Hackett Group and Celonis partner to bridge the gap between Process Intelligence and AI transformation

Hackett Group and Celonis have announced a partnership that combines process intelligence with enterprise benchmarking and Artificial Intelligence (AI) development platforms, targeting one of the most persistent problems in enterprise AI: the gap between identifying opportunities and actually delivering measurable business outcomes.

The collaboration brings together Celonis's Process Intelligence Graph - which creates digital twins of business operations - with Hackett's AI XPLR platform for identifying AI use cases and its ZBrain technology for building AI solutions. Key to the partnership though is Hackett's extensive benchmarking database, which includes performance data from 97% of the Dow Jones Industrials and 90% of the Fortune 100.

For Ted Fernandez, Chairman and CEO of Hackett Group, the partnership tackles a fundamental problem with how companies are implementing AI today. He explains:

We're helping companies move from intention to action and impact. Celonis Process Intelligence lets companies understand how their business runs and how to make it run better. On that basis, AI XPLR identifies, designs and evaluates potential solutions, and ZBrain delivers the agentic workflows to drive the change.

AI needs to understand your business

The partnership reflects something we're seeing more of: the recognition that enterprise AI needs business context to actually work. This has become central to Celonis's pitch as the company has evolved from analyzing individual processes to creating comprehensive digital twins through its Process Intelligence Graph.

The context problem was amusingly illustrated by Celonis co-CEO Alex Rinke at the company's user event last year. He asked ChatGPT about "excess stock levels and safety stock levels in HR" - a question that makes no sense since you don't manage inventory in human resources. But ChatGPT produced an elaborate response filled with HR buzzwords anyway. It's a clear example of how AI tools can sound authoritative while being completely unhelpful when they lack business context.

The Hackett-Celonis partnership aims to fix this by combining deep process intelligence with transformation expertise and AI development capabilities- the companies believe that they can help organizations with that challenge of scaling AI from pilot projects to enterprise-wide transformation. Fernandez explains:

Our capability is really distinct. This ability to use this market-leading operating workflow process intelligence information, which Celonis clearly has, into a client situation in a highly sophisticated environment - to take that and immediately bring all of the Hackett best practice knowledge, benchmarking and performance information, along with the ability to propose a fully designed AI solution.

Process intelligence + benchmarks + AI

What makes this partnership interesting is how it combines three things that enterprises typically struggle to bring together: understanding how processes actually work, knowing what performance is possible, and having the AI capabilities to bridge the gap.

Think about a company processing invoices in 30 days. Most organizations know this feels slow, but they lack the specific intelligence to understand why it's happening and what's realistically achievable. The Celonis-Hackett combination addresses both sides of this problem.

Celonis's Process Intelligence Graph reveals what's actually happening operationally - maybe invoices are getting stuck in approval workflows, or there are delays in matching purchase orders with receipts. But process intelligence alone doesn't tell you whether 30 days is actually a problem worth solving, or what improvement you can realistically expect.

This is where Hackett's benchmarking data becomes important. When asked about invoice processing scenarios, Fernandez explains:

The answer is yes. In fact, in the client that I'm involved with, which has both capabilities, that is one of the areas we're looking at. We get a chance to provide more credibility to the opportunity because they know us for doing that.

The benchmarks don't just provide industry averages - they show what top performers are actually achieving. If the benchmark suggests 10 days is achievable for similar companies, organizations suddenly have both a clear problem (process intelligence shows where bottlenecks occur) and a concrete target (benchmarks show what good performance looks like). Fernandez says:

We also not only speak to the opportunity numerically - we're transformation experts, so we have a chance to tell the client, 'Here's exactly what that would look like with or without AI.'

This combination aims to provide much needed clarity for transformation initiatives. Traditional consulting approaches often give recommendations without the detailed Process Intelligence to understand root causes, while process mining tools can identify bottlenecks without providing context about whether the performance gaps matter or what improvement targets make sense.

The AI layer then becomes the way to close the performance gap. Fernandez notes:

AI brings the ability to build functionality to bring in unstructured data sources or public data sources, more readily leverage the power of a Large Language Model (LLM) that sits underneath whatever model you're building. It also gives a client a chance to deploy cognitive functionality, which is self-learning and can continue to improve.

This is different from what you typically get from enterprise software platforms:

You've got to remember enterprise platforms today provide rule-based, static functionality.

The power of this approach is that it gives organizations specific transformation scenarios backed by actual data. Instead of theoretical AI possibilities, buyers get specific insights into their current performance, evidence-based targets for improvement, and AI solutions designed to bridge the gap. Fernandez explains:

Our capability is really distinct. This ability to use this market-leading operating workflow process intelligence information, which Celonis clearly has, into a client situation in a highly sophisticated environment...the combination lets organizations move beyond generic best practices to understand exactly how their processes differ from top performers and what specific changes would deliver the greatest impact.

Beyond what software vendors are offering

One of the key differentiators that Fernandez emphasizes is the partnership's ability to frame AI opportunities more broadly than what enterprise software vendors are currently bringing to market. While traditional enterprise platforms are adding AI features to their platforms, Fernandez argues these remain tactical implementations that don't address the full potential of generative AI:

For example, you can take a customer service solution and we can go in there and tell them, 'This is exactly what this platform is getting you'. And by the way, we'll tell them 'here's what this platform can do today, but that's still only half of your total opportunity if you looked at generative AI more broadly.'

This speaks to a broader challenge in the enterprise AI market. While vendors are racing to add AI features to existing products, organizations often struggle to understand how these point solutions fit into a comprehensive transformation strategy. The Hackett-Celonis partnership aims to provide that strategic perspective by combining process intelligence with benchmarking insights to identify where AI can deliver the greatest impact.

The approach becomes particularly relevant when you consider the complexity of modern enterprises. As companies have grown and evolved, they've accumulated layers of customizations, workarounds, and process variations that make it difficult to understand how work actually gets done. Traditional enterprise software provides transactional data but lacks the process context that reveals why bottlenecks occur and where improvements would have the biggest impact.

Practical implementation

The partnership structure reflects the realities of how enterprise technology actually gets adopted. Rather than forcing customers to choose between vendors, Hackett and Celonis are taking a flexible approach to go-to-market. Fernandez says:

Because we didn't want to delay, we're going to try to integrate it and offer it - we want to avail it to clients whichever way they want to consume it.

The companies are starting with co-led efforts while working on deeper technical integration. A development team has been collaborating for about 60 days to figure out how to bring both capabilities "as seamlessly to a client as possible."

This approach extends to how the partnership handles potential overlap between Celonis's orchestration capabilities and Hackett's ZBrain platform. Rather than viewing this as competition, Fernandez frames it as giving customers the best options for their specific needs:

We want to give the client the very best, the most accurate, the most strategic set of options to realize value from the investments in either using Celonis or Hackett services or platforms. You just do what's right for the client. The capability is the capability, and I don't believe that we will be sensitive to 'Oh, they're doing this and we're doing that.' No, no, no. Provide the client with the best set of options.

Cutting through vendor confusion

The partnership also aims to address a significant challenge facing enterprise technology buyers: the proliferation of AI claims and the difficulty of distinguishing between meaningful capabilities and marketing hype. As Fernandez notes, buyers are "being bombarded with potential," making it difficult to identify genuine opportunities. He says:

Everyone's trying to position their capability - but I know it's our job to help CIOs and the business leaders to realize the value, to provide the clearest indication of what their current ecosystem can and can't provide, to be able to speak to the extended AI plans of each of those software companies realistically, and then propose the total opportunity.

This independent perspective becomes valuable when enterprise software vendors are, in Fernandez's words, "confusing for a reason" as they try to protect their install bases. He adds:

That confusion is not helping adoption.

My take

The Hackett-Celonis partnership positions itself as providing the objective analysis needed to cut through vendor positioning and focus on actual business value. With Hackett's transformation expertise and Celonis's process intelligence, the combination could offer a way to ground AI discussions in operational reality rather than theoretical possibilities.

The timing of this also reflects broader shifts in how organizations are approaching AI adoption. After an initial wave of experimentation and pilot projects (and hype!), many companies are now focusing on scaling AI to deliver measurable business outcomes. This requires moving beyond point solutions to more comprehensive approaches that consider how AI fits into broader transformation strategies.

It represents a recognition that successful enterprise AI requires more than just technology - it needs deep process understanding, transformation expertise, and the ability to design solutions that fit within existing enterprise architectures while delivering measurable business outcomes.

Execution, as always, will be key. However, the companies are confident in the partnership's ability to deliver rapid value. As Fernandez says:

Give us a quarter and I think clients will come back with some incredible client stories.

We will be following up on that promise!