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Building an AI Center of Excellence From Risk to Revenue

    Home Case Study Building an AI Center of Excellence From Risk to Revenue
    NextPrevious
    AI for marketing

    Building an AI Center of Excellence From Risk to Revenue

    By ConsultNet Team | Case Study | Comments are Closed | 12 February, 2026 | 0

    A national sales and marketing services company in the CPG industry had teams experimenting with AI tools across the organization, but no one knew which tools were safe to use with client data. Manual processes were causing commission errors and making it hard to keep up with service commitments across 1,000+ manufacturer partnerships. Without governance frameworks or security standards, the company faced a choice: restrict AI usage entirely and fall behind competitors, or build the infrastructure to adopt AI safely at scale. 

    RESULTS AT A GLANCE
      • Faster client servicing through automated agreement reviews and KPI tracking 
      • More accurate commissions by eliminating manual calculation errors 
      • Clear AI policies that let teams innovate without creating compliance risks 
    CHALLENGE 

    The company manages complex, customized agreements with each manufacturer client, where commission rates vary by customer, order type, and shipment volume, and service levels are tied to specific performance metrics. Teams were already using AI tools or wanted to, but there was no way to know if those tools violated existing NDAs or put client data at risk, since legacy vendor agreements weren’t written with AI in mind. 

    Manual work was creating real problems. Commission calculations were done by hand, leading to both underpayments and overpayments, while tracking whether the company was over or under servicing each client required constant manual review. Without a consistent process for evaluating new AI tools, teams were either moving too slowly or taking unnecessary risks. 

    SOLUTION

    We helped the company build an AI Center of Excellence, a dedicated team responsible for setting AI standards, ensuring security, and enabling safe experimentation. 

      • Hired roles including an Automation Engineer, GenAI Engineer, and AI Security Architect (reporting to the CISO) 
      • Reviewed every existing vendor agreement and NDA to identify compliant AI usage 
      • Built security policies that protect client data while enabling innovation 
      • Launched proof of concepts using Microsoft Azure and Power Automate tied to 2026 business priorities 
    Delivering Lasting IMPACT

    The COE gave the company a clear path forward, allowing teams to confidently use AI tools while knowing which ones are compliant and how to protect client data. Automation reduced time spent reviewing agreements and monitoring service levels, while commission calculations became more accurate, eliminating costly errors. 

    Most importantly, the company now has a repeatable process for adopting AI across the enterprise while balancing speed with security. 

    Business Insights, Digital Transformation, IT, Workforce Solutions, Artificial Intelligence (AI)

    ConsultNet Team

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