Practical AI in business processes · Alexander Koltsov
As an AI automation consultant, I help you apply AI to document processing, reporting, CRM and business analysis. I define the use case, connect the relevant information and build a workflow in which people can review and control the result.
Discuss your project ↗How I can help
We agree the priorities and deliverables around your business, current systems and team.
AI can be useful when work involves interpreting documents, summarising discussions or preparing a proposed response from existing information. I assess whether the task is frequent, reviewable and supported by suitable data. Predictable calculations and fixed business rules can remain in conventional software.
I review the process, source material, permissions and the quality of information available. We identify what an acceptable result looks like and how a person currently checks it. The assessment also makes dependencies visible, such as inconsistent documents or missing access controls.
I compare candidate use cases by expected benefit, data readiness, integration effort and the consequences of an incorrect output. We choose a narrow starting point with clear acceptance criteria. A successful pilot should show useful work on representative examples, not only an impressive demonstration.
I test whether the proposed task can be performed reliably enough for its intended use. We examine ambiguous inputs, missing information and the work needed to review errors. You receive a view of feasibility, limits and the controls required before wider use.
I define how information enters the workflow, what AI produces and which checks happen before anyone acts on it. Structured outputs and clear exception routes make the result easier to review. Access and approval rules are designed into the workflow rather than added after launch.
I design workflows for classifying documents, extracting information and preparing records for review. The output stays connected to the source so users can check a value or interpretation. Missing fields, conflicting information and uncertain results are routed for attention.
I connect reporting assistance to approved business data and agreed definitions. AI can help explain changes or prepare a narrative, while calculations and source values remain traceable. Review focuses on whether the explanation is supported and whether important context has been omitted.
I design CRM workflows that use lead history to prepare summaries, proposed next steps and follow-up drafts. Your team retains control over customer-facing decisions. This can reduce repeated review without assuming that an AI-generated message is ready to send.
I identify opportunities around ERP documents, management information and user assistance. The ERP remains responsible for business records and permissions. We define which information AI may retrieve and which proposed changes require validation and approval before entering the system.
I give an AI agent a bounded task and a limited set of permitted tools. The workflow records what it accessed and what it proposed or changed. Actions with material business consequences need explicit approval and a recovery path when something goes wrong.
I define review responsibilities, test examples, monitoring and the conditions for pausing the workflow. We examine actual outputs and exceptions as use expands. Changes to prompts, models or connected data are reviewed against the same acceptance criteria used for the original release.
I structure delivery as assessment, prototype, evaluation, controlled pilot and supported rollout. Each stage has a decision about whether the evidence justifies continuing. You receive the workflow, documentation, review rules and ownership needed to use it in daily operations.
Relevant experience
These projects show the processes and responsibilities behind the service. Open a case to review the full scope and result.
AI
Management reporting often requires finance and operational teams to gather data from ERP, accounting, CRM, inventory, databases, spreadsheets, and external sources before they can explain business performance.
Read the case study ↗AI
Complex ERP and transformation projects generated long meeting transcripts, changing requirements, conflicting stakeholder expectations, and large volumes of supporting documentation.
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A CRM process contained more than 200 existing leads requiring structured review and follow-up.
Read the case study ↗Before we start
It can be given narrowly defined permissions, but the appropriate level of autonomy depends on the action and its consequences. I start by separating retrieval, proposed changes and approved execution.
We use representative examples with expected results and review difficult cases as well as normal ones. Measures can include accepted outputs, correction effort, omissions and the time saved after review.
We define permitted sources, access roles and the information needed for the task before implementation. Tool and provider choices are evaluated against your data-handling requirements.
Define the next step
Describe your situation, the systems you use and the result you need. I will help define a useful starting point.
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