{"id":17333,"date":"2022-11-29T22:24:23","date_gmt":"2022-11-30T03:24:23","guid":{"rendered":"https:\/\/d3.harvard.edu\/platform-digit\/?post_type=hck-submission&p=17333"},"modified":"2022-11-29T22:24:23","modified_gmt":"2022-11-30T03:24:23","slug":"who-me-for-that-project-how-ai-is-changing-the-consulting-world","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-digit\/submission\/who-me-for-that-project-how-ai-is-changing-the-consulting-world\/","title":{"rendered":"Who Me? For that Project? How AI is Changing the Consulting World"},"content":{"rendered":"\n

<\/p>\n\n\n\n\n\n

\"\"<\/a><\/figure>\n\n\n\n

How AI is Changing the Game<\/h2>\n\n\n\n

In 2018, I submitted a proposal to the PwC Emerging Tech Competition to leverage Artificial Intelligence for assigning staff members to consulting engagements in order to expedite staffing decisions and better align resources to projects. <\/p>\n\n\n\n

When I joined the firm, I noticed multiple problems with the way in which staffing was traditionally done. Staffing for the most part was a data-blind process and resources were often misused and underutilized.<\/p>\n\n\n\n

Artificial Intelligence (AI) offered a solution because using machine learning allowed the firm to staff team members faster, and also match employees to projects that align with their interests and career goals. The use of machine learning facilitated better matching of individuals with specific skillsets for particular types of engagements. <\/p>\n\n\n\n

Value Capture of Using AI to Assign Staff to Engagements<\/h2>\n\n\n\n

The overall value of this use of AI is that resources are used more effectively, which drives higher revenue because less personnel are idle at any given time. In addition, staff members are more engaged on their projects, contributing to greater work satisfaction and commitment. Not only are staff members happier with their project placement, but the strengths and expertise of staff are better matched with client needs. At the average consulting firm, the turnover rate is 20-25% annually and average tenure is about 2.5-3 years. By increasing staff members’ overall satisfaction with their jobs, companies have the ability to decease churn and drive down costs associated with finding and hiring replacements. <\/p>\n\n\n\n

How It Will Work <\/h2>\n\n\n\n

The idea was to train the staffing model using employee interests, project requirements and TalentLink, which is the program used to manage staff requisition and skills (i.e. it served as a data book with information on all of the employees at the firm).<\/p>\n\n\n\n

\"\"<\/a>
Figure 1.<\/strong> Representation of how Artificial Intelligence will be leveraged for project staffing<\/figcaption><\/figure>\n\n\n\n

Data to train the staffing model is collected through two steps: (1) from general TalentLink and project information and (2) from post-engagement surveys. Staff will be responsible for updating their TalentLink profiles and Engagement Leaders (i.e. Partners) will be responsible for providing the project information and submitting post-engagement surveys. <\/p>\n\n\n\n

\"\"<\/a>
Figure 2. <\/strong>Aggregation of data sources to be used to train the AI model<\/figcaption><\/figure>\n\n\n\n

The model matches upcoming projects with available staff members, prioritizing staff experience, skills and interests when making staffing decisions.<\/p>\n\n\n\n

\"\"<\/a>
Figure 3.<\/strong> Output of the AI model with recommendations for staff that best fit the needs of the project from strongest fit to weakest fit<\/figcaption><\/figure>\n\n\n\n

Artificial Intelligence empowers the firm to utilize its resources to their greatest extent and become a more agile organization. Quicker and better staffing means more chargeable time to the client, resulting in higher potential profit. <\/p>\n\n\n\n

Challenges of Implementing AI for Staffing<\/h2>\n\n\n\n

Challenges for implementing AI for this use include limitations with the accuracy of staff members’ TalentLink profiles and incomplete post-engagement surveys. The model hinges on a certain level of engagement from both staff and engagement leaders. If there is a lack of responsiveness form Partners, the model might need to be updated so that Directors or Managers are responsible for providing the necessary information. In addition, the system will need ample data in order to “learn” and iterate on its recommendations. Therefore, significant lead time is required before it can be fully implemented at the firm. <\/p>\n\n\n\n

Where Are We Today?<\/h2>\n\n\n\n

The proposal won the Emerging Tech Competition at PwC and is now under development by the firm’s product engineering team. The idea is not for this AI tool to replace the HR team that is currently managing the staffing assignments, but rather to improve the approach in which engagement staffing is done. There is currently a pilot being running with some members of the Deals Advisory team and the goal is to finalize the AI by Q2 2023 and then roll it out to the full consulting team by the end of Q4 2023.<\/p>\n","protected":false},"excerpt":{"rendered":"

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