndrome.

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Key Challenges in Identifying Use Cases

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Best Practices for Use Case Discovery

Leading practices are emerging to address the above challenges and systematize AI opportunity discovery:

• Business-Centric Ideation — Start with concrete business objectives or problems. Use strategic goals as guideposts (e.g. improving customer retention, reducing supply chain delay, increasing revenue per user) and brainstorm how AI might help achieve them. This ensures use cases are aligned with value creation. As one IBM executive advised in The Economist, “the biggest success [stories] started with a clear business problem” and then applied AI to solve it​ impact.economist.com.
• Cross-Functional Teams — Involve stakeholders from across the organization (business unit heads, process owners, data scientists, IT, risk/compliance, etc.) in ideation workshops. Diverse perspectives help surface a wider range of use cases and vet them from multiple angles. Gartner recommends that line-of-business stakeholders be able to clearly articulate the expected business benefit of any AI idea — answering what problem it solves, who the end-user is, and how success will be measured gartner.com.au gartner.com.au. This forces clarity and shared understanding early.
• “Design Thinking” Approach — Embrace an iterative, user-centric discovery process. The CMR case study highlighted forming a multi-disciplinary team, conducting field observations and user interviews to identify pain points, then holding a needs discovery workshop to define use cases collaboratively​ cmr.berkeley.edu cmr.berkeley.edu. Rapid prototyping (e.g. hackathons) can then be used to quickly test the viability of ideas in practice​ cmr.berkeley.edu cmr.berkeley.edu. This approach ensures use cases are grounded in real user needs and validated early by proof-of-concept, increasing the chance that they’ll be impactful and adopted.
• Benchmark and Borrow Ideas — Look at industry peers and even other industries for inspiration. Many companies publish case studies of successful AI applications (in journals and conferences). For instance, manufacturers adopting AI for predictive maintenance or quality control have well-documented ROI — a classic early win in manufacturing. Oil & gas company Repsol analyzing drilling data with AI to find root causes of inefficiency, or Hitachi using AI-driven recommendations to improve warehouse worker productivity by 8%, are examples that can spur ideas in other firms​ kenaninstitute.unc.edu. Scanning external “lighthouse” use cases helps organizations identify analogous opportunities in their own operations. Gartner’s concept of an “AI use-case repository” or opportunity radar is useful: some firms maintain an internal database of AI use cases gathered from across the industry, which business units can review when brainstorming (avoiding starting from scratch)​ mitsloan.mit.edu mitsloan.mit.edu
• Start Small, Then Scale — As a practical strategy, experts advise starting with a few targeted, low-risk AI projects to build confidence and organizational know-how​ ir.coveo.com. Early successes (even if modest) can demonstrate value and generate momentum for broader AI exploration. Harvard Business Review experts note that organizations should “begin with targeted AI use cases and gradually expand as confidence and expertise grow.” ​ ir.coveo.com

This iterative approach also allows the company to develop governance and best practices on a small scale before larger rollouts.

By following these practices, enterprises can create a repeatable “use case discovery” process that continually feeds high-potential AI project ideas into their innovation pipeline. However, once a list of potential use cases is in hand, the next challenge is deciding which ones to pursue — that’s where systematic prioritization and ranking come into play.

Prioritizing and Ranking AI Use Cases

Not all AI ideas are equal — some promise huge ROI or strategic advantage, while others may be nice-to-have or unfeasible in practice. Enterprises need structured methods to evaluate, rank, and prioritize AI use cases so that resources are invested in the most impactful projects. This section explores established prioritization frameworks, alternative methodologies, key metrics, and decision-making criteria organizations use to select AI initiatives. Effective prioritization ensures alignment with strategy and maximizes the chances of delivering business value from AI.

Frameworks for Evaluating and Ranking Use Cases

Beyond the basic matrix, enterprises use various structured methodologies to rank AI initiatives:

• Scoring Models / Decision Matrices: Many firms develop a weighted scoring system to evaluate use cases against multiple criteria. For instance, a decision matrix might score each idea on criteria such as estimated ROI, strategic alignment, required investment, technical difficulty, data availability, time to implement, risk level, and so on. Each factor is given a weight (reflecting its importance to the organization’s context) and an overall score is calculated. This helps quantitatively compare a set of diverse projects. A Journal of Business Strategy article notes that combining resources and capabilities is key — an AI project should be evaluated on how well it can exploit the firm’s existing knowledge and systems​ kenaninstitute.unc.edu kenaninstitute.unc.edu. In practice, this means criteria like “fit with our data assets” or “leverages our domain expertise” often appear in scoring models. Some companies include an “innovation score” to gauge how novel or differentiating a use case is, ensuring that not only incremental projects get chosen. Decision matrices bring objectivity and transparency to prioritization, though they are only as good as the estimates applied.
• Cost-Benefit and ROI Analysis: Especially for more mature or later-stage AI initiatives, organizations conduct formal cost-benefit analyses or develop business cases. This involves estimating the expected benefits (e.g. revenue uptick, cost savings, productivity gains) and comparing against the projected costs (development effort, infrastructure, training, maintenance) over a period of time. Traditional financial metrics like Return on Investment (ROI), net present value (NPV), or payback period might be calculated for each use case. However, calculating ROI for AI can be tricky, particularly for innovative or intangible benefits. A Harvard Business Review piece observed that many companies struggle with measuring ROI on AI because they run too many small pilots without clear links to business KPIs​ cognitiveworld.com. Best practice is to tie AI projects to specific business metrics from the start (as noted earlier by Gartner) and to identify success metrics early and track them ​ gartner.com.au gartner.com.au. Some organizations follow an attribution approach — for example, attributing a lift in sales conversions to a new AI recommendation engine. As AI matures, new ROI frameworks are emerging. Gartner suggests expanding the notion of ROI for AI by considering “Return on Employee” (productivity gains) and “Return on Future” (new revenue streams) in addition to traditional ROI nationalcioreview.com. These broader measures capture AI’s impact on efficiency and innovation, not just immediate profit.
• Feasibility Studies and Risk Assessment: Before fully committing, enterprises might do detailed feasibility studies for top use case contenders. This can include prototyping or proof-of-concept results, an assessment of data readiness, evaluation of required AI models and their success likelihood, and an analysis of potential risks (e.g. regulatory, ethical, cybersecurity risks specific to that use case). As part of prioritization, companies increasingly incorporate risk criteria — both the risk of project failure and the risks the AI system could introduce if implemented. For instance, banks considering AI use cases in credit scoring must weigh model risk and compliance issues heavily. The Societe Generale case offers a best-practice example: the French bank set up a formal process where each business unit must register AI use cases in a central portal and go through frameworks for value assessment, feasibility, risk, and reusability before approval mitsloan.mit.edu. They conduct “formal studies to determine feasibility, risk, and reusability potential” for each AI idea​ mitsloan.mit.edu. Only those that pass these checks and clearly align with strategic goals get resourced. This kind of disciplined gating process ensures, for example, that if a use case has high estimated value but comes with disproportionate risk or low technical viability, it will be delayed or dropped in favor of more attainable projects.
• Portfolio Alignment and Strategic Buckets: Some organizations take a portfolio management view, ensuring the set of AI initiatives is balanced and aligned with strategy. They may allocate “buckets” or quotas for different types of projects — e.g. 70% of AI budget to core business improvements, 20% to new growth or innovation bets, 10% to exploratory or R&D use cases (an approach analogous to the classic 70/20/10 innovation model). This ensures a pipeline of both low-risk improvements and high-upside experiments. McKinsey and others have noted that companies seeing the most AI value are those that treat AI initiatives as a portfolio, not just individual projects​ mitsloan.mit.edu mitsloan.mit.edu. Societe Generale’s chief digital officer echoed this, cautioning that having a large number of scattered AI pilots is less effective than focusing on a smaller portfolio of use cases that truly drive strategic value​ mitsloan.mit.edu. In her words: “When you go from number of use cases to value… it’s better to have fewer use cases with bigger value at stake than a broad range of use cases.” ​ mitsloan.mit.edu. This sentiment is widely shared: rather than bragging about dozens of AI experiments, leading firms prioritize a portfolio that moves the needle on key metrics.

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Key Metrics and Decision Criteria

What specific metrics and criteria do enterprises use to judge “value” and “feasibility” in the above frameworks?

While these vary by company and industry, some essential factors consistently appear:

• Financial ROI and Cost Savings: Hard dollar impact remains a primary metric. This includes increased revenue (from, say, better personalization or new AI-driven products) or cost reductions (from process automation, improved efficiency, labor savings, waste reduction). For example, if an AI-driven supply chain optimization could reduce inventory costs by 15%, that projected saving is a concrete benefit to weigh. Some companies set hurdle rates — e.g. an AI project must show potential for >X% ROI or payback within Y years to be prioritized. However, purely short-term financial metrics can undervalue strategic or innovative projects, so ROI is weighed alongside other criteria. As one Gartner report noted, AI leaders focus more on business-centric metrics (like customer satisfaction or productivity) than on traditional financial accounting alone​ gartner.com.au. Still, demonstrating a line of sight to financial value is important for executive buy-in.
• Strategic Alignment and Competitive Advantage: A use case tightly linked to the company’s strategic priorities will be ranked higher. This could mean aligning with strategic goals (e.g. “improve customer experience”, “expand to new markets”, “digitize core operations”) or enhancing key capabilities that drive competitive advantage. An academic study in Strategic Management Journal found that AI adoption can fundamentally change the basis of competition — substituting some human capabilities and creating new hybrid human-AI capabilities that become sources of advantage​ ouci.dntb.gov.ua ouci.dntb.gov.ua. In practice, this implies organizations should prioritize AI initiatives that reinforce what makes them competitive. For instance, if a bank’s strategy is to lead in risk management, then AI use cases in fraud detection or risk modeling may deserve priority due to strategic fit. Many firms use strategic fit as a gating criterion: if a proposed AI project doesn’t clearly link to strategic objectives or core competencies, it may be shelved even if it has positive ROI. As Societe Generale discovered through their value-tracking, some use cases “tick the box of AI” but have no real relationship to the strategy and thus don’t contribute meaningfully​ mitsloan.mit.edu. Enterprises avoid these distractive projects in favor of those with strategic alignment.
• Innovation Potential and Future Growth: Not all benefits are reflected in immediate ROI; some use cases are pursued for their innovation potential or long-term positioning. This includes enabling new business models, services, or revenue streams that could be game-changers. Gartner’s concept of “Return on Future (ROF)” captures this forward-looking value​ nationalcioreview.com. For example, deploying AI to develop a new personalized product line may not yield large short-term ROI, but could unlock a new market segment — a strategic move warranting investment. Companies often consider whether a use case, if successful, could create a sustainable competitive differentiation or open up new opportunities (e.g. data network effects, ecosystem advantages). Metrics here are more speculative, but might involve scenario analysis of future market share or qualitative ratings of innovativeness. In essence, this criterion asks: Does this AI initiative help future-proof the business or give us an edge? If yes, it might be prioritized even if near-term returns are modest.
• Feasibility (Technical, Data, and Organizational): On the practicality side, feasibility metrics are key. This includes an assessment of technical difficulty (is the AI technique required well-understood or cutting-edge? Do we have the needed infrastructure?), data readiness (do we have sufficient data of good quality to power this use case?), and talent availability (do we have or can we acquire the AI expertise to execute it?). Gartner suggests evaluating technical feasibility by asking how well current technologies can address the problem and whether requisite data is accessible​ gartner.com.au. Organizational feasibility involves factors like stakeholder buy-in, cultural openness, and change management — sometimes called internal feasibility​ gartner.com.au. External feasibility covers regulatory compliance or customer acceptance; for instance, an AI use case involving personal data must consider privacy regulations (GDPR, etc.) which could complicate implementation gartner.com.au. Enterprises may score use cases on a feasibility index or mark any “red flags” (e.g. missing data, high algorithmic risk, regulatory hurdles). Often, a use case that ranks high on value but low on feasibility will either be broken into smaller phases to improve feasibility (such as doing a pilot to gather data first) or put on hold until conditions improve.

• Time to Impact and Complexity: Another practical criterion is how quickly the use case can be implemented and start delivering results. Some organizations prioritize projects that can show impact within, say, 6–12 months, to build momentum. Longer-horizon projects might be limited in number due to the patience and resources required. Complexity (in terms of number of systems to integrate or magnitude of process change) also factors in — highly complex, multi-year AI programs (like a full AI-driven business transformation) will be fewer and need strong justification versus simpler applications that slot into existing processes. That said, companies will often pursue a few complex strategic initiatives if they promise transformative value, balancing them with quick wins.

• Risk and Ethical Considerations: Enterprises increasingly include risk assessment in prioritization. This covers execution risk (likelihood of project failure or cost overrun) and outcome risk (potential negative consequences of the AI solution). If an AI use case has potential legal, ethical, or reputational risks — for example, an AI recruiting tool that could inadvertently discriminate — organizations weigh those heavily. Some might deprioritize high-risk use cases or require strong risk mitigation plans before proceeding. Gartner’s notion of AI governance (often via AI risk frameworks like Gartner’s “AI Trust, Risk and Security Management (TRiSM)”​ gartner.com.au) comes into play here. Mature enterprises set up cross-functional AI governance boards (including legal, compliance, security, etc.) to review proposed AI projects and ensure they meet standards for fairness, transparency, and security​ gartner.com.au gartner.com.au. Use cases that don’t pass these ethical and risk screens may be rejected or sent back for redesign.

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Case Studies: AI Use Case Identification and Prioritization in Action

To ground these concepts, we examine several brief case studies across different industries. These examples illustrate how real organizations identified high-impact AI use cases, the frameworks they used to prioritize them, challenges faced, and the outcomes achieved.

Financial Services: Transforming with AI at Societe Generale

Large banks have been aggressive in pursuing AI, but they face the challenge of coordinating AI opportunities across sprawling business lines (retail banking, investment banking, insurance, etc.). Societe Generale, a 150-year-old European bank, provides a compelling example of systematic AI use case management. According to MIT Sloan Management Review, SocGen’s digital strategy group quickly **gathered 100

• potential use cases** for generative AI from all areas of the business, reflecting the huge interest and possibilities identified by employees​ mitsloan.mit.edu. To turn this brainstorm into execution, the bank established a robust governance process: all AI use cases must be logged in a central portal, and each is evaluated for feasibility, risk, reusability, and value before being approved​ mitsloan.mit.edu. The bank sets global value targets (i.e. how much value they aim to generate from AI) and regularly communicates these to ensure projects align with strategic goals​ mitsloan.mit.edu.

Manufacturing: AI for Operational Efficiency at Hitachi

How did Hitachi identify and prioritize this use case? It started with a clear business goal: improve operational productivity without massive capital investment — classic kaizen. The company likely surveyed its processes and identified warehouse operations as a candidate where AI’s pattern recognition could yield insights (i.e. a feasible domain due to availability of process data and repetitive tasks). The expected impact (even a few percentage points of efficiency) represented millions in savings, making the business case attractive. In prioritizing, Hitachi’s leadership weighed this against other potential AI projects (like predictive maintenance on machines, or supply chain forecasting). The deciding factors were likely the availability of data (they had sensors and logs for worker activities), the alignment with their efficiency strategy, and manageable risk (the AI would advise workers, not control machinery — lower safety risk). By starting with this relatively contained use case, Hitachi could prove AI’s value on the factory floor and then expand. Indeed, many manufacturers follow a similar path: begin with a well-scoped AI project that delivers efficiency (e.g. defect detection via computer vision or energy usage optimization), then use that success to scale to more sites and explore more advanced use cases (like autonomous robots or digital twins).

Retail and E-Commerce: Personalization and Beyond

In retail and e-commerce, AI use cases have proliferated in areas like personalized recommendations, inventory optimization, and customer service chatbots. Amazon famously pioneered AI-driven recommendation engines (“Customers who bought X also bought Y”), which became a cornerstone use case due to its direct impact on sales and customer engagement. Most retailers followed suit once the value was evident. The prioritization of recommendation systems was straightforward: high strategic alignment (improving customer experience and basket size is a top priority in retail), proven ROI (Amazon reported significant revenue contributions from recommendations), and increasing feasibility with advancing machine learning algorithms and more customer data. By treating personalization as a must-have capability, retailers made it an early AI investment. This shows how an industry’s competitive dynamics can make certain AI use cases “table stakes” — effectively forcing all players to prioritize them.

Another example: Walmart and others use AI for demand forecasting and inventory management. These use cases often emerged from the business problem of overstocks and stockouts. They were prioritized because of clear ROI (reduce inventory carrying costs, avoid lost sales) and alignment with operational excellence strategy. Retailers typically started with pilots in a few product categories or regions, proved that AI models could forecast demand better than traditional methods, and then scaled up across their supply chain. The challenges here included data integration (sales, weather, promotions data needed to be consolidated) and change management (getting planners to trust AI forecasts), but the feasibility was boosted by advances in algorithms and cloud computing. Many retailers now consider AI-driven supply chain optimization as a high-impact, high-feasibility use case — essentially a “quick win” that also builds resilience.

Healthcare: Targeted AI Solutions with Clear ROI

Common thread in case studies: Organizations that succeed with enterprise AI don’t just identify use cases randomly — they tie them to business goals, rigorously assess them, and often start with focused projects that deliver proof of value. Whether it’s a bank, a manufacturer, a retailer, or a hospital, the winners create a virtuous cycle: _identify -

prioritize -

execute -

learn_, then feed that learning back into identifying the next round of opportunities. They also institutionalize this process via governance structures (e.g. AI councils, innovation labs) and ensure continuous alignment with strategy as the landscape evolves.

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Key Takeaways for Decision-Makers

Entering the AI era, enterprises face a vast array of possible applications — but only a subset will truly drive competitive advantage or ROI for a given company. The research and cases discussed provide several key takeaways for management when it comes to identifying and prioritizing high-impact AI use cases:

• Make Strategy the North Star: Anchor AI initiatives to your business strategy. Use cases should solve real business problems or advance strategic goals — whether that’s operational efficiency, superior customer experience, or new revenue streams. Avoid “AI for AI’s sake” projects. As studies show, aligning AI projects with strategic priorities is crucial; otherwise, you risk scattering efforts on interesting but low-value experiments​ mitsloan.mit.edu. A strategic lens also helps in communicating the “why” of AI to stakeholders and securing buy-in.
• Establish a Systematic Discovery Process: Treat use case identification as an ongoing, structured process rather than ad-hoc brainstorming. Leverage cross-functional teams to explore pain points and opportunities across the enterprise. Techniques like design thinking workshops, internal innovation challenges, and maintaining an idea repository can institutionalize opportunity discovery cmr.berkeley.edu cmr.berkeley.edu

• Encourage business units to continuously look for AI applications in their workflows and share learnings. The goal is to create a pipeline of vetted, high-potential AI ideas ready for evaluation.

• Evaluate Rigorously with Clear Criteria: Develop a transparent framework to evaluate and rank use cases. Use multi-criteria decision matrices or templates that include value (ROI potential, cost savings, revenue upside), strategic impact (alignment and differentiation), feasibility (data, tech, talent readiness), time-to-value, and risk. Involve stakeholders from finance, IT, and the business in scoring to get a holistic view​ gartner.com.au gartner.com.au. This rigor prevents pet projects from sneaking in unvetted and ensures resources go to initiatives with the strongest cases. As one example, Gartner’s AI prioritization approach of weighing business impact against feasibility, and looking for quick wins versus long-term bets, can help executives visualize which projects to do first​ gartner.com.au.

• Focus on Feasibility and Foundations: When ranking ideas, pay special attention to feasibility — many AI failures stem from picking a use case that sounded great but wasn’t realistically achievable with the current data or tech. It’s often wise to prioritize “data-ready” use cases early (those for which you have sufficient, quality data) and build out data infrastructure for others in parallel. Also ensure the necessary enablers (skills, governance, change management) are in place for each project. A less glamorous use case that your team can execute in 6 months may deliver more value than an “ideal” use case that would take 3 years of prep. In short, match ambitions to capabilities, growing both over time.

• Start Small, Demonstrate Value, Then Scale: Rather than betting everything on one moonshot, take an incremental approach. Pilot a few high-feasibility use cases first — show quick wins or tangible improvements. This builds organizational confidence, expertise, and momentum​ ir.coveo.com. Use those wins to justify more complex or innovative projects next. Many companies find success by scaling in waves: e.g. automate a handful of processes with AI, reinvest savings into more data science talent, then tackle predictive analytics use cases, and so on. Each iteration should expand the scope and impact of AI in the business. Critically, ensure you measure outcomes of initial projects and publicize the results internally (e.g. “AI reduced processing time by 40% in Dept X”) to galvanize support.
• Measure and Track Value Closely: Treat AI projects with the same discipline as other investments. Define clear KPIs for each use case (e.g. reduction in error rates, increase in conversion, cost per transaction, etc.) and track them during and after implementation gartner.com.au gartner.com.au. If a project isn’t delivering the expected value, investigate why and decide whether to pivot or halt. Conversely, double down on the approaches that work. Societe Generale’s practice of comparing realized value to initial estimates is a good model for accountability mitsloan.mit.edu. Over time, this will improve your ability to choose winners. Remember, what gets measured gets managed — and in AI, business value delivered is the ultimate metric of success.
• Don’t Neglect Innovation and Learning: While focusing on ROI, leave room for experimentation and learning. Some AI use cases won’t have a clear short-term ROI but could be strategically important (think of early investments in AI that later became industry game-changers). Allocate a portion of your AI portfolio to exploratory projects or proofs-of-concept that might unlock new capabilities​ kenaninstitute.unc.edu. The key is to manage this like a venture portfolio — small bets, stage-gated by results. Encourage a culture where lessons from even “failed” use case experiments are captured and shared (e.g. finding out a certain approach doesn’t work is valuable knowledge). This keeps the organization innovative and prepared for future technology waves.
• Ensure Governance and Ethical Oversight: Finally, incorporate risk and ethics in decision criteria from the start. Develop guidelines for what kinds of AI use cases are acceptable (compliant, fair, aligned with company values) and set up oversight (AI ethics committees or review boards) to evaluate projects, especially customer-facing or high-stakes ones​ gartner.com.au. Prioritize use cases that enhance trust and avoid those that could erode it. Gartner’s concept of “Trust ROI” — the idea that AI outcomes must be trusted by users and customers — is vital. For instance, a use case involving AI decisions that affect customers (loans, medical advice, etc.) might be lower priority until robust guardrails are in place. Proactively addressing these considerations will smooth deployment and adoption when the time comes.

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References and Further Reading

1. CrossML: AI Use Case Discovery Services — Unlock AI Potential
2. ActionIQ: The IDEAL Framework: A Recipe to Deploy an AI Use Case
3. Lantern Studios: Prioritizing AI Use Cases: A Horizon-Based Approach
4. Multimodal: How to Identify AI Use Cases for Your Business [+Free Template]
5. Microsoft Learn: Evaluating and prioritizing an AI use case with ISV business envisioning
6. Planview: Choosing Your AI Use Case: A Prioritization Framework
7. Graft: A Guide to Prioritizing AI Use Cases for Success [Free Template]
8. Emerj: Establishing Measurable ROI Benchmarks for AI Projects — A 5-Step Process

9. Deloitte:

• State of Generative AI in the Enterprise 2024
• Strategic governance of AI: A roadmap for the future
• Trustworthy Artificial Intelligence (AI)™
• Generative AI for Enterprises

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1. McKinsey & Company**:

• Superagency in the workplace: Empowering people to unlock AI’s full potential
• The state of AI: How organizations are rewiring to capture value
• Upskilling and reskilling priorities for the gen AI era
• The executive’s AI playbook

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1. Boston Consulting Group (BCG)**:

• AI @ Scale | AI Consulting and Strategy
• The Leader’s Guide to Transforming with AI
• From Potential to Profit: Closing the AI Impact Gap

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1. Google Cloud**: KPIs for Gen AI: Measuring your AI success

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1. Pandata**: Reliable KPIs for AI: How To Measure Success

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1. Techstack**: Measuring the ROI of AI: Key Metrics and Strategies

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1. Pecan AI**: How to Measure (and Increase) the ROI of AI Initiatives

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1. NIST**: AI Risk Management Framework