Establishing an AI strategy and implementation plan that fits your organization

An effective artificial intelligence strategy and implementation plan have rapidly become necessary elements of a successful business, creating critical opportunities to run smarter, grow faster and stay protected. However, many companies struggle to develop an optimal AI implementation approach that aligns with their overall organizational strategy and business needs.

Successful AI implementation is not a one-size-fits-all project. Implementation is a thoughtful process that integrates AI tools and strategies into company processes and operations, but defining priorities and outlining beneficial use cases can vary significantly between companies. Getting to an AI implementation strategy that fits, however, can have rapid transformative effects, from improving customer relations and managing risks more effectively to increasing efficiency and identifying opportunities for growth. 

Key considerations for AI implementation success include understanding what business problems to target with AI, what solutions can best address those issues, how to analyze the return on investment and how to put your strategy into action.

To illustrate the AI implementation challenges companies face, the 2025 RSM Middle Market AI Survey: U.S. and Canada found that 92% of middle market executives experienced challenges with AI implementation. In addition, 62% said generative AI was harder to implement than expected, and 70% of those using generative AI report they need outside assistance to get the most out of that tool.

Steps for traditional AI implementation include identifying a problem, defining objectives, evaluating data, selecting technology, designing a framework, testing, refining and, ultimately, deploying. However, RSM’s experienced AI consulting team has condensed the traditional approach into four proven steps that integrate AI strategy and implementation to tailor AI to your goals, deliver desired results and maximize your return on investment.

1. Start with what you already own in embedded AI

Exploring these options first represents the quickest, lowest-friction wins, because they are already implemented—they don’t require new data pipelines or major change management. Since these are not new tools, employees are already using them to some extent and they are familiar with the systems, so training efforts and adoption times should be minimal.

Conducting an audit of your technology platforms to detail the depth of existing AI features and functionality is an important exercise. Depending on the size of your organization and digital footprint, that audit can become a complex process, but it is a crucial step to align AI tools to business goals, and it will only become more of a challenge as growth continues and new technology platforms are integrated.

After the audit, your organization can activate the AI features that could benefit you most and encourage internal teams to maximize adoption of these applications first. This means understanding the capabilities of tools like copilots, AI assistants or embedded AI features that are already included in your core systems. In some cases, additional licenses or platform upgrades may be necessary to expand the use of existing AI functionality, but spending on duplicate solutions may also be identified to reduce costs.

2. Extend and configure AI with tailored intelligence

This process is about shaping AI to reflect your specific language, processes and metrics—so outputs are more specific and relevant to your business model. With this level of modification, you can make better business decisions based on insights that are aligned with your operations and business drivers. However, you may still be constrained by vendor platform guardrails within solutions that may not fully meet your needs.

3. Build where you need custom AI differentiation

These issues are examples of where building specific AI solutions or enlisting the help of a trusted advisor to co-develop custom tools can provide a competitive advantage and enhance risk mitigation. Rather than flipping a switch or activating a general AI application within an existing technology platform that you cannot configure on your own, getting to AI that fits your business sometimes requires custom AI solutions that allow more control and fine-tuning.

The benefit of today’s purpose-built solutions, relative to custom options of the past, is new industry-specific accelerators and similar industry-centric platforms from vendors like Microsoft that help accelerate time to value and bring stability to deployments in ways that were previously cost- or time-prohibitive.

To obtain accurate, relevant and actionable outputs and performance from an AI model for your specific business drivers, you need to have elevated configuration capabilities and the model needs to be trained on your organization’s specific data. This functionality is typically not possible with AI solutions embedded within existing technology platforms.

For example, forecasting is a common challenge within many organizations, resulting in poor planning processes, excess inventory, etc. Training a forecasting AI model on your data that is specific to your customers, products and business dimensions will be unique, and provide much better business outcomes relative to a generalized model that doesn’t understand the nuances in your data. The model uses your data, your rules and your workflows in a bespoke way to drive more targeted and actionable results.

More general AI solutions are effective for personal productivity and increasing efficiency, but those will not be a differentiating factor for your business in the marketplace. Instead, focusing more of your time and energy on custom AI solutions for your unique use cases can lead to critical insights and enable nimble decision making that can quickly establish a significant competitive edge.

4. Balance your AI portfolio

Companies often wrestle with whether to buy, configure or build AI applications, but in reality, companies often employ a mix of all three strategies to optimize AI strategies, depending on what level of insight and analysis is necessary to support a specific business function.

To determine the right fit for specific use cases and how to truly differentiate and successfully leverage AI to help your business run and grow, you need to understand how processes are done today and how they can be done differently, and better, moving forward. Some functions can be optimized with embedded AI solutions or by extending existing applications with your proprietary data.

However, to truly accelerate growth and generate significant outcomes, you can identify where AI agents can be built to manage entire workflows and provide end-to-end support for key business functions.