How to better manage diagnostics, data collection, analysis, methodologies, client decisions, implementation of recommendations and project profitability
The greatest result is created not by slides prepared more quickly, but by a digitalised consultancy methodology that consistently guides the client from problem diagnosis to implemented solution and measurable outcome.
The business area encompasses different types of services, but they are united by problem qualification, diagnostics planning, data collection, interviews, analysis, preparation of recommendations, client decisions and implementation support. Value is created through competence, reliable data, consistent execution, quality control and a clear deliverable to the client.
A poorly defined problem leads to superficial analysis and a solution that is difficult to implement.
The same diagnostic principle must maintain quality whilst taking into account the client's context.
Data provision, interviews, decision-making and initiative implementation depend on the client's team.
The value of recommendations depends on whether they are implemented and whether the agreed business KPIs have changed.
AI reduces the cost of information gathering, summarisation, analysis and presentation preparation, so the value of business consultancy shifts to the quality of problem diagnosis, methodology adaptation, choice of solutions, implementation discipline and the ability to demonstrate measurable results.
The business question to be solved, desired outcome, assumptions, data availability, team and budget are determined.
Methodologies, data sources, interviews, analysis models, work stages and decision points are selected.
Client data, documents, process information, surveys, interviews and external context are obtained.
Hypotheses, calculations, scenarios, root causes of problems and possible solutions are tested.
Alternatives, benefits, risks, priorities, dependencies and implementation plan are presented.
Initiatives, responsibilities, deadlines, decision changes and agreed impact KPIs are managed.
Client data, interviews, analysis and recommendations are managed in separate documents, and methodology depends on the consultant.
Project, survey and analysis tools are used, but their data and client solutions are not connected.
Data collection or diagnostics stages are digitalised, but justification of conclusions and implementation progress are still managed separately.
A single diagnostics scenario is managed from problem qualification through to traceable conclusions, client decisions and initiatives.
Consultancy activity is managed according to methodology usage, project economics, decisions, implementation and business impact data.
AI and analytical models assist in diagnosing and monitoring business impact, whilst the consultant is responsible for assumptions and the final recommendation.
The digitalisation potential of business consultancy is very high, as a large part of the work consists of gathering, structuring and analysing information, preparing documents and coordination.
The most common problem is not a lack of presentation or project management tools. Diagnostic methodologies, client data, interviews, analysis assumptions, recommendations, solutions and implementation actions often remain in separate systems.
It is worth implementing the scenario 'Single diagnostic process from problem to initiatives' first. Only after confirming actual usage, quality control and economic benefit is it worth extending the solution to other services, clients or more advanced AI scenarios.
