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 strategy and 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.

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.

The pillars of AI value creation

The effectiveness of any AI solution rests on four foundational pillars:

1. Revenue generation
   AI can unlock new growth opportunities through:
   • Personalized customer engagement to enhance experience and loyalty
   • Upselling and cross-selling with dynamic pricing models
      
2. Expense reduction
   AI helps reduce costs through:
   • Process automation to minimize manual tasks
   • Efficient resource management that optimizes IT infrastructure and energy use
      
3. Efficiency gains
   AI can enhance efficiency by:
   • Providing deeper insights to make informed, strategic decisions
   • Improving supply chain management by reducing delays and optimizing inventory
     
     
4. Quality improvement
   AI 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