Accelerating Enterprise AI from Proof of Concept to Production

AI has moved past asking “Should we do this” to “How can we run it in production.” Developers are building with large language models (LLMs). Executives are asking where the business value shows up.

However, organizations still face challenges in deploying AI solutions securely, scaling them without excessive costs, and operating them with confidence. And this is where we see organizations getting stuck.

A proof of concept is the easy part. However, it’s a completely different challenge to build a global AI platform that protects your data, integrates with your existing tech stack, and can actually be managed by your internal team. Suddenly you're making decisions about infrastructure, governance, compliance, security, model operations, and costs simultaneously. And they're all connected.

The demo works. The pilot works. But the gap between that pilot and real production is where projects quietly stall.

Every organization begins the production process from a different place. Some are still figuring out where AI even fits. Others already know that they need to move inference closer to their users, tighten up how they run models, or defend AI applications against a new class of threats. Plenty are juggling all of it at the same time.

And the technical work is only half of it. The questions that actually keep teams awake at night sound more like these:

• Is our architecture even ready for AI?
• How do we protect these applications against prompt injection and adversarial inputs?
• How do we scale inference without GPU costs spiraling out of control?
• How do we govern AI without smothering the people trying to build with it?

And, maybe the biggest one:

• How do we make sure our own teams can own these systems long after the launch announcement?

None of those issues get solved by picking a better model; they get solved by experience — specifically, of having previously designed, deployed, secured, and operated production AI systems.

Because the gap between AI strategy, a successful pilot, and production is where many organizations need the most help, a trusted partner can make the difference. Akamai AI Professional Services is built to close that gap by helping you move from AI strategy to AI in production.

We took a deliberate approach in building Akamai AI Professional Services. Instead of the sprawling, open-ended consulting engagement that everyone dreads, we built a catalog of fixed-scope services. You get the expertise you need, when you need it, without signing up for a program with no clear finish line.

Four principles shape how it works:

1. You choose only the engagements you need, so you can tackle the priority in front of you without committing to a giant consulting program.
2. Every engagement has a clearly defined scope and outcome, which keeps the risk low and the investment justified while you evaluate, design, deploy, and optimize.
3. The work is built to accelerate time to value, so your AI initiative actually moves forward instead of stalling in analysis.
4. Every engagement is designed to leave you independent. Documentation, knowledge transfer, and enablement are part of the deal, so your team can confidently run what we build together.

That last principle matters more than it might seem. The goal isn't to make you dependent on us; it's to hand you something that your team can own, long after the engagement is complete.

No two AI journeys look the same, but most organizations land in one of three phases depending on their AI maturity, including:

1. Assess
2. Build
3. Enable

The assess phase is about establishing your baseline. You figure out where you actually stand today and what the highest-value next step really is, before you sink any money into it.

We evaluate AI readiness, architecture, governance, workloads, and operational maturity. Engagements in this phase include work such as maturing agentic AI and optimizing for AI bots.

The build phase is where architecture turns into systems that run. Our engineering teams help you deploy AI capabilities, migrate workloads, employ production-grade MLOps practices, secure your applications, and tune inference infrastructure for performance and scale.

This is why our engagement catalog includes: secure your AI, AI on the edge, and MLOps with Akamai.

The enable phase is about your people. All the infrastructure in the world doesn't help if your internal teams can't run it.

So, we offer education and workshops that get both technical and business teams fluent in AI, aligned on the goals, and equipped with the practical skills to build and operate what comes next.

We didn't design these engagements in a vacuum. They map to the questions that customers bring us repeatedly. For example if you’re asking:

• How AI affects your business: We help you secure your applications, govern AI bots, and protect your APIs and data.
• How to run AI at scale: We help you move inference closer to your users, build MLOps practices that hold up in production, optimize your infrastructure, and get your platform ready to grow.
• How AI can improve the way your teams work: We help your developers build secure AI applications while giving your business leaders the foundation they need to make smart calls.
   

Successful AI isn't measured by how fast a chatbot goes live or how many models you've deployed. It's measured by whether those systems become reliable, secure, and sustainable parts of how the business runs, while adding business value.

It takes architecture, security, real operational processes, and teams that can manage AI with confidence over the long haul.

Akamai AI Professional Services pairs deep technical expertise with practical, outcome-focused engagements so you can get past experimentation and build AI that's genuinely ready for production.

Whether you're still assessing your strategy, deploying your first production workload, or scaling across the globe, our experts can help you move forward.

Learn more about Akamai AI Professional Services and find the engagement that fits where you are in your AI journey.