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Course: AI Implementation & Workflows Pathway: Business (Paid) Level: Intermediate Estimated Reading Time: 8 minutes
Before you touch a single AI tool, you need to understand your work. Not in the abstract — the actual, specific sequences of tasks that keep your operation running. This lesson is about building that map, then finding the spots where AI can genuinely help.
Most failed AI implementations share the same root cause: someone bought a tool and went looking for a problem to solve. We're going the other direction. Start with the work. Then see where AI fits.
A workflow is any repeatable sequence of steps that moves something from input to output. It might be:
Every organisation runs on dozens of these. Some are documented. Most aren't. They live in people's heads, in habits, in "that's just how we do it."
Your first job is to make the invisible visible.
Start by listing every repeatable process in your area of responsibility. Don't filter yet — just capture. Think about:
Talk to your team. Ask them: "Walk me through what you actually do when X happens." You'll be surprised how many steps exist that nobody's written down.
A simple table works well for this:
| Workflow | Trigger | Steps (approx.) | People Involved | Frequency |
|---|---|---|---|---|
| Customer complaint handling | Complaint received | 7 | 3 | ~15/week |
| Weekly sales report | Monday morning | 5 | 2 | Weekly |
| Invoice processing | Invoice arrives | 6 | 2 | ~50/month |
Pick your top five workflows by frequency or business impact. For each one, document every step in sequence. Be specific. Not "process the order" but:
This level of detail matters. AI doesn't help with vague processes — it helps with specific tasks within processes.
For every step, ask yourself four questions:
1. Is this step primarily about information processing? Reading, summarising, extracting data, categorising, translating, reformatting. AI is strong here.
2. Is this step repetitive and rule-based? If the same logic applies every time with minor variations, that's a good candidate. If every instance requires novel judgment, less so.
3. How much does this step cost in time? A task that takes 2 minutes but happens 200 times a month is 400 minutes of potential savings. A task that takes 3 hours but happens once a quarter is 12 hours a year. Both matter, but differently.
4. What's the consequence of getting it wrong? This is your risk assessment. Sorting emails into folders? Low risk. Approving medical claims? High risk. The higher the consequence, the more you'll want human oversight even if AI does the heavy lifting.
Mark each step with a simple rating:
Once you've mapped and categorised several workflows, patterns emerge. Common AI-ready tasks tend to cluster into a few categories:
You'll likely find that 20% of your steps account for 80% of the time savings potential. Focus there.
A significant development in 2026 is the rise of agentic automation — AI systems that don't just assist with individual tasks but autonomously orchestrate multi-step workflows. Blue Prism's analysis of 2026 trends highlights how agentic AI marks "the true democratisation of AI where every company can wield intelligence at scale." However, only organisations with proper governance foundations will transform this availability into genuine advantage.
Agentic workflows differ from traditional automation in key ways:
Traditional RPA: Pre-defined rules execute fixed sequences. If the input deviates, it breaks.
AI-assisted steps: Humans remain in control; AI augments specific tasks.
Agentic workflows: AI systems make decisions about which tools to use, when to escalate to humans, and how to handle exceptions — all within guardrails you define upfront.
Examples of agentic workflows:
Governance as a competitive differentiator: The organisations that win with agentic AI aren't those with the most advanced models — they're those with clear governance frameworks defining what agents can do, how they should behave, and who oversees them. This connects directly to the AI governance course: your risk assessments, accountability structures, and transparency requirements become the foundation upon which agentic workflows are safely deployed.
Not every green-rated step should be tackled first. Prioritise based on:
A simple 2x2 matrix of Impact vs. Feasibility gives you a clear starting point. High impact, high feasibility? Start there.
Consider a property management company handling maintenance requests:
Steps 1–3 and 7 are strong AI candidates. An AI system could read incoming maintenance emails, categorise them by trade and urgency, pull up property details, and draft the initial response — all before a human even looks at it. That might save 15 minutes per request across hundreds of requests a year.
Steps 4–6 involve coordination with external people. AI can assist (drafting messages, suggesting available times) but a human should oversee the communication, at least initially.
Mapping too broadly. "Our sales process" is not a workflow — it's a department. Get granular.
Ignoring the messy bits. Real workflows have exceptions, workarounds, and edge cases. Map those too. They're often where AI struggles most, and knowing that upfront saves pain later.
Assuming AI replaces the whole workflow. It rarely does. AI typically handles specific steps within a workflow, not the entire thing. Think assistance, not replacement.
Skipping the people. The person doing the work knows it best. If you map workflows from a management perspective without talking to the people who actually do them, you'll miss critical details.
Map one real workflow from your organisation.
Write it up. You'll use this map in later lessons when we move into implementation.
Question 1: What is the recommended first step before selecting an AI tool for your business?
A) Research the most popular AI tools in your industry B) Map your existing workflows and identify specific steps where AI could help C) Hire an AI consultant to assess your organisation D) Start a pilot project with the cheapest available AI tool
Correct Answer: B — Always start by understanding your actual work processes before evaluating tools. The workflow map tells you what you need; only then can you find the right tool.
Question 2: Which type of task is generally the strongest candidate for AI assistance?
A) Tasks requiring novel creative judgment every time B) Tasks that depend heavily on in-person relationships C) Repetitive, information-heavy tasks with lower risk of error consequences D) Tasks that happen rarely but are extremely important
Correct Answer: C — AI excels at processing information consistently across repetitive tasks. Lower-risk tasks also make better starting points because the cost of errors during implementation is manageable.
Question 3: When mapping workflows, why is it important to document exceptions and workarounds?
A) They make the documentation look more thorough B) AI handles exceptions better than standard processes C) They reveal where AI is likely to struggle, helping you plan for human oversight D) Exceptions should be eliminated before implementing AI
Correct Answer: C — Edge cases and workarounds are often where AI performs least reliably. Knowing about them upfront lets you design appropriate human oversight into your AI-assisted workflow.

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