5 Steps to Kickstart AI Implementation for SMEs (Crawl, Walk, Run Framework)

Step 1: Identify High-Impact AI Opportunities 

(Crawl Stage – Ideation)

Begin your AI journey by ideating and pinpointing use cases where AI can add value to your business. Rather than trying to “do AI” everywhere at once, start with strategic low-hanging fruit. Focus on problems that align with your core business goals and have clear metrics for success.

As Harvard Business School’s Iavor Bojinov advises, choose AI projects that “align with strategic goals, deliver measurable impacts, and have clear use cases” . In practice, that means targeting use cases likely to show immediate benefit with minimal effort.

Hatz AI recommends looking for universal use cases – “low-hanging fruit” applications that deliver immediate value, work broadly across the company, and can be implemented quickly. At the Crawl stage, document these ideas and gauge feasibility, building a basic understanding of how AI might solve each problem. This upfront clarity ensures your first AI steps address real business needs and set the stage for quick wins.

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Step 2: Educate and Prepare Your Team 

(Crawl Stage – Foundations)

With promising use cases in mind, lay a strong foundation by upskilling your team and establishing governance. Early AI projects often falter due to lack of internal understanding or oversight. Many businesses still aren’t “investing in upskilling and training their employees to make the most of this generational moment”, a recent Hatz AI report warns.

Don’t let that be your SME – equip your staff with basic AI knowledge and hands-on training. In fact, nearly half of employees want more AI training, yet many leaders aren’t moving fast enough to provide it. Managed Service Providers (MSPs) can play a key role here by running workshops on AI tools (like chatbots or data analytics) and helping draft acceptable-use policies. Also, set up the right tools and safeguards from the start.

As one guide suggests, “implement a secure AI platform, establish basic governance, [and] train key team members” as initial steps. In the Crawl phase, this preparation builds AI fluency and trust. Your team will be more confident using AI, and you’ll have policies to prevent mishaps (for example, avoiding sensitive data leaks or unethical AI use). A well-prepared foundation means you can move to implementation with your people on board and risks mitigated.

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Step 3: Launch Small Pilots and Iterate 

(Walk Stage – Initial Implementation)

Now it’s time to get hands-on. In the Walk stage, develop a pilot project or prototype for one of your high-value use cases. Start small and focused – build a minimum viable AI solution rather than a mission-critical system on day one. This could be as simple as a chatbot answering common customer questions or an AI tool automating a step in your workflow.

The key is to iterate with real feedback: involve end-users early, test the AI in a controlled setting, and refine it in short cycles. Research shows that companies seeing success with AI often pursue “‘small t’ transformations,” finding ways to derive real value from AI without overhauling entire processes . In other words, don’t try to reinvent your whole business with AI overnight. Instead, implement a narrow solution, see how it performs, and improve it gradually.

This agile approach aligns with the “Walk” mindset – you’re taking your first real steps by deploying AI in practice. MSPs can assist by setting up the pilot environment and tuning prompts or model parameters. Remember, the goal of a pilot is learning: figure out what works, what doesn’t, and gather tangible examples of AI-driven improvements. These quick wins not only solve the immediate problem but also build confidence in AI across the organization.

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Step 4: Evaluate Results and Refine the Approach 

(Run Stage – Evaluation & Improvement)

After deploying an initial AI solution, rigorously evaluate its impact. This step, corresponding to the early “Run” phase, is about measuring outcomes and making improvements before scaling up. Define clear metrics (e.g. time saved, error reduction, sales growth) and compare performance with and without the AI. Harvard expert Iavor Bojinov stresses the importance of testing AI projects thoroughly – “rigorously test [solutions] through methods like A/B testing to ensure the model delivers real value before widespread rollout” . If the results fall short, analyze why: Do you need more data? Fine-tuned prompts? Additional training for users? Use this feedback to refine the AI system or even reconsider the use case if needed.

It’s equally crucial to manage risks at this stage. Evaluate the AI’s outputs for accuracy, bias, and compliance with your policies. McKinsey advises companies to “prioritize AI safety and ethics—ensure transparency and fairness” as they adopt AI . For example, if your AI tool makes decisions, make sure you (and your team) understand how and that it treats customers fairly. By measuring and communicating success (or lessons learned), you also keep stakeholders engaged. Hatz AI suggests tracking quick-win pilots closely – “measure and communicate [their] success” and share the results to “build momentum through visible results”.

In sum, use data to prove the value of your AI initiative and to spot any issues early. This evaluation discipline ensures you only scale what truly works and that you maintain trust and transparency as AI moves deeper into your business.

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Step 5: Scale Up and Sustain the Success 

(Run Stage – Expansion & Management)

Finally, take what works and scale it across the organization. In the full “Run” stage, AI moves from a pilot project to an integral part of your operations. This involves both technology and change management. Integrate the successful AI solution into everyday workflows and look for other departments or processes that could benefit from similar automation or insights. McKinsey’s 2025 report notes that leaders must be ready to “rewire their companies” to fully capture AI’s value – in practice, that means redesigning some processes and roles so AI and employees can work together efficiently.

As you roll out AI more broadly, remain adaptable and continuously improve. New data or business changes might require retraining your models or tweaking how people use the AI tool. Experts emphasize staying agile: “AI is evolving fast, and companies must adapt quickly” . Put a structure in place to monitor performance over time, handle model updates, and support users. For instance, many organizations establish an AI center of excellence or designate “AI champions” in each team to sustain momentum. Harvard Business Review highlights that long-term success requires ongoing oversight: “Continuously monitor, retrain, and audit AI systems to ensure they evolve with user needs and new data”.

In scaling, also focus on organizational adoption – build trust and transparency so that employees embrace AI in their daily work (clearly explain how the AI works and address concerns proactively). By the Run stage, you should have a proven use case that’s delivering value; now it’s about multiplying that value across your SME. Successful MSPs can help by templating the solution for reuse, integrating it with IT systems, and measuring the broader ROI.

Over time, scaling AI can transform your operations and even business models, but it will do so gradually and sustainably because you’ve followed a crawl-walk-run progression.

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Final Thoughts

For SMEs and their MSP partners, implementing AI need not be overwhelming. By following these five steps – from brainstorming feasible use cases and training your people, to piloting small and learning, then scaling what works – you can embrace AI in a controlled, value-driven way.

The Crawl-Walk-Run approach ensures that at each stage, the focus is on practical wins and growing confidence. As one MIT Sloan review noted, many companies are opting for incremental “small t” improvements while “building the foundation for larger transformations to come” . In other words, ease into AI, demonstrate clear value, and then ramp up. With leadership support and the right expertise, even a modest-sized business can start running alongside industry giants in the AI arena.

The takeaway for MSPs is to guide clients through this journey: help them start smart and scale fast. In an AI-enabled future, those who begin with thoughtful steps today will be the ones leapfrogging ahead tomorrow.

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