66Degrees CEO On How Google AI Sales Grew 325 Percent

Top Google partner 66degrees takes a deep dive with CRN around how the solution provider’s AI sales spiked 325 percent in 2024. ‘If you and I are in a brainstorming session, we’re going to come up with 10 good ideas. But if you can have a 5,000-person company come up with ideas, your use cases are just going to flow out,’ says CEO Ben Kessler.

One of the nation’s top Google AI consulting and services partners, 66degrees, is witnessing a massive 325 precent AI sales spike this year as it pivots customers from AI proofs of concept into production.

“Market trend No. 1 is we’ve moved from POC and trying the art of the possible into picking a few lanes in revenue generation or cost reduction where customers are applying AI to test that,” said Ben Kessler, CEO of Chicago-based 66degrees.

Kessler said Google’s market position and strategy for enterprise search and agentic AI, or AI agents, “have a ton of legs.”

“Those are two areas where people can look and touch and feel AI—who are not familiar with AI—but can think of the AI use case in their business,” said Kessler. “If you can get people to adopt AI in your business, the use cases will come and the use cases will spread.”

“For our clients that are very advanced from an AI standpoint, they’ve given AI into the fingertips of their employees. They said, ‘We’re going to give you these tools. We’re going to give you Gemini for Workspace or Copilot. We’re going to give you a variety of different things so that you can start experimenting,’ said the 66degrees CEO.

“Because if you and I are in a brainstorming session, we’re going to come up with 10 good ideas. But if you can have a 5,000-person company come up with ideas, your use cases are just going to flow out,” he said. “So the really forward-leaning CIOs have said, ‘Let’s get AI into as many people’s hands as we can and familiarize and train with it. The adoption will go up, but also the ideas in our organization will go up.’”

66degrees And Google Cloud

66degrees is a leading AI consulting and data services solution provider and one of Google Cloud’s top AI channel partners, specializing in developing AI-focused, data-led solutions alongside Google technology.

In fact, the company won Google Cloud’s 2024 Expansion Partner of the Year Award for North America.

In an interview with CRN, Kessler takes a deep dive into why AI sales are skyrocketing in 2024, Google BigQuery momentum in migrating customers’ data estate to the cloud, Google’s AI agent charge and 66degrees’ winning AI strategy.

How much are AI sales growing for 66degrees?

From the end of 2022 to the end of 2023, our Google AI growth was around 600 percent. We couldn’t repeat that again. But if you look at the end of 2023 to the end of 2024, our AI practice, our AI revenue, is growing between 320 [percent] and 325 percent. This is measured by billable revenue for our AI/ML consultants.

It’s an exciting trend to see in the market as more companies move from POC to production use cases for their enterprise AI.

How did Google AI sales grow more than 3X in 2024?

We’ve got about 50 data scientists at 66degrees. It’s the fastest-growing area.

Our strategy breaks down into the business centers of where customers can benefit from AI. There are two strands. Strand No. 1 is: How can you have AI and GenAI, from an agent standpoint, interact with helping drive revenue in your business?

I’ll give you a couple of case studies. From prospecting to helping identify high probability, all the way to helping keep your current and active customers engaged and buying more, so everything in the revenue center is what we’re thinking about in one pillar.

Then the second pillar is how you can actually optimize your operations. That would come down to a variety of different things: Some are agents, some are just more traditional data science models.

That could be, ‘How do you get better at forecasting? How do you get better at pricing? How do you actually build out your supply chain and logistics arms with automating with data science models. How do you expand your employee productivity?’ And that is where AI agents would actually come into play. So, ‘How do you do this across your finance arm? Your HR arm?’ It’s relevant to every single business.

Google’s strategy plays to each of these things. Because what does a CEO or CIO care about? If you can propose a solution that increases an end client’s revenue or reduces cost or improves productivity—you’re going to catch a board’s ear as well as a CIO’s ear.

What are the biggest market trends driving sales for 66degrees? What’s working for you?

Our sales trends are three-fold. Enterprises moved last year from the POC, from an AI standpoint, into now actually moving into production—especially if there can be a business value attached to it.

We’re having a lot of our clients doing an ROI test on their AI. Market trend No. 1 is, we’ve moved from POC and trying the art of the possible into picking a few lanes in revenue generation or cost reduction where customers are applying AI to test that.

No. 2 is on the data side because you need good data to have good AI. I’m a consultant by background and started off my career in business consulting and finance. You can’t have bad data.

Where 66degrees has had lots of tailwind at this point is deploying ROI-driving AI. But our stumbling block is if a company doesn’t have the data ready, if their data isn’t modernized and their data isn’t there—the model will only go so far.

Google and other models right now are quite good. It comes down to the AI pull and then the data pull, companies actually having their data in a good place, but then being able to have plenty of data to basically train their final outset.

No. 3: We’ve seen some resurgence from just some cloud migration and actually doing some lift and shift and some application modernization. Some of that pull has been AI and data, but I also think that the interest rate environment and spending has loosened up a little bit.

People are saying, ‘Hey, if there can be an ROI on this, moving this or modernizing this application, I’m absolutely going to do that.’

In terms of getting customers’ data estate in order so AI can do its magic, what’s working for 66degrees on the Google side?

So the data products that Google is offering, first and foremost with BigQuery, are very good products. Best-of-breed products.

I look at AI as a three-legged stool. Is your data platform in order? Is your data state in order? That can be both from a data warehouse and a data lake standpoint. We’ve been very satisfied with the BigQuery product, especially how it integrates and centralizes people’s data, but that it integrates with other Google products or other large language models that are needed.

So No. 1, BigQuery is very good.

No. 2 is the large language model.

Then No. 3 is, from a RAG [retrieval-augmented generation] standpoint, the final mile, that’s where a partner comes in and not only moves and modernizes your data but sets up a RAG framework so you can frame the last mile model on your data. And you can have your application or your analytics tuned.

The other way Google is helping from a product standpoint is they’re helping fund us from a migration standpoint. There’s funding, especially if there is spend downstream from that. Google’s continued to be a good partner in that. Some of their targeted campaigns that they’re going to market with partners have been very successful.

Is there one particular campaign from Google that’s helping you?

Customer Experience Reimagined is one we’re focused on right now. There’s a dozen use cases.

What Customer Experience Reimagined means is using and leveraging the CCAI [Contact Center as-a-service AI] platform to develop and put agents into the market.

We’ve built AI agents for large theme parks that are sitting on Google, which is enhancing the guest experience. For travel and tourism, we’ve built the customer experience that before you get to the resort or hotel, you’re engaging with a Google AI agent that is extending your experience.

So we’re able to push our solutions into the market with the back end of an agent that’s built initially with Google technology but uses [66degrees] as the final mile for us to help execute and train the Google model on that client’s use case. It’s actually quite effective.

The three-legged stool is BigQuery, Gemini or a Google model as the large language model. But then, the final mile,is really important for us. Because oftentimes, when you pull a large language model out of the box, it’s not super helpful. You have to train the model on your data and your intended use case.

One of Google’s biggest pushes in AI right now is AI agents. You said customers are buying into agentic AI or AI agents because it either drives revenue or saves them money. First, can you talk about how AI agents are creating new revenue for customers?

We talked about the agents at these various different travel and tourism use cases. We’ve also done this in financial services, manufacturing and in the energy industries, where agents are helping customer service representatives or helping the customer do one of a variety of things.

In manufacturing, logistics and energy, the agent is helping the customer service rep remediate an issue. For example, the power goes out or they can’t find a replacement part. It’s helping the customer service agent be faster and quicker and more responsive.

In customer-facing, this is true in financial services. It’s the agent interacting directly with the customer. So the customer is having a conversation with the AI agent.

If you can actually fine-tune the agent to understand—and we’re working with even large telecommunications customers on this—if you can train that agent on the customer’s data, it can actually have a more fine-tuned and better customer experience than if you or I were actually serving that customer.

If you think about resorts, or if you’re on a cellphone and you’re interacting with this agent, it actually is much more fine-tuned in the tone and the data.

We can know your data about what you did last vacation and about what experiences you liked. This is true in telecom companies. This is true in banks. This is true on large ships. This is all about using Google technologies to help improve the revenue for each of these customers. This is why we’re growing at 325 percent this year.

Where are customers buying AI agents in terms of cost savings?

On the cost side, I’ll give a couple of different examples.

A finance and procurement office will always need help around forecasting business demand. If you think about the job of a financial analyst, which was my job when I first started off, it’s processing data. It’s processing information and lots of structured and unstructured data.

We have helped a number of different clients actually build data science models to process and help that data. It’s not replacing the analyst. It’s just allowing the analyst to [have] better insight. And that could be on pricing, and that could be on demand forecasting, etc.

In many ways, the agent in this case—based upon that data science model—is making recommendations to the human agent about how the outlook will come across. Imagine if you were able to develop a forecast over the next six or 12 months that wasn’t just based upon your financial analyst but was able to take a data science model and completely process it.

It’s actually accelerating and improving the accuracy of that demand. Or it’s improving the accuracy of a raw material input into this where you can say, ‘I can be more fine-tuned.’ Essentially, what [AI agents] do is, it’s not taking the human decision out of this—it’s improving human decisions with the correct data.

So if you think about what data science is doing and what agents on the face of this are doing, it’s improving the life of the financial analyst so that they can make better, more well-informed decisions.

What’s another example around AI cost savings?

We have a number of clients asking us to put an enterprise search capability on their HR and their finance and their information [departments].

This is a little bit more enterprise AI than this is agentic AI. But what AI is allowing them to do is smart enterprise search to completely search through and have information faster. We’re seeing some clients actually using agents to suggest or make suggestions where there can be a conversation with this enterprise search.

So those are the couple of most popular use cases that we’re seeing: ‘How do you make the financial analyst’s job easier across a number of different metrics? And then No. 2 is, how do you actually remove some of the interface of the financial analyst or the HR person or the legal person, so that you can actually do some disintermediating and get to the information faster?

At the end of the day, what you’re doing is you’re democratizing data.