In this episode of The CTO Show with Mehmet, Mehmet sits down with Omid Pakseresht, CEO of Goodfolio. Omid works on enterprise AI systems that move beyond pilots and into real business workflows.

The conversation reframes enterprise AI failure as a systems problem, not a model problem. Omid argues that most AI initiatives break because the workflow, ownership model, governance layer, audit trail, and adoption path were never designed properly. The model may work, but the enterprise system around it often does not.

If you are building, investing in, or leading enterprise AI adoption, this conversation gives you a clearer way to judge whether an AI initiative is ready for production or stuck as another pilot.

About the Guest

Omid Pakseresht is the CEO of Goodfolio, a company focused on helping enterprises build and scale AI systems inside real workflows.

His background is in product and technology, with a particular focus on finance. He has spent around 10 years building and scaling AI solutions in enterprise environments.

Omid is well placed to frame this signal because his work sits at the point where AI models meet workflow design, governance, compliance, and business outcomes.

LinkedIn: https://www.linkedin.com/in/omidpakseresht/

Website: https://goodfolio.com

Key Takeaways

Most enterprise AI fails because the system around the model was never built.

A working AI pilot is not proof that the business is ready for production.

AI adoption fails when it is treated as a data science project.

Workflow owners must be part of the AI design process from the beginning.

Human-in-the-loop fails when humans become late-stage QA gates.

AI can create new bottlenecks when upstream productivity increases faster than downstream capacity.

Regulated AI needs audit trails, governance layers, risk monitoring, and clear decision rights.

AI ROI must be tied to business outcomes, not seat counts or software usage.

What You Will Learn

The difference between an AI tool and an AI system inside an enterprise workflow.

How AI pilots fail after the proof of concept looks successful.

Why model quality is rarely the biggest barrier to enterprise AI adoption.

How compliance, governance, and auditability shape production AI.

What changes when AI becomes embedded into regulated workflows.

Why AI can move bottlenecks rather than remove them.

How leaders should evaluate AI ROI through outcomes instead of software spend.

Episode Highlights

00:00 — Enterprise AI failure starts beyond the model

02:00 — Proofs of concept became the easy part

04:00 — Workflow fit beats model quality in adoption

05:30 — AI cannot remain a data science project

08:30 — Production AI needs more than a model

12:00 — Compliance workflows expose AI bottlenecks

17:30 — Human-in-the-loop needs a better framing

20:00 — Governance becomes table stakes for enterprise AI

24:00 — AI ROI must connect to business outcomes

28:00 — AI exposes process gaps before scaling

Resources Mentioned

Goodfolio: https://goodfolio.com

Inspector: Goodfolio tool for compliance review of marketing assets in regulated industries

AI agents: discussed in the context of compliance workflows

Model governance: discussed as a production requirement

Evaluation pipelines: discussed as part of production AI systems

Prompt engineering versioning: discussed as part of AI system management

Risk monitoring: discussed as part of regulated AI adoption

Data lakes: discussed as a comparison point for large enterprise technology projects

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Available on all major podcast platforms and YouTube.

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