Artificial Intelligence is no longer just for tech giants. From automating repetitive tasks to generating business insights, AI is becoming more accessible—and more powerful—by the day. But before a small business can harness its benefits, it must be AI-ready.
So, what does “AI readiness” actually mean? It’s not about hiring data scientists or deploying robots. It’s about laying a strong foundation so AI tools can plug into your business and start delivering value. Here’s what that foundation looks like:
If your business still relies heavily on on-premise servers, now is the time to change. AI tools (and most modern applications) are built for the cloud.
Why it matters: Cloud-based infrastructure allows for easier integration with AI platforms and offers the scalability, security, and availability AI tools require.
First step: Migrate key applications and file storage to services like Microsoft 365, Google Workspace, or other cloud-first platforms.
Cloud-native business apps are designed to be flexible, update automatically, and integrate easily with other tools—including AI.
Examples: CRMs like HubSpot or Salesforce, cloud-based ERPs, or project management platforms like Trello or Asana.
Bonus: Many of these already have AI features baked in or available as add-ons.
AI is only as smart as the data it consumes. Messy, inconsistent, or incomplete data can lead to poor insights—or worse, bad decisions.
Key priorities:
Centralize your data
Standardize naming conventions
Remove duplicates and outdated records
Tag or categorize data meaningfully (e.g., industry, client type)
The more structured and repeatable your processes are, the easier it is to apply AI.
Start with: Automating email follow-ups, calendar scheduling, invoice reminders, or ticket triage.
Tools to explore: Power Automate, Zapier, Make.com
As you move to the cloud and integrate more tools, governance matters.
Why it matters: AI often touches sensitive information—customer data, internal documents, financials. Make sure you have clear rules around:
Who owns the data
How it’s shared
How long it's retained
What’s considered private or protected
Cultural readiness is just as important as technical readiness.
Create policies: Establish and enforce acceptable use policies.
Encourage experimentation: Let teams try AI tools for brainstorming, summarizing, or coding assistance.
Train your staff: Start with Microsoft Copilot or ChatGPT and show practical use cases.
Create guidelines: Set ethical boundaries and usage expectations early.
AI shouldn’t be adopted just because it’s trendy. It should help solve real business problems.
Ask yourself:
Where are we spending the most time?
What are our most repetitive tasks?
Where do we lack insight or visibility?
What’s stopping us from scaling?
As your business introduces AI-powered tools into workflows, it’s critical to ensure not just access to those tools — but smart usage. The real value of AI comes when your team knows how to ask the right questions, provide useful context, refine results, and interpret the output. Below are key practices and training topics to equip your staff for success.
What is Prompting – and why it matters
Prompting = the instructions you give an AI tool (e.g., a large language model, generative AI) to get the output you need.
A well-crafted prompt leads to useful, accurate, actionable output; a vague or ambiguous one can waste time, generate irrelevant or incorrect results, or mislead staff.
Training your team on prompting raises your “AI maturity” — turning AI from a toy to a tool.
Be specific: Instead of “Write a marketing email,” train users to say “Draft a 250-word B2B email to a manufacturing IT manager in Pennsylvania, introducing our managed IT and cybersecurity services, including a clear call to action and personalization for their pain point of legacy systems.”
Provide context: Supply background such as audience, tone, purpose, constraints (e.g., word count, channel, compliance). The more relevant context, the better the output.
Use examples and templates: Encourage staff to keep a library of good prompt templates relevant to your business — e.g., for help desk ticket summaries, monthly customer-success check-ins, project status updates, or cybersecurity awareness reminders.
Iterate and refine: Teach them to treat the first output as a draft — review, adjust the prompt, ask follow-ups (“Rewrite focusing on urgency,” “Make it one paragraph shorter,” “Add bullet points”).
Check and validate: Remind them that AI output is not infallible. They should review results for accuracy, relevance, tone, and any security or confidentiality risks (especially for internal or customer-facing communications).
Respect boundaries and data governance: Incorporate the policies you’ve already established (see Section 6) by training users not to prompt AI services with sensitive or private data unless explicitly approved, and by understanding when human oversight is required.
Here’s a simple training roadmap to get your organization comfortable with AI prompting:
Kick-off session: Explain what AI tools your company is using, why they matter, and how prompting fits into your business goals.
Hands-on workshop: Let staff try out prompts on real scenarios — e.g., drafting help-desk summaries, generating ideas for content, extracting key points from meeting notes. Review results and discuss how prompt adjustments changed outcomes.
Prompt library build-out: Collaborate with teams (Customer Success, Service Desk, Sales, Marketing) to create a shared repository of prompt templates tailored to your MSP business models (e.g., recurring billing explanation, cybersecurity compliance checklist, customer roadmap summary).
Ongoing coaching & review: Schedule regular check-ins or “AI best-practice” huddles where teams share what worked, what didn’t, refine templates, and highlight successes.
Governance refresher: Incorporate a brief segment into your regular training cadence that revisits data governance rules, acceptable use policies, and risk mitigation in the context of AI.
By training employees on effective prompting, you accelerate the value-creation loop of AI: you’ll see improved productivity, more consistent communication, better customer-facing outputs (e.g., in your customer success and project teams), and fewer misfires or security risks from unchecked AI usage. This step shifts AI from “we can try it” to “we know how to use it.”
Becoming AI-ready doesn’t happen overnight. But every step you take—migrating to the cloud, cleaning your data, automating a process—brings you closer to leveraging AI for growth, efficiency, and innovation.
If you're not sure where to begin, start with a cloud and data audit. From there, you can identify low-effort, high-impact ways to prepare your business for the next evolution in technology.
Reach out today to get your AI readiness assessment started.