Every enterprise deserves the freedom to transform its business on its own terms. Right? It’s tough to argue against such a basic idea.
As a business leader, it’s your right, remit, and responsibility to ensure your operation evolves to meet the challenges and opportunities stemming from changes in the technological and commercial landscapes. The emergence of Enterprise AI means that right now, there’s an abundance of both challenges and opportunities. And it’s your job to shape that evolution as you see fit to suit the specific needs of your business.
Eroding freedoms
But somewhere along the line, these freedoms began to erode, as processes and their underlying data—an enterprise’s greatest source of value and fastest lever for change—became imprisoned by business systems. Rather than serving business strategies, processes have had to be retrofitted to suit these technologies.
More than that, some tech suppliers are even closing data ecosystems or disincentivizing data use beyond their system boundaries. Meaning the pace, possibility, direction, and cost of process transformation have become increasingly dictated by systems and systems vendors. Strategies being defined or constrained by systems is not just the tail wagging the dog. It’s a significant threat to commercial success in the AI era. Businesses need to leverage their process data to embrace new, transformative Enterprise AI applications.
A leading manufacturer of design and architectural surfaces provides a great example of this in action. The company consolidates data from multiple systems with business context to power an AI assistant for credit block management. The Enterprise AI solution enables it to process up to five times more orders per day without additional risk and is “a game changer.”
A systems-centric approach to transformation, or restricted use of your own data, slams the brakes on digital transformation and the kind of AI ROI that this company is experiencing.
End-to-end transformations span multiple teams and systems
The above example illustrates how an open ecosystem of data flowing between each system is essential for effective business transformations. Why? For the simple reason that to optimize most key operational processes (such as Order-to-Cash or Procure-to-Pay), or to complete major migrations or integrations (such as ERP and AI), the work will span multiple teams using multiple systems.
CIOs or CTOs need to be able to steer these operational transformations at a process level, and that means compiling and analyzing data holistically from across the tech stack. It means understanding the interdependence of disparate teams and systems. And it means having the freedom to deploy process-level solutions or applications (fueled by comprehensive, accurate data) to course correct processes to achieve business goals.
For example, one of the world’s leading providers of paper-based packaging uses Enterprise AI to optimize inventory management anomalies stemming from inconsistent master data spanning multiple systems, sites, and sources. This data is analyzed and harmonized via an AI layer, producing front-end dashboards to drive impactful, data-driven process optimizations. Optimizations that other businesses may find more challenging if their data gets locked up in individual systems.
Diluting or derailing your Enterprise AI competitive advantage
There’s a straight line between the effectiveness of Enterprise AI and the nature, quality, and comprehensiveness of the data that feeds it. Where Enterprise AI initiatives stumble, underwhelm, or stall altogether, it’s frequently because they lack process data and business context.
The star of the Enterprise AI data show is the business-specific context, knowledge, and detail that is formed by consolidating systems and process data. This data-set trains and fuels AI tools with the information they need to accurately analyze and understand how your business operates. And this is the cornerstone on which Enterprise AI drives business-wide transformations to help produce goods and services at a better margin than your competition.
Again, this level of understanding can only be achieved where the data is captured and analyzed at the process level (taking in all systems and teams). Restricting data from being used at this tech-stack-wide, cross-business level diminishes Enterprise AI’s decision-making ability. You’re asking the AI to help reshape your business with one hand tied behind its back.
For example, one of the largest exchanges in the world is implementing an AI agent in contract renewals—connecting every one of its multiple source systems in Celonis to gain a single view of the procurement process. B3 anticipates being able to reduce contract renewals from 30 days to 10 days (or fewer).
Anything less than full data use and access and you’re compromising competitive advantage.
The danger of systems-level siloes in AI optimization
Every single enterprise is redrawing its IT landscape. Under ever-growing pressure to integrate and monetize new technology, CIOs and CTOs are busily reviewing their enterprise architecture—wondering where and how to accommodate this big, shiny new box marked ‘AI.’ At the same time, software vendors are telling them, “We’re all about AI, you need us for AI, you should put us in that box. In fact, let us look after that box.”
Don’t. Enterprise AI optimization that maxes-out RoAI (the ROI of AI) operates at the process level, not system level.
As the ‘The State of AI’ survey from McKinsey confirms, the redesign of workflows has the greatest effect on organizations’ ability to see a positive earnings impact from the use of AI. Workflows—not individual systems. Enterprises finding themselves tempted (or encouraged) to deliver AI optimizations at the departmental or even system level should recognize this route is fraught with dangers.
When Enterprise AI solutions are confined to individual systems or walled gardens—whether it’s the CRM, procurement platform, or any other isolated tool—they become essentially glorified automation scripts only able to optimize within narrow boundaries. This approach fundamentally limits AI’s potential, creating system-centric siloes and reducing it to what amounts to sophisticated RPA bots that miss the bigger picture of how business processes interconnect.
Open tech ecosystems promote innovation—potentially game-changing innovation in the current AI landscape. To take full advantage of Enterprise AI solutions, organizations must be free to decouple the origins of their data (i.e., systems), from what they choose to do with the data (i.e., pursue their AI strategies).
How do I free my processes?
In an AI-driven world, there’s no such thing as a single-vendor strategy. To embrace major transformations and optimize your Enterprise AI future, you’ve got to be able to use your data to shape your destiny—unrestricted by systems’ boundaries or vendor lock-in.
If it’s news to you that this essential freedom is under threat from closed platforms or potentially anticompetitive behavior, here’s the first step to freeing your processes: check your backyard. Take a close look at your systems’ contracts for any:
- Data use provisions or restrictions
- Tariffs, price hikes, or any other disincentive to use your data in third-party applications or systems
Don’t panic, many major systems vendors support (or even actively encourage) open data ecosystems and third-party engagement. But if you do find your data locked in system-specific walled gardens or taxed for usage beyond their walls, speak to your account manager. Get them to justify dictating how you use your own information—and make sure data usage is a key part of the conversation when it comes to contract renewal.
Because when platform providers hold data and processes hostage, they deny you—their customers let’s not forget—the freedom to choose the best solutions for your business.