In this episode of "Full Tech Ahead," host Amanda Razani interviews Mark Talbot, AVP Customer Success AI Incubation at Appian. They discuss transitioning enterprise AI from isolated experiments into governed production workflows, focusing on recent research conducted in collaboration with Harvard Business Review. 

Talbot reveals a stark contrast in enterprise adoption: while 59% of organizations have AI in production, only 16% realize a high degree of measurable value. He attributes this gap to a failure to embed AI directly into core business workflows, as well as the mistake of applying AI to inefficient, broken legacy processes. 

To scale successfully, Talbot advocates for the creation of AI Centers of Excellence (CoEs) to manage data fabric, fragmentation, and strict compliance (such as SOC 2 and FedRAMP). 

Moving forward, he predicts a shift away from disconnected chatbot tools toward unified, automated platforms that offer full auditability, traceability and concrete business results.

Key Quotes

• "My lens is always where does AI fit into real work in a way that's secure, measurable, and scalable?"
• "Only sixteen percent realize a high degree of measurable value from those investments... because only eighteen percent said AI is primarily integrated into workflows."
• "If you have AI chat and you have ten thousand employees, you have ten thousand different ways of doing things. That's one of the reasons why AI needs to be embedded into existing workflows."
• "Prioritize sustainable implementation and the long term rather than chasing every AI trend."

Takeaways

• Embed AI in Workflows for True ROI: Running isolated AI experiments or simple chat windows doesn't drive top-line business growth. Organizations that embed AI directly into automated, existing workflows report significantly higher value (70% reporting moderate to substantial success) because it systematically removes human toil.
• Empower AI Centers of Excellence (CoEs): Scaling AI requires organizational discipline. Establishing an AI CoE ensures that the company maps performance metrics before and after AI deployment, maintains strict data logging, and keeps the enterprise out of the headlines for data security failures.
• Demand Traceability and Auditability: In complex, regulated environments, governance is non-negotiable. Successful deployments rely on platforms (like Appian) that provide built-in compliance frameworks (SOC 2, ISO, FedRAMP) and offer clear explainability for every decision the AI makes.
• Move Beyond Chatbots and Model Hype: The era of comparing LLMs or relying on generic chat screens is fading. The future belongs to structured platforms where the technology is invisible, secure, and seamlessly integrated into day-to-day operations to deliver scalable efficiency.

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