• AI-enabled category teams to renegotiate SOW's. This results in fewer consultant/testing hours and reducing total spend. These savings are the result of intentional effort, supplier engagement, and commercial negotiations (similar in concept to value engineering initiatives on the Direct side) Example:

Optimizing the cost of the SOW using the enterprise GitHub Copilot licenses

• The original budget for project $1MM (signed SOW)
• SOW renegotiation - cost saving through Copilot usage $0.15MM (15% cost savings). Savings due to reduced time spend on the project as Copilot suggests code completions to developer and integrates with existing code base. 
• GitHub Copilot License fees $0.01MM
• Final Cost to PepsiCo $0.85MM

• Although these AI-enabled efficiencies reduce cost, they achieve this by lowering hours charged and time spend on the project rather than reducing the contracted rate. 

• How can AI-enabled, P&L-impacting savings from reduced hours spend on testing or code writing in Tech services projects be captured and recognized within the GP framework, in a way that honors the category team's intentional efforts. 

• Capture measurable P&L impacting savings through AI-enabled negotiations of SOWs resulting in tangible reductions in consulting, testing, and coding hours that reduce overall cost of the project. 
• Leverage cost-reduction strategies and recognizing lower costs per comparable job or project. 
• Ensure alignment with value engineering and re-engineering principles, validating outcomes against Fieldglass reporting and vendor AI submission documentation.