The Business of AI: A Roadmap for Strategic Adoption

A step-by-step guide to implementing AI in your organization, including readiness assessment, use cases prioritization, cost evaluation and more

Brian Silverman March 10, 2025

Editor’s Note: This is the final installment of “The Business of AI” series. In case you missed them, the first seven articles are linked at the bottom of this page.

Throughout “The Business of AI” series, we have explored how organizations can harness AI effectively.

As Michael Curry highlighted in our recent interview, organizations that focus on the right use cases—those aligned with business goals and backed by the necessary infrastructure—are best positioned to achieve real value from AI investments.

This roadmap offers a structured approach to evaluating, prioritizing and implementing AI solutions, ensuring a sustainable foundation for high-impact adoption. Throughout, we reference insights from other articles in “The Business of AI” series to provide deeper guidance on key topics.

Step 1: Organizational Readiness for AI

For organizations to successfully adopt AI, they must establish a culture that is open to learning and experimentation, while ensuring leadership and employees understand AI’s potential and limitations. Clear objectives, practical applications and a knowledgeable, supportive leadership team are essential for driving meaningful AI adoption. Take these actions:

Evaluate leadership and workforce AI awareness.
Form an AI steering committee for strategic oversight.
Promote ongoing education (internal content and external courses).

Reference article: “Focus on ‘The Business of AI’ to Move From Hype to ROI“

Step 2: Align AI with Organizational Priorities

One key element of AI success is aligning an organization’s AI strategy and development to address key strategic priorities and objectives. Take these actions:

Conduct brainstorming sessions with stakeholders.
Identify and categorize AI use cases (productivity, automation, reinvention).
Prioritize quick wins for early success.

Reference article: “The Business of AI: A Look at the Different Types of AI and Their Value“

Step 3: Evaluate Use Cases—Is AI the Right Solution?

Not all challenges need AI. As organizations progress through this roadmap, they should continuously evaluate whether their prioritized use cases align with their readiness, technology fit and existing applications. This includes assessing whether AI is truly necessary or if enhancements to current processes and systems would be a more effective solution. Take these actions:

Assess AI’s advantage over existing processes, applications or technologies, ensuring it provides clear benefits over alternative solutions.
Evaluate whether foundational processes, data or technology gaps must be addressed before AI implementation.
Ensure AI use cases align with organizational readiness, existing applications and strategic priorities.

Step 4: Define and Prioritize AI Use Cases

Establish a standardized AI use case evaluation template to ensure a consistent, structured approach to assessing AI opportunities. This template should be created and regularly updated to document key factors such as potential benefits, technology and data requirements, organizational readiness, compliance considerations and feasibility. Take these actions:

Document use cases clearly (opportunity, technology, readiness, compliance, costs).
Standardize scoring system for prioritization.

Step 5: Data Readiness—The Fuel for AI

Data is the foundation of AI success. Regardless of the AI type, organizations must ensure that data is accurate, accessible and aligned with business needs. Poor data quality or lack of access can undermine AI effectiveness and increase costs. Take these actions:

Ensure data is accurate, current, compliant, unbiased and scalable.
Plan data integration (ETL, APIs) and real-time needs.

Reference article: “The Business of AI: Current Applications and Data Fuel AI Innovation and Solutions“

Step 6: Application Readiness—The Foundation for AI Integration

Applications—both AI-powered and non-AI—play a crucial role in AI adoption. Organizations must assess their existing application landscape and determine how AI will integrate into workflows, ensuring smooth interoperability with enterprise systems and external services. Consider the following:

Inventory applications (ERP, CRM, custom solutions).
Evaluate application compatibility, interoperability and integration points with AI.

Reference article: “The Business of AI: Current Applications and Data Fuel AI Innovation and Solutions“

Step 7: API Evaluation & Planning

APIs are the essential wiring that connects AI applications and agents. They enable AI to interact with enterprise applications (ERP, CRM), external services (payments, shipping), and other AI-driven solutions to execute tasks and deliver business value. Take these actions:

Confirm API availability or modernization needs for required applications and data.
Evaluate external APIs for integration.
Define clear API governance and security strategy.

Reference article: “The Business of AI: The API Advantage—Getting to Work with Generative AI“

Step 8: Re-Evaluate AI Feasibility for Prioritized Use Cases

With key AI readiness factors now assessed and the AI use case template from Step 4 completed for each prioritized use case, organizations should take this opportunity to re-evaluate whether their selected AI initiatives are viable for development and implementation or if foundational improvements are needed first. Take these actions:

Address gaps in data quality, application capabilities and API accessibility, reprioritizing as necessary.
Reprioritize or defer AI initiatives as needed based on foundational readiness.

Step 9: Choosing the Right Type of AI for Your Use Cases

In Step 2 of this roadmap, you prioritized AI use cases into objectives to improve productivity, automate key business processes or reinvent and innovate new business opportunities. It is crucial to align each prioritized use case with the most suitable AI type to ensure effective implementation and maximize business value. Take these actions:

Match each use case to the various types of AI—traditional, generative or agentic—based on objectives such as productivity enhancement, process automation and reinvention.
Research current AI solution directories to identify existing solutions that could accelerate deployment.
Evaluate the expected benefits and business impact of the AI type and solution to ensure alignment with strategic goals.

Reference article: “The Business of AI: From Foundation Models to Large Language Models”

Step 10: Building and Training AI—From Models to Deployment

After selecting the right AI type and business use case, organizations must develop a clear plan for training AI models and deploying them effectively. Each AI type requires distinct strategies for data preparation, model selection and continuous refinement to ensure optimal performance. Take these actions:

Specific AI training plans for traditional, generative and agentic AI use cases.
Focus on data preparation, model selection, governance and integration into existing workflows.
Implement best practices for deployment, continuous monitoring and refinement.

Reference article: In the addendum to the article, “The Business of AI: Current Applications and Data Fuel AI Innovation and Solutions,” there are tables with additional information on AI training.

Step 11: System and Infrastructure—Deploying Your AI Solution

Once an organization has selected an AI model and training approach, the next step is determining the best deployment strategy. AI solutions may run in the cloud, on-premises or in hybrid environments, depending on performance, security and compliance needs. Take these actions:

Determine optimal deployment (cloud, on-prem, hybrid).
Ensure infrastructure supports seamless AI integration and operations.
Address performance, compliance, scalability and governance considerations.

Reference article: “The Business of AI: Unlocking AI for Legacy Systems, With Michael Curry of Rocket Software“

Step 12: AI Security—Protecting AI Systems and Data

AI adoption introduces unique security challenges beyond traditional cybersecurity concerns. Organizations must ensure that AI solutions are secure, protected against misuse and aligned with enterprise security policies. Take these actions:

Establish AI-specific cybersecurity measures, including identity and access management, data protection, API security and cybersecurity integration to ensure data integrity and system reliability.
Integrate and align AI security within broader enterprise cybersecurity strategies.

Ref: “The Business of AI: Cybersecurity and Trust in the Age of AI“

Step 13: AI Trust and Governance—Ensuring Responsible AI Use

Ensuring AI trust and governance goes beyond security—organizations must establish clear policies to align AI with existing processes, ethical standards and regulatory requirements. A well-structured governance framework promotes transparency, accountability and responsible AI deployment, ensuring AI solutions support business objectives while mitigating risks. Take these actions:

Establish governance policies to ensure AI aligns with organizational culture, processes and rules.
Emphasize transparency, explainability, ethical AI and compliance frameworks.
Use governance committees for ongoing oversight and adjustments.

Ref: “The Business of AI: Cybersecurity and Trust in the Age of AI“

Step 14: Evaluating AI Costs vs. Expected Value

AI investments require careful cost analysis to ensure a strong return on investment. The best AI solution will not succeed if the costs far outweigh the benefits, making cost-benefit evaluation critical when prioritizing AI use cases. Take these actions:

Conduct thorough cost-benefit analysis covering infrastructure, model licensing, data, training and hidden expenses.
Structure analysis around productivity, automation or reinvention goals.

Reference article: In “The Business of AI: From Foundation Models to Large Language Models,” there is detail explaining the different approaches to cost for AI, including the different models and approaches for Generative AI.

Step 15: AI Training & Organizational Readiness

By prioritizing continuous education, cybersecurity awareness and governance, organizations can foster a well-trained workforce that maximizes AI’s benefits while mitigating risks. Take these actions:

Provide continuous role-specific AI training (awareness, security, governance, ethical use).
Regularly update training aligned with evolving AI capabilities.

Step 16: Final AI Feasibility Review for Prioritized Use Cases

With fully developed AI use cases and updated use case evaluation templates, it’s time to reassess prioritized use cases. This is an opportunity to elevate high-impact AI use cases, deprioritize those that may not be viable in the short term or determine whether certain opportunities are better suited to a non-AI approach. Take these actions:

Comprehensive final evaluation considering all readiness, security, governance and cost-benefit factors.
Confirm viability or defer/redefine use cases clearly.

Step 17: Report to the Steering Committee and Executive Leadership

To ensure successful AI adoption, organizations must secure alignment and support from key decision makers. This final step focuses on presenting a clear summary of AI initiatives and their strategic impact to the AI steering committee and executive leadership. Take these actions:

Provide a structured review of the AI planning process and key insights.
Present prioritized AI use cases with justifications for selection.
Outline next steps, expected outcomes and required resources for execution

Ongoing Recommendations for Your AI Roadmap:

AI adoption is an ongoing journey, not a one-time initiative. As AI technologies continue to evolve, organizations must remain agile and proactive in refining their AI strategy to ensure alignment with business goals and technological advancements. Consider the following:

Establish a process for regularly monitoring AI advancements and assessing their potential impact.
Reassess AI opportunities based on emerging technologies and internal IT improvements.
Balance staying informed about industry trends with prioritizing practical business value over hype.
Maintain agility in your roadmap, regularly reassess new AI capabilities and update your AI roadmap as internal capabilities evolve and new opportunities become viable.