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Forward deployed Engineering webinar

From AI Pilot to Production: The Delivery Model That Actually Ships

Inside Kanerika's Forward Deployed Engineering Practice

Your AI pilot worked. Now comes the harder question: can it work in production?

Only 25% of organizations have moved 40% or more of their AI pilots into production, according to Deloitte’s 2026 State of AI in the Enterprise report. Gartner projects that over 40% of agentic AI projects will be cancelled by the end of 2027, due to escalating costs, unclear business value, or inadequate risk controls.

The problem often isn’t the AI model. It is everything required around it: enterprise data, existing applications, security, governance, evaluation, infrastructure, and production ownership. That gap is driving demand for a different engineering model: Forward Deployed Engineering (FDE).

Forward Deployed Engineers work directly with business and engineering teams to take AI from a promising prototype to a working production system. Instead of handing requirements between teams, FDEs stay close to the problem and own the technical work from scoping and system design through integration, testing, deployment, and adoption.

That is the focus of Kanerika’s FDE practice. In this webinar, Kanerika’s AI experts will explain how the FDE model works, where it fits into enterprise AI delivery, and how organizations can use it to move AI projects past the pilot stage.

Why Forward Deployed Engineers Are in High Demand

Enterprise AI has changed the engineering problem. 

Companies no longer need engineers only to build models or applications. They need people who can connect AI to real business processes and make it work within existing technology environments. 

That requires a mix that traditional delivery models often separate: 

Engineering Depth: Build AI applications, agents, integrations, data pipelines, APIs, evaluation systems, and production infrastructure. 

Production Expertise: Address security, access controls, reliability, monitoring, governance, model evaluation, and operational requirements before deployment. 

Business Context: Work directly with the teams using the system to understand the workflow, define the right outcome, and adjust the solution as real requirements become clear. 

Key Takeaways from the Webinar

  • Why AI Pilots Get Stuck: Identify the technical, data, governance, and ownership gaps that block production.
  • Where FDEs Fit In: Compare the model against consultants, AI specialists, and in-house engineering teams.
  • Whether This Is Consulting With a New Name: Get a direct answer to the obvious objection, including where the skepticism is fair.
  • How FDEs Move AI Into Production: Follow the work through integration, testing, deployment, and production ownership.
  • How FDEs Work Inside Your Environment: See how embedded engineers operate across business, data, AI, cloud, and product teams.

How Kanerika's FDE Practice Increases Your AI Project Success Rate

Faster path from prototype to production. Our FDEs scope the problem builds, integrates, tests, and deploys the solution. 

Production risk addressed early. Security, access controls, governance, evaluation, and monitoring get built into the project from day one,

Built around the actual workflow. FDEs work directly with the teams who’ll use the system. They keep the solution grounded in the business process it’s meant to serve. 

Engineering depth across the full stack. Expertise in AI/ML, data engineering, application development, cloud and DevOps capabilities, because production AI depends on more than the model. 

Outcomes tied to the work. Faster decisions, lower manual effort, stronger operational visibility. Our FDEs stay on the hook until the outcome shows up in production. 

Speakers

Bhupendra

Bhupendra Chopra | Co-Founder and CRO

Bhupendra Chopra is the Co-Founder and Chief Revenue Officer at Kanerika, where he works with enterprise clients to turn AI initiatives into production systems that deliver measurable business outcomes. He has guided large-scale digital transformations across banking, manufacturing, retail, and logistics, and has a direct view into why AI pilots stall before reaching production, and what it actually takes to get them there.

Amit Kumar

Amit Kumar Jena | Head – AI/ML Solutions

Amit leads the AI team at Kanerika, where he develops practical strategies to help organizations implement AI solutions and maximize the value of their data assets. With extensive experience in Python development, Amit specializes in statistical modeling, machine learning, and natural language processing. His technical expertise includes data preparation methodologies, predictive analytics, and advanced regression techniques.

Who Should Attend? ​

  • CIOs, CTOs, and Heads of Digital responsible for enterprise AI strategy and delivery 
  • VPs and Directors of Engineering moving AI initiatives from pilot into production 
  • AI and ML Leaders responsible for GenAI, agentic AI, RAG, and enterprise AI applications 
  • Engineering and Product Leaders dealing with integration, deployment, reliability, or adoption issues after a successful pilot 
  • Business and P&L leaders who funded an AI pilot and are still waiting to see it in production 

Your AI Pilot Worked. What Happens Next?

A successful proof of concept proves that an idea is technically possible. Production requires the AI to work with your data, systems, people, security requirements, and business processes every day. 

Join Kanerika’s experts to see how Forward Deployed Engineering can help close that gap and move enterprise AI projects into production. 

Reserve Your Spot today. 

From AI Pilot to Production: The Delivery Model That Actually Ships
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