AI & Data Science CoE helps a major CPG firm save 50% talent costs and accelerate GTM for AI applications by 60%


The client is a leading manufacturer and supplier of personal care products in the USA with operations across Latin America, Europe, and APAC.


  • The client  wanted to expand  its AI & Data Science team to accommodate the new digital transformation initiatives and increasing business requirements. 
  • The Analytics team faced challenges with respect to talent acquisition, cost, and timelines. 
  • The client was looking for a partner who can create an excellent data science team offshore that would solve diverse problems ranging from the supply chain, marketing, pricing, digital analytics, and customer analytics. The CPG firm  wanted to partner with an offshore data science team to solve diverse problems ranging from the supply chain, marketing, pricing, digital and customer analytics.
  • The other expectations from the partner included providing strategic vision & direction for AI initiatives, imparting best practices, governance structure, and program management. 


Tenzai conducted a Purpose-Driven workshop to understand the business objectives and vision for AI & Data Science. Based on the insights from the workshop, the roadmap and execution plan for creating a data science team for the CPG firm was defined. 

 In the next step the ideal team composition, skillsets, and technology stack were finalized.  In the initial phase, the client was looking to set up a team of 5 data scientists. Tenzai leveraged existing teams, databases, universities and AI finishing schools to accelerate the recruitment process. 

Introduced a seed team for capabilities such as supply chain, marketing and customer analytics to accelerate GTM timelines for the program.  The program benefitted from the best practices, frameworks and domain expertise of these data scientists. 

In the next step the processes ranging from delivery management, analytics governance, program management, technology infrastructure, KPIs, etc. were set up. 

The data science program was kickstarted within a month with a seed team of 2 data scientists and the entire team of 5 data scientists was ramped up within 2 months. 

The data science team started interacting with the client’s Analytics and business teams. Both the teams collaboratively created a charter of deliverables for each quarter. 

The data science team’s responsibilities included developing machine learning models, developing AI prototypes, data visualization and identifying new use cases. Another important responsibility included rapid insights generation (RIG), where data scientists conducted an in-depth exploratory analysis to help business users arrive at important business decisions backed by insights. 

The data science team also contributed to enhancing analytics adoption within the organization by conceptualizing new intuitive analytics applications which had a low learning curve for business users.


During the first 12 months, the data science team built over 6 machine learning models, over 10 dashboards, 3 prototypes and conducted numerous exploratory analyses across functions. The client is looking forward to enhancing the scope and scale of the engagement.

Other benefits include:

  • Helped reduce AI & Data Science Opex by 50% 
  • Created an economic impact of more than $25M for the client
  • Accelerated GTM for AI & Analytics applications by 60%