The problem: pressure without a plan
If you lead a small or mid-sized business today, you are probably hearing some version of “we need to do something with AI” from your board, your team, or your peers. The pressure is real, but the path is often vague.
What we see often is one of two reactions: either nothing happens because it feels risky and unclear, or a scattered set of experiments pop up with no connection to real business goals. In both cases, leaders struggle to answer basic questions like: “What are we trying to improve?” and “How will we know if this is working?”
The result is confusion, wasted effort, and a growing sense that AI is a buzzword rather than a business tool. The good news: you do not need to “transform everything” to get value. You need a clear outcome, a few well-chosen workflows, and a simple, repeatable way to test and learn.
Stop saying “AI” – start with outcomes
The word “AI” hides more than it helps. You do not buy “AI” and plug it in. You solve a problem.
Instead of saying “we need an AI strategy,” try framing the conversation this way:
- “We want account managers to walk into every client meeting fully prepared, in half the time.”
- “We want early warning before key clients are at risk of leaving.”
- “We want to close the books faster with fewer manual steps and fewer errors.”
- “We want a single, daily view of what’s going wrong across IT and operations, instead of hunting in five different tools.”
These are outcomes. Once the outcomes are clear, AI and automation become tools to get there, not goals in themselves. We often package repeatable patterns like these into AI powered automation accelerators that focus directly on business results, not technology for its own sake.
A quick recap: what “AI” really means
In the earlier post, we broke “AI” into a few simple ideas:
- Models: systems that make predictions or suggestions based on patterns in data.
- Machine learning: a way for those models to get better over time as they see more examples.
- Generative models: tools that can draft text, summaries, or suggestions in natural language based on what they’ve learned.
- Automation: connecting these models to your actual systems and workflows so they can gather information, take actions, and support your team.
You do not need to remember the labels. What matters is that AI can read, summarize, and reason over large amounts of information, and automation can plug that intelligence into your day-to-day operations.
A simple roadmap for your first AI and automation pilot
Think of your first pilot as a short, focused project with a clear finish line, not a multi-year transformation. Here is a practical roadmap you can use.
Phase 1: Clarify the business outcome
Start with one outcome that matters this quarter or this half-year. Ask:
- Where are we losing time or money because of manual, repetitive work?
- Where are we making decisions late because information is scattered?
- Where are mistakes or slow responses hurting customers or employees?
Turn the answers into one or two plain statements like: “Reduce prep time for key client meetings by 50% while improving quality,” or “Cut invoice processing time from 10 days to 3 days without adding headcount.” This becomes the anchor for every decision you make in the pilot.
Phase 2: Choose 2–3 concrete workflows
Next, pick 2–3 specific workflows that affect that outcome. Each workflow should be well understood, repeated often, and currently painful. For example:
- Meeting intelligence and follow up: For important client or internal meetings, imagine having a brief that pulls recent emails, CRM notes, support tickets, and key documents into one short overview, then automatically summarises what happened and the next steps afterwards.
- Client health and renewal monitoring: For customer success or account management, imagine a simple view that pulls signals from CRM, support, billing, and survey tools to flag at-risk accounts and upcoming renewals before they become urgent.
- Finance and back office: In finance, imagine automating invoice and expense capture, basic checks, and routing for approval, or tracking key contract dates and obligations so your team is not hunting through email and shared folders.
- IT and operations digest: For IT or operations, imagine a daily or weekly digest that pulls incidents, tickets, and alerts from multiple systems into a single, plain-language briefing for leaders.
Treat these as ideas, not blueprints. The point is to select workflows where better preparation, faster insight, or reduced manual effort would clearly move the needle on your chosen outcome. Many of the patterns above are the basis of AI powered automation accelerators we offer, which you can browse as concrete examples here if you want inspiration: https://ansa.solutions/automation-accelerators
Phase 3: Get your data and “plumbing” ready
Before you introduce any new tools, make sure the information you need is accessible and reasonably clean. For a first pilot, you do not need perfect data, but you do need:
- A clear understanding of where the key information lives today (CRM, helpdesk, finance system, shared drives, email, etc.).
- Agreement on which systems will be “sources of truth” for the pilot.
- Basic access and permissions so the pilot can safely read from and write to those systems where appropriate.
In the earlier post, we talked about the “plumbing” that connects AI to your business systems. For a pilot, this is as simple as making sure the systems involved can be connected in a secure, compliant way and that you are comfortable with what the automation is allowed to see and do. This preparation step often determines how fast you can move.
Phase 4: Design the pilot in plain language
Now, outline what the pilot will actually do day to day. Keep it simple and testable. For each chosen workflow, capture:
- Trigger: What starts the process? (e.g., “A meeting is scheduled with a top-50 client,” “A new invoice is received,” “A ticket is created with severity 1.”)
- Inputs: What information does the automation need? (e.g., “Last 90 days of emails and tickets for this client,” “Invoice PDF and supplier details,” “All severity 1 tickets in the last 24 hours.”)
- Actions: What will the system produce or update? (e.g., “Create a one-page meeting brief,” “Draft a summary and action list after the meeting,” “Post a daily digest to a Slack channel,” “Route invoice to the right approver.”)
- Outputs and owners: Who receives the output, and what are they expected to do next?
If you cannot describe the pilot in a few short, non-technical sentences that your team immediately understands, it is probably too big or too vague. Narrow it until the scope feels manageable for a 6–10 week effort.
Phase 5: Implement in small, visible steps
With a clear design, you can begin implementing in phases. A simple pattern that works well:
- Start with “read-only” support. Let the automation fetch and summarise information, but not make changes in your systems. For example, have it produce meeting briefs and post them in a shared channel.
- Move to “suggested actions.” Once people trust the summaries, add recommended next steps—like suggested follow-up emails, tasks to log in the CRM, or invoices to route for approval. Human owners still make the final call.
- Finally, allow limited automation of routine steps. For stable, low-risk actions (like creating a follow-up task or tagging a ticket), let the system perform them automatically within clear boundaries you define.
Throughout this phase, keep the circle of users small but representative. You are aiming for fast feedback, not perfection.
Phase 6: Measure, learn, and decide what to scale
From the start, agree on a handful of simple measures tied to your outcome. For example:
- Time saved per meeting, per invoice, or per incident.
- Reduction in manual steps or handoffs.
- Faster response or resolution times.
- Fewer missed renewals or fewer surprises with at-risk clients.
- User satisfaction (do people actually want to keep using it?).
You do not need an elaborate dashboard; a simple before-and-after comparison over a few weeks is enough to make a decision. At the end of the pilot, ask three questions:
- Did we improve the outcome we cared about enough to matter?
- Do users trust and actually use the new workflow?
- What would it take to roll this out to a larger group or another team?
If the answers are positive, you have a candidate to scale and possibly a pattern you can reuse in other parts of the business. This is exactly how we turn successful pilots into reusable AI powered automation accelerators that other teams can adopt quickly.
Start small, learn fast, build on success
You do not need a sweeping AI strategy document to start creating value. You need a single, meaningful outcome, a few well-chosen workflows, and a structured way to test whether AI and automation actually help your people do better work.
By starting with something concrete like meeting intelligence, client health monitoring, finance automation, or an IT and operations digest, you give your team a clear, visible win that builds confidence. From there, you can decide what to repeat, what to refine, and what to leave behind. Over time, a series of small, successful pilots becomes your AI and automation strategy, grounded in real results, not hype.
If you would like to see concrete examples of how these patterns look in practice, you can explore our AI powered automation accelerators here: https://ansa.solutions/automation-accelerators/