Tapping into a Digital Brain: AI-Powered Talent Management at Infosys
Group 1
Vuong Quyen Mai, Jaya Sai Prakash Mandali, Shivani Medarametla, Bharadwaj Nampelly,
Saketh Varma Pakalapati
Emerging Tech Information System
April 27, 2023
Tapping into a Digital Brain: AI-Powered Talent Management at Infosys
Page 1
Background
The case study focuses on the Infosys’s initiative to develop an AI-powered employee
management system. A multinational provider of IT consulting and services, Infosys has
locations across 40 nations. Over 35 years, an enterprise with a $250 market capitalization has
successfully grown into one with a $33 billion market capitalization. The business achieved a
significant milestone in 2017 with $10.5 billion in revenue and over 200,000 employees. The
company's primary activities involve receiving and resolving issues with exceptional IT projects
from clients. Hence, it takes a lot of time and effort to put together the appropriate team of
people for a given project. Moreover, this arrangement would then become more frequent as
ongoing new projects are processed, and ongoing old projects are completed. The firm needs a
more efficient method than the conventional process – the manual assigned procedure – to
manage and allocate an immense amount of human resources. The CEO and co-founder of
Infosys, Nandan Nilekani, issued a challenge to the organization in 2017 to model itself after a
living organism that could sense, react, and adapt to changing needs and circumstances. The first
step in achieving Nilekanni's vision and resolving the issue of efficient human resource
arrangement is the development of an AI-based talent management system. This system is
anticipated to alter management practices and enhance overall company productivity.
Analysis
The Overview of Infosys’s Talent Management System
Offering exceptional solutions in innovation, client satisfaction, and profitability is
Infosys's core competitive advantage. This advantage is demonstrated not only in each project,
but also in the company’s operation process. Thus, the success of the projects and the reputation
of the firm depend heavily on the alignment of the right talents and clients. The Infosys’s talent
management system is up against a great challenge while also demonstrating efficiency over the
traditional way and ensuring the ongoing success of the business.
The talent allocation process at Infosys requires more than a million hours of work
annually when done the old-fashioned way. There are four tasks need to be done: (1) determine
open vacancies for clients’ projects; (2) assess required expertise and experience; (3) find
available and qualified talents; (4) and appoint requirement-meet employee to the projects. Once
the evaluation and selection process has been completed in steps (1), (2), and (3), the task of
Tapping into a Digital Brain: AI-Powered Talent Management at Infosys
Page 2
assigning suitable talents in step (4) is straightforward. The procedure for gathering and
processing data is depicted in Appendix A.
Analyzing fundamental aspects of development progress depends significantly on
determining the users' domain. In fact, area managers are the system's primary users; they send
requests and accept the talent the system recommends. The development team must comprehend
the primary purpose and motivation in order to satisfy the user experience. Despite the fact that
they do not use talents management software, employees are the ones who are most directly
impacted by the decisions and outcomes made by the system. The Talent Task Force (TTF) was
established to guarantee the rights of talents and the correct allocation of personnel. To help
staffing decision-makers be more productive, TTF was assigned to provide proposals. Below is
an analysis of the three main components of the most recent talents' management system:
Determining the Talent Demand. It is challenging for the demand estimation process to
obtain information quickly, completely, and correctly. In facts, the company is a massive
compilation of thousands of small groups. Therefore, the process of gathering data solely
depends on the contributions of various localized, small committees around the world. The
policies and business strategy of each region have significant impacts on common issues. Many
area managers refuse to use the firm’s employee demand system unless their local team was
unable to handle the project. Furthermore, to saving time and satisfy the customer, most projects
are assigned to the employee who is closest to the location rather than the one who is best suited
to complete them.
Determining the Talent Supply. Even when the completion characteristic is not
required, the speed and accuracy of data collection are still crucial to the supply estimation
process. The talent's potential for reassignment and the predicted talent availability in the near
future are two critical pieces of information. The delayed data on employee work status makes it
more difficult to collect this data. In actuality, eligible employee reports are unreliable due to the
inaccuracies in the process of updating information of managers or arising tasks. Then, The TTF
ultimately decides to manually verify each employee's availability for each staffing suggestion.
To clarify, all employees' work status is anticipated to be accessible in a company's central pool
of available talents once a project is completed. However, in order to preserve the talent supply
and guarantee client satisfaction, the area managers assign a talent to a new project as soon as
Tapping into a Digital Brain: AI-Powered Talent Management at Infosys
Page 3
possible. The managers will indeed add a “buffer” code to the talents' profiles if there were no
upcoming projects, preventing other managers from requesting them.
Determining the Talent Skill Competences. Once again, updating data takes on a key
role in the process of evaluating an employee's skill set. Data sources on employee competencies
must be supplemented not only during the hiring process, but also as the project is completed.
The time spent working on a particular project and the skills needed are two essential factors to
consider when estimating a talent's skill proficiency. The challenge with skill evaluation is that
not every talent in a project works well enough and fully understands the entire project. As a
result, some managers encounter the issue of recommended talent's lack of expertise. Moreover,
the TTF is challenged in the era of digital transformation and ongoing technology advancement.
The majority of Infosys’s talents are constantly acquiring new tech and refining their existing
skills, which add another factor to determining talent skill progress.
The New Talent Management Solution's Design: Plans, Goals & Issues
AI technology implementation. The decision to integrate AI into the system should
indeed address the issue of biased data. The output of AI may be biased, even if the company has
no intention of using biased data because of missing data or male-predominated workforce.
Optical Character Recognition (OCR) and Employee Knowledge Graph (EKG) are two machine
learning techniques that are recommended to help with getting detailed information. The system
could use OCR to extract information from data sets based on project description, project rule,
development, strategies, and personnel lists. Besides, the EKG method enables the integration of
a talents’ profile with their owned skills, completed projects, networks, and interactions. The
AI-powered system would change the operation procedure by saving time and accelerating
searching, matching, staffing progress.
Enabling managers to input data. The gathering of demand data is a most challenge for
the current talent management system. For the benefit of the company as a whole, managers who
refuse to update information for either objective or subjective reasons need to be encouraged to
change. To actualize, The new employee management system must demonstrate its benefits in
order to help the managers as well as the business. These advantages of the new management
system must be shown in the new features, capabilities, and productivity boost.
Low quality input data. The primary ground for TTF’s rejecting candidates is a talent's
lack of skill. However, inadequate input data had a significant negative impact on the skill
Tapping into a Digital Brain: AI-Powered Talent Management at Infosys
Page 4
proficiency assessment. The system needs a different method of collecting data to improve the
outcome of the assessment progress. The business strategy involving a variety of data collection
sources has a great deal of potential and viability. As an instance, the managers' and employees'
perspectives are included in the extended data source of talents' proficiency skill. After finishing
a project, each talent would complete a self-assessment survey. Then, the AI system will
compare and synthesize with the managers' input data to create a more accurate talents’ profile.
Issue
To solve the current problem of poor quality data, Infosys must ensure input data
requirements (reliable, accurate, and consistent) while maintaining the ongoing satisfactions of
customer, and the benefits of both managers and customers.
Alternatives
Alternative 1: Large-scale data collection
The first alternative solution involves collecting data on a large scale, which requires
managers and employees to enter a lot of the required data. The most advantageous approach for
the data source and machine learning method is to collect as many data variables as possible.
Indeed, more input variables produce results with higher quality and match acceptance ratings.
After the models have received and processed input data, appropriate ones will be statistically
chosen. All data will be stored, combined with the data from the following project, and used in
the future even though the eliminated data variables were unnecessary for the most recent
project.
The entire grand data collection would significant increase the quality of input data and
serve as the ideal source of information for determining in demand, supply, and proficient skills
of talents. This solution fully addresses the issues of data scarcity and poor quality while also
being quickly implemented on a large scale. In fact, the management model handles all the data
processing while managers and talents provide all the data collection. TTF's remaining task is to
review the data input and record employee feedback regarding the performance of the system.
The large-scale data alternative, however, does not encourage productivity or most
advantages for employees or managers. The amount of input data is excessive, which could
hinder the data gathering. To illustrate, superfluous data requirements will give rise to managers
taking on quite a lot of unessential works. The fact that the managers did not update the
necessary information frequently enough was one issue that contributed to the old talent
Tapping into a Digital Brain: AI-Powered Talent Management at Infosys
Page 5
management system's stagnation. Mass data collection actually discourages managers from
utilizing the new system and overwhelms them with redundant works.
Alternative 2: Conservative input collection
A different approach for Infosys is starting the development of talents allocation system
by variable researching phase. With this strategy, the TTF would have to conduct surveys and
research on significant impact input variables. Based on which variables have considerable
impact on the majority of organizations’project, TTF will select the core input variables. In brief,
the variable research will include doing survey with managers and synthesize for the variable
collection.
This strategy modifies the operational process to focus on values that are more beneficial
to managers and employees. First off, machine learning models are no longer fully capable of
handling the data processing. The TTF department will offer predetermined variables that
eliminate unnecessary input requirements. As a result, managers will dedicate less time and work
looking for and requesting talent for each project. The self-examination would also not feel
excessively drawn out or pointless to talents at the same time.
The shortcoming in this workaround is that it will reduce the variety and precision of the
management system’s outcomes. To elaborate, the selected variables from TTF, which will be
applied to all projects’ requirement, are subjective and do not reflect the goals of each project.
The arrangement of personnel for projects will only be determined by general mass factors, not
by factors unique to each project. Each project variable input reflects the distinct visions,
objectives, and expectations of the client. The projects will not be tailored and personalized as
the firm’s promised goals. As a result, the quality of allocation system and projects’ output will
not be guaranteed. Additionally, the research phase for this solution causes the system initiative
to take longer.
Alternative 3: Mangers’ filtering system
Managers' variable input filtering is another alternative approach to obtaining input for
Infosys. Similar to the first alternative, this one was developed by requesting manager and
employee input for data. The data will then be processed by the system model, and TTF will
review and assess the operation's progress. The key distinction is that managers are allowed to
filter the variable for the input data. Managers, who interact with consumers directly, could,
select influential and relevant variables according to how well they fit a project.
Tapping into a Digital Brain: AI-Powered Talent Management at Infosys
Page 6
The manager selection procedure will allow more features for managers while
guaranteeing that the input data will meet the project's requirements. Managers will be
encouraged to use the system more because they have more options and comparisons during the
updating and hiring processes. Additionally, because no additional research is required in order
to implement the talent management system, it is quicker to develop and initiate. TTF will assist
the project managers in implementing the management system and wait for the model to return a
list of compatible candidates.
The deficiencies of this alternative are the discrete talent profile and the disorganized
data. Undirected data and biased selections are the main issues with giving managers
decision-making authority. To give an instance, the flexibility in managers' variable selection will
eliminate some essential variable input that does not provide enough information for the machine
learning model. The evaluation of talents' proficient skills will be less objective due to the lack of
data. In addition, the talents’ self-assessment is limited by local manager’s filtering. As a result,
the task of matching will be inaccurate because of the low-quality profile and evaluation.
Alternative 4: Combined approach
The discussion of all three above approaches uncovered the potentials and limitations of
each aspect. The Infosys’s core values need to be preserved in order to maintain its reputation
and well-operation. Hence, the final alternative will combine the advantages of the previous
solutions to make the best course of action for the firm. Three key advantages from other
alternatives include: (1) good amount of data variable for the assessment, (2) managers have
more options to use the management system, (3) complete and objective talents’ profile.
Moreover, the matching results will be more accurate due to comprehensive talents’ profile.
The method will have an additional development phase where a different machine
learning model will be trained for the variable with the greatest impact. Thus, Infosys' core value
will be reserved in the criteria that are necessary for finding talent. When managers use the
system to better align with a project's objectives, they can choose to add more variables. The
self-assessment component also offers options for the talents, which increases the amount of data
used to determine skill proficiency.
The longer development period and potential for additional costs are this alternative
solution's drawbacks. The development phase requires additional model training for most
impacted variables. As a result, it requires more time to develop and integrate with the primary
Tapping into a Digital Brain: AI-Powered Talent Management at Infosys
Page 7
model. Different teams might develop the evaluating variable model, which would raise the
budget for progress.
Recommendation
We assessed the above alternatives using a decision matrix in order to effectively select
the option that best aligns Infosys's current circumstance. We utilized critical thinking and
experience to rate the alternatives for each criterion from 0 (no effect or negative effect) to 3
(strongly positive effect). Based on Infosys’s goal on a new talent management system’s design,
there are five considered criteria: high-quality input, managers’ satisfaction, accurate talents’
profile, ease of implementation, projects’ goals guaranteed. Refer to Appendix B, we can clearly
see that alternative 1 and 4 will provide the most quality since alternative 3 has the lowest one.
The rest of the table represent the full decision and results. To summarize, alternative 4 bring the
most benefits while fulfilling all requirements with exceptional level. To emphasize, this
alternative solution does not sacrifice any conditions – from managers, projects' goals, to
employees – to develop and operate. By both using predefined core variable and opening options
for managers, Infosys could get good amount of high quality data for the training model.
Simultaneously, this approach supplies comprehensive and accurate views of talents’ profiles,
which is a solid foundation for the matching process.
Implementation
The above ambitious plan will require feedback and improvement to be carried out
successfully. The development stage demonstrates Infosys' expectations, but the area
implementation stage will reveal the system's efficiency and adaptability. A testing phase and
gathering feedback are required for the implementation in order to have the best scenario
initiative. The development team will receive feedback from the managers and talents in some
locals and alter the talent management system in response to that feedback. Please see Appendix
C for a timeline outlining the steps in the execution process.
Development phase is the first stage of Infosys' implementation plan. Due to the
operational management system, Infosys has the knowledge and information needed to advance
without additional study. The two main tasks of the development phase are to create models for
the variables with the greatest impact, and a new talent management system. Both tasks will be
developed simultaneously, and later the management system and core value models will be
connected. The development phase is anticipated to about three months.
Tapping into a Digital Brain: AI-Powered Talent Management at Infosys
Page 8
A targeted area will receive the demo system to test it out for three months and provide
feedback. The TTF will gather opinions on effectiveness and satisfaction from local talent and
managers. All leaders will gather for a meeting to evaluate the new talent management system.
The final version will be widely distributed to all branches worldwide after revision. To sum up,
Infosys' AI-based talent management system will be utilized in over a year.
Tapping into a Digital Brain: AI-Powered Talent Management at Infosys
Page 9
References
Sawhney, M., Poddar, V., & Kruse, K. (2022). Tapping Into a Digital Brain: AI-Powered Talent
Management at Infosys. Kellogg School of Management eBooks.
https://doi.org/10.4135/9781529622904
Tapping into a Digital Brain: AI-Powered Talent Management at Infosys
Page 10
Appendix A: Process Diagram for Creating and Maintaining the Talent Management
System
(Sawhney et al., 2022, Exhibit 2)
Tapping into a Digital Brain: AI-Powered Talent Management at Infosys
Page 11
Appendix B: Decision Matrix and Criteria
Criteria
Alt. 1
Alt. 2
Alt. 3
Alt. 4
Large-scale
Conservative
Managers’
Combined
data collection
data collection
filtering
approach
High-quality input
3
2
0
3
Managers’ satisfaction
0
2
3
2
Accurate talents’ profile
3
2
1
3
Ease of implementation
2
1
2
1
Projects’ goals guaranteed
2
0
3
3
Total
10
7
9
12
Scale:
● 0: No impact or negative effect
● 1: Positive effect
● 2: High positive effect
● 3: Strongly positive effect
Decision criteria:
● High-quality input: Will this alternative guarantee high-quality input variable?
● Mangers’s satisfaction: As a main user, will this alternative satisfy the managers? Will
this alternative encourage managers to use the system frequently?
● Accurate talents’ profile: Will this alternative provide a complete and objective of talents’
profile? Will talents are evaluated fairly?
● Ease of implementation: Will this alternative requires more research? Will this alternative
implement the system quickly?
● Projects’ goals guaranteed: Will this alternative guarantee match between talents and
projects?
Tapping into a Digital Brain: AI-Powered Talent Management at Infosys
Appendix C: Implementation timeline
Page 12