How to Build an AI Strategy that Actually Drives Business Value – Mid-Market Edition

If your board asked you today what your AI strategy is, would you have an answer?

For many organizations, the reality is they have a handful of experiments, but no strategy. Every mid-market leadership team hears about AI. Vendors are pitching copilots. Teams are testing tools on their own. But what’s the next step?

An effective AI strategy is a practical, business-driven plan that connects AI investments to specific outcomes: cost savings, revenue growth, and risk reduction. This fits the reality of a mid-market organization in the Midwest or beyond.

AI Strategy vs. AI Initiatives: What’s the Difference?

Most organizations start their AI journey with isolated initiatives: Someone tries a chatbot, a business unit buys an AI add-on, or IT pilots a new feature on a major platform. That is normal, but it is not a strategy.

• AI Initiatives are individual projects or experiments.
• AI Strategy is the overarching decision about where AI belongs in your business, why it belongs there, and how you will roll it out over time.

A Simple Way to See the Gap

Consider a financial services organization that New Resources Consulting (NRC) worked with that was struggling with knowledge access. Critical information lived in documents and systems that were scattered across the business, and staff spent far too much time hunting for answers to customer questions with poor results to show for their efforts.

An “AI initiative” approach might have looked like:

• Turning on a generic AI feature in an existing system
• Letting a few team members experiment with off-the-shelf tools on their own

Instead, the leadership team stepped back and asked strategic questions:

• How much time and money did we lose today because people can’t find information quickly?
• How would faster search options and better answers change customer experience?
• Where does this fit in relation to our other priorities this year?

That led to a decision: AI-powered search should be a top-priority use case in their AI strategy. NRC’s AI Solutions Group (AIG) then designed and implemented an AI-powered search solution using OpenAI GPT-4 and Azure AI Search, drastically reducing search times and improving customer satisfaction. This saved the client up to $200,000 per month.

The difference is important: The organization did not chase “AI” in the abstract. It chose a concrete problem, aligned it with business value, and made AI one of the tools to solve it.

Step 1: Start with Business Outcomes, not AI Features

An AI strategy that actually drives value starts with business priorities, not with a catalog of AI capabilities.

For mid-market leaders, the most useful starting questions are:

• What are the 3 – 5 most important business outcomes over the next 12 – 24 months?
  • Margin improvement?
  • Faster quote or order turnaround?
  • Better customer or member experience?
  • Reduced operational or compliance risk?
• Where are people stuck doing repetitive, manual, or judgment-heavy work that could be augmented with better data and AI?
• Which customer or employee experiences feel slow, frustrating, or inconsistent?

From there, you can begin to sketch candidate AI use cases under each outcome, for example:

• Outcome: Faster RFQ turnaround and better win rates
  • AI Use Case: Automatically classify, extract, and route RFQ details from inbound emails and documents to speed evaluation and quoting.
• Outcome: More efficient operations in a shared services or contact center environment
  • AI Use Case: AI-powered search that surfaces the right policy, procedure, or answer in seconds instead of minutes.

At this stage, the goal is not to design the solution. It is to identify where AI could matter most.

Step 2: Make AI Strategy Concrete for Mid-Market Realities

Enterprise AI Strategy templates often assume large teams with bigger budgets and plenty of lead time. Mid-market organizations need a more focused process. NRC’s AIG work with mid-market clients shows that a practical AI strategy usually fits on a few pages and answers four questions clearly:

1. What are we trying to achieve with AI, in business terms?

• A short list of outcomes like “reduce RFQ processing time by 70%,” “cut knowledge search time in half,” or “improve pricing accuracy in key product lines.”

2. Which use cases are we prioritizing first?

• 2 – 4 specific use cases, not 20. Each should be tied to a business outcome and owner.

3. What do we need to have in place before we start?

• Data, systems, security, and governance readiness for those use cases.

4. What does the 12 – 24 month roadmap look like?

• A phased sequence of education, pilots, and scale-up that fits your bandwidth.

If your current AI efforts cannot be summarized this way, you likely have scattered initiatives rather than a strategy.

Step 3: Map AI to Business Outcomes Using Real Examples

Once you know your top business outcomes, you can map specific AI use cases to them. Concrete examples from organizations like yours make this process easier.

Example 1: AI-Powered Search for Financial Services

For the financial firm mentioned earlier, the business outcomes were:

• Reduce time spent searching for information
• Improve customer response times and accuracy

By aligning those outcomes, AI-powered search became an obvious strategic use case. NRC’s AIG team implemented a solution using OpenAI GPT-4 and Azure AI Search that:

• Dramatically reduced search and knowledge retrieval times
• Enabled staff to handle queries more confidently and quickly
• Led to the saving of up to $200,000 per month and a better customer experience

The AI strategy here was not “let’s deploy GPT-4.” It was “let’s solve our knowledge retrieval problem in a measurable way.”

Example 2: Cognitive Search for RFQs in Manufacturing

In another case, a global manufacturer was overwhelmed by RFQ volume. Teams had to manually open, read, and interpret hundreds or thousands of RFQ emails and attachments, consuming valuable time and creating delays in responding to potential business.

The business outcomes were clear:

• Cut RFQ processing time
• Reduce manual effort and overtime
• Increase capacity to respond to more opportunities without adding headcount

NRC implemented an Azure Cognitive Search solution that automatically processes and categorizes RFQ content, reducing the average handling time per RFQ from 13.2 minutes to 2.1 minutes and saving more than $500,000 annually.

For many manufacturers across the Rust Belt, especially those drowning in RFQs, this kind of AI-enabled process improvement is exactly what an AI strategy should prioritize: specific, measurable, and tightly tied to how the business actually makes money.

Step 4: Choose the Right Early Wins and Quick ROI

Not every attractive AI idea is a good first move. A strong mid-market AI strategy deliberately chooses early wins with three attributes:

1. High business impact

• The use case touches real cost, revenue, or risk, not just “nice to have” improvements.
• Example: RFQ processing or search time, where minutes and errors directly impact sales and service.

2. Reasonable feasibility

• The required data exists or can be assembled without a massive multi-year data program.
• The process is well understood and repeatable enough for AI to add value.

3. Manageable risk

• Early use cases do not make irreversible decisions without human oversight.
• Failure modes are tolerable (e.g., the system suggests, but a human decides).

For many organizations, strong candidates for early wins include:

• AI-assisted search across policies, procedures, and knowledge bases
• Intelligent document processing (invoices, RFQs, claims, applications)
• AI-augmented pricing or risk models where humans remain in the loop

The RFQ cognitive search example is a textbook early win: well-defined inputs and outputs, clear business value, and humans still involved where judgment matters.

Step 5: Design Governance and Decision-Making Frameworks from the Start

An AI strategy that ignores governance is a liability. For mid-market enterprises, the goal is not to copy a global bank’s governance model, but to implement right-sized guardrails to enable AI to scale safely.

Effective AI governance at this stage usually covers:

• Roles and accountability
  • Who approves new AI use cases?
  • Who owns model behavior and performance over time?
• Data and security rules
  • What data can and cannot be used with external AI services?
  • How are access controls, logging, and monitoring handled?
• Use case approval criteria
  • Business value: Is the use case aligned with strategy?
  • Risk level: What could go wrong, and how will it be controlled?
  • Readiness: Are data, processes, and ownership in place?

NRC’s AI Solution Group often helps clients define simple governance artifacts as part of strategy work:

• A use case intake and evaluation template
• A lightweight AI policy for staff and contractors
• A review cadence (e.g., quarterly) for active AI initiatives

These frameworks do not slow you down. They help you say “yes” and “no” more confidently.

Step 6: Turn Strategy into a Phased Roadmap

With outcomes, use cases, early wins, and governance defined, you can build a realistic roadmap. For mid-market organizations, a common structure looks like:

Phase 1: Educate and align (0 – 3 months)

• Executive AI briefing and workshop
• Clarify business outcomes and candidate use cases
• Agree on initial governance and decision-making approach

Phase 2: Prioritize and prototype (3 – 9 months)

• Select 2 – 3 high-value, feasible use cases
• Run rapid prototypes or proofs-of-concept (e.g., AI search, RFQ processing, document processing)
• Measure impact, capture lessons learned, refine governance

Phase 3: Scale and integrate (9 – 24 months)

• Take successful use cases into production with appropriate engineering and MLOps
• Integrate AI into existing systems and workflows (ERP, portals, line-of-business tools)
• Expand the portfolio of AI initiatives gradually, using the same evaluation criteria

This phased approach helps organizations move quickly without betting everything on a single big-bang project.

Step 7: Make AI Strategy a Cross-Functional Conversation

The most successful AI strategies are not written in isolation by IT or a single business unit. They are cross-functional by design.

For each priority use case, you should have:

• A business owner (P&L, operations, or functional leader)
• An IT/data partner who understands systems and data constraints
• Representation from risk, compliance, or legal where appropriate
• Front-line input from the people who will actually use the AI-enabled process or tool

This is one reason NRC’s AI Innovation Workshop has become a common starting point: It brings these stakeholders into a structured, facilitated session where they can align goals, risks, and next steps together.

Step 8: Treat Your Strategy as a Living Document

AI will not stand still, and neither will your business. A static “AI strategy” created in 2026 and left untouched will quickly lose relevance.

Instead, treat your AI strategy as a living document:

• Revisit it at least annually, or more often if there are major business or regulatory changes.
• Use post-mortems from pilots and implementations to refine your selection criteria and governance.
• Add new use cases only if they support your core outcomes and pass your risk and readiness tests.

NRC’s AIG engagements often include a plan for ongoing strategy refresh, so your AI roadmap evolves with your organization rather than becoming shelfware.

Next Step: Explore NRC’s AI Innovation Workshop

If your leadership team is asking for an AI strategy, but your current reality looks more like a handful of disconnected experiments, it may be time to bring people into a structured conversation.

NRC’s AI Innovation Workshop is designed specifically for organizations that want to:

• Build a shared understanding of what AI can and cannot do for their business
• Identify and prioritize a short list of high-impact, but feasible, AI use cases
• Discuss risks, governance, and change implications openly with IT, business, and risk leaders
• Leave with a practical starting roadmap (and not a 200-page slide deck)

For many clients, this workshop is the bridge between “we know AI matters” and “we have a clear, business-driven AI strategy we can execute.”