From creating initiatives to analysing an entire transformation portfolio
BICon Portfolio Steering already provides extensive reporting capabilities – from interactive dashboards and flexible reports to executive summaries, resource planning and strategic portfolio analysis.
But even the most flexible reporting system has a limitation: Someone needs to anticipate which analyses users might want.
With the latest extension of our MCP Server, that limitation is becoming much less relevant.
From data entry to data intelligence
Until now, the MCP Server was primarily used to create new initiatives through AI assistants such as Claude and ChatGPT.
Now, the entire portfolio dataset can also be accessed and analysed through the same interface.
This opens up a fundamentally different way of working with portfolio information.
Instead of navigating dashboards, configuring reports or exporting data into spreadsheets, users can simply describe what they want to understand.
AI can explore the available data, combine information across different dimensions, perform calculations and generate tailored visualisations.
A practical example: What happens after go-live?
Transformation initiatives do not stop consuming resources when their implementation projects finish.
Once applications and AI solutions enter production, they generate recurring operating costs and require specialised personnel.
These future commitments are often underestimated when organisations prioritise their transformation portfolios.
Using the MCP Server, I analysed the projected production costs and staffing requirements of the initiatives in our demonstration portfolio.
The results:
- €1.19 million in annualised operating costs by December 2027.
- Approximately €600,000 in cumulative operating costs during 2027.
- 4.8 full-time equivalents required to operate the solutions by December 2027.
- A particularly sharp increase between August and November 2027, as several initiatives enter production.
The analysis also breaks down staffing requirements by skill – including ML Engineering, Data Science, AI Governance, AI Product Management and Domain Expertise.
The result is more than a financial forecast. It provides a foundation for future workforce planning, budgeting and strategic decisions about the portfolio.
The bigger picture
This example illustrates a broader change in enterprise software.
Traditional reporting answers questions that were anticipated when the software was designed.
AI-enabled analysis allows users to ask questions that nobody anticipated.
The underlying portfolio data remains structured and centrally managed within BICon Portfolio Steering. The MCP Server makes that information accessible for flexible analysis through compatible AI assistants.
And the questions can go far beyond operating costs:
- Which initiatives create the greatest future resource bottlenecks?
- How do strategic benefits compare with total lifecycle costs?
- Where are risks, dependencies and capability impacts concentrated?
- What changes if selected initiatives are postponed or cancelled?
- Which combinations of initiatives deliver the greatest strategic value within available capacity?
The objective is not to replace structured reporting or established governance processes. It is to complement them with an almost unlimited analytical layer.
The next generation of enterprise software may be defined less by the reports it provides – and more by the questions it allows users to ask.
Explore BICon Portfolio Steering: www.bicon.digital