AI implementation
AI implementation that starts with the work.
A practical consulting approach for organisations moving from AI ideas to useful workflows: define the problem, build a focused prototype and help people use it.
What can an engagement cover?
1. Find a worthwhile workflow
Map a repeated task, its users, information sources and existing process. Agree what improvement means before selecting a model or platform. Useful starting points include finding internal knowledge, preparing routine information and supporting repetitive business tasks.
2. Build a focused prototype
Define the inputs, expected outputs and limits of the solution. Use representative examples to test quality. Decide what the system can do independently and what requires a person’s approval.
3. Plan adoption and ownership
Bring the people doing the work into testing. Document the workflow, train users and assign an owner. A rollout needs a feedback process, a way to handle mistakes and a clear decision about whether to expand.
Where the operating experience comes from
Shivendra’s work includes WorkIntel, a workplace chatbot and selected finance automation with his CFO. These projects inform an approach that connects business needs, implementation decisions and day-to-day use.
Does every workflow need an AI agent?
No. A rule-based automation may be sufficient when the steps and inputs are predictable. A language model may help when the task involves interpreting varied text. An agentic workflow becomes relevant when the system needs to select tools or steps within clear boundaries.
What should you bring to the first conversation?
- One repeated workflow and the team responsible for it.
- The current pain point and the result you want to improve.
- A high-level description of available systems and information.
- Any approval, privacy or access constraints.
Keep confidential company, employee and customer information out of the initial inquiry.
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