In this episode of "Full Tech Ahead," host Amanda Razani interviews Sarah Edwards, CPSO at Kantata. They discuss the strategic implementation of AI in the services industry (consulting firms, agencies, and B2B IT service teams). 

Edwards argues that measuring AI success solely through the lens of traditional productivity and speed is a massive mistake for services firms, as charging by hours while simply doing tasks faster inevitably leads to a financial "race to the bottom." 

Instead, she advocates for shifting toward an AI-native operating model that powers the "expertise economy." 

Key Quotes

• "Measuring productivity [is] the wrong way to measure AI success... if I'm just delivering things faster and faster, traditionally billing my time based on hours or days, well, how am I growing my revenue? That just becomes a race to the bottom."
• "Traditionally, expertise has been reliant on tribal knowledge... AI is disrupting all of that. For the first time, we can really compound that expertise across your business."
• "AI for me is not just about getting faster. It's how do I get better? Because unless I get better... I'm not going to win."
• "In services, quality has been something we've always struggled to measure... Now with the help of AI, we can truly start to gather and measure [sentiment] during project delivery."

Takeaways

• Ditch the Speed Metric for Quality: In professional services, utilizing AI to execute work faster shrinks billable hours without adding value. Firms must stop treating AI as a siloed efficiency tool and start measuring leading indicators of revenue growth, margin improvement, and transformed service delivery.
• Capitalize on Compounded Expertise: Historically, consulting firms were constrained by "heroics" and individual expertise, which created an operational ceiling. An AI-native model unlocks years of hidden project context and conversation logs, instantly upskilling every consultant to the level of the firm's best performer.
• Automate the Sales-to-Delivery Handover: One of the largest operational friction points is when sales teams "throw a project over the fence" to the delivery team. AI agents can eliminate this silo by parsing entire sales-cycle call data into comprehensive briefs, ensuring scope, stakeholder concerns, and project requirements are perfectly aligned.
• Transition to Outcome-Based Metrics: Instead of tracking static, trailing metrics like "on time" and "on budget" at the end of a lifecycle, firms can use AI to track real-time qualitative health indicators, such as client sentiment, delivery team mood, and continuous project drift, while the work is actively in flight.

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