How to build an effective AI business strategy
Unlocking AI-driven value, efficiency and transformation
Rapidly evolving AI technology has become a cornerstone for businesses aiming to enhance productivity, efficiency and innovation. It is no longer a luxury but a necessity for gaining and maintaining a competitive edge. From automating routine tasks and accelerating decision making to enabling strategic business models, artificial intelligence is quickly transforming how middle market firms operate.
According to the RSM Middle Market AI Survey 2025: U.S. and Canada, 91% of total respondents said their organization uses AI, either formally or informally, in business practices. This marks a significant increase from 78% last year, emphasizing AI’s grip across businesses and industries. However, even with such broad use, 92% of respondents faced challenges implementing AI, primarily due to poor data quality (41%), data privacy and security concerns (39%), and lack of internal skills (35%).
As these digital capabilities continue to expand and evolve, so do the challenges of maintaining quality data, as well as managing risks and compliance. To move beyond experimentation to full-scale integration, business leaders and decision makers must play a pivotal role in implementing an effective AI business strategyand then ensuring data analytics and AI strategy are fully aligned with it.
AI strategy and assessment: Building a robust foundation
To fully harness the potential of AI, organizations must build a robust foundation that includes data governance, operational and technical processes. Though complex, this foundational step is essential for defining measurable return on investment and deploying AI solutions that align with enterprise AI strategy.
Many companies struggle with laying that initial groundwork: The RSM AI survey reports that 53% of respondents in organizations that have adopted and implemented generative AI believe they were only somewhat prepared to do so and 70% of respondents overall said they needed outside help to get the most out of their AI solutions.
To support responsible AI adoption, organizations need an effective AI strategy and a readiness assessment.
AI strategy development: Decision makers and leaders must align AI initiatives with their business goals. The focus should be to balance quick wins with long-term growth. Steps include:
- Assessing high-impact use cases that align with the firm’s business strategy
- Quantifying potential financial benefits, such as cost savings, revenue growth and risk mitigation
- Establishing AI governance frameworks across workflows for responsible AI usage
AI readiness assessment: Before moving ahead with implementation, firms must thoroughly evaluate their AI readiness in terms of:
- The current state of data, information technology and operational processes
- Data quality, availability and accessibility
- Organizational readiness for managing AI initiatives
Process design and tool and vendor selection: Operationalizing the AI strategy
Developing AI processes and optimizing vendor governance require a structured approach. Together, these elements enable the creation of targeted, effective AI solutions that align with the firm’s strategic vision and are scalable across workflows and systems.
AI process design: AI processes must seamlessly integrate with existing systems. Leaders should assess existing and potential future business processes to establish ROI targets and key performance indicators (KPIs) for AI solutions. This involves:
- Analyzing process workflows, identifying key touchpoints and potential risks
- Identifying gaps within existing business operations to design targeted AI solutions
- Planning for compliance with data protection standards and industry-specific requirements
AI tool and vendor selection: Selecting the right AI solutions and optimizing vendor management involve:
- Evaluating AI solutions and quantifying potential financial and operational outcomes
- Conducting thorough vendor assessments to determine the best solutions that align with business goals
The pillars of AI value creation
The effectiveness of any AI solution rests on four foundational pillars:
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Revenue generationAI can unlock new growth opportunities through:
- Personalized customer engagement to enhance experience and loyalty
- Upselling and cross-selling with dynamic pricing models
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Expense reductionAI helps reduce costs through:
- Process automation to minimize manual tasks
- Efficient resource management that optimizes IT infrastructure and energy use
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Efficiency gainsAI can enhance efficiency by:
- Providing deeper insights to make informed, strategic decisions
- Improving supply chain management by reducing delays and optimizing inventory\
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Quality improvementAI enhances quality by:
- Increasing accuracy in data handling and processes
- Enhancing quality control processes for product offerings
AI framework process: A step-by-step journey
Ultimately, the key steps for a successful AI implementation strategy include:
Frequently asked questions
The takeaway
No one can predict the next breakthrough in AI. Businesses are trying to capitalize the best they can to optimize workflows and generate lasting value and impact. An AI strategy built on extensive, detailed assessment and preparation helps enhance and accelerate the value these digital tools and services can bring to an organization.
To guide them in this effort, organizations can benefit from consulting with experienced AI advisors. RSM’s AI advisory team understands the enterprise AI journey and the foundational elements necessary to generate increased value and reduce risk. Contact our team to learn more about how AI can transform your key business operations.