Hi guys,
I was tasked with creating a creative ai based solution on the client-consulting matching process to reduce benching. I have no experience in consulting but would like to hear all your personal opinions and ideas on what to add to this process, what should be considered and what you would like this whole process to improve on.
Hi there,
I would be happy to share my thoughts on your question:
If you would like a more detailed discussion on your specific situation, please don't hesitate to contact me directly.
Best,
Hagen
Hey there,
I see 4 big 'players' and considerations here:
I'd think about markers to be able to determine interdependencies across these categories when developing the solution.
For example, the state of the economy will determine how many future clients we can have, which will affect number of client projects. This is almost a "top-down" view. Then blend this with current number of consultants (and incoming consultants), with projects they are on now, what % are currently on the beach, the timelines for their projects, their utilisation rates, etc. - and then use the ai model to leverage past data to identify expected pathways and choose the one that minimises benching.
This is one possible way, and there are many others I can think of
All the best
Hey!
I think this solution should focus on optimizing staffing by considering consultant skills, availability, past project performance, and personal preferences while aligning them with client needs, project complexity, and timelines. Key areas for improvement could include better visibility into upcoming projects, predictive analytics to anticipate demand, and dynamic upskilling recommendations to reduce bench time. Consultants would benefit from a more transparent and fair allocation process, while firms could optimize utilization rates and client satisfaction. Think about integrating real-time data, feedback loops, and adaptability to shifting project demands.
Alessa
