Every new frontier model release seems to spur a fresh round of doomsday articles. Just Google “the end of white-collar jobs,” and you’ll be bombarded with discourse on the end of modern work, the unraveling of the social contract between employees and organizations.
What I don’t see anyone talking about, however, and what I believe is a far more productive conversation, is the opportunity for knowledge workers.
Nobody understands critical business processes better than your line-of-business employees. Not executives. Not IT. Not even the most advanced LLMs. These are your business analysts and RevOps professionals, your supply chain managers and finance leaders, and the employees whose expertise has been forged over decades.
For an enterprise to become truly intelligent, these workers must be involved in how AI workflows are built and deployed. Their guiding hand is the only way AI can learn and truly understand your business.
But what does this transition look like, and how can organizations start operationalizing AI in a meaningful way alongside knowledge workers? Let’s take a look.
What enterprise intelligence requires
Imagine walking your board through a set of financials and recommending specific actions. Then, in your next meeting, you walk everything back because your AI layer got the numbers wrong.
There is no faster way to kill an AI initiative than by delivering wrong outputs. Without trust, the whole system falls apart.
In our recent survey of 1,400 business and IT leaders, we found that while over 90% of organizations are using AI, only 28% trust it to support decision-making. As for how many organizations scaled their AI pilots into production, the number was just under 25%, suggesting a very strong correlation between trust and operationalization.
An intelligent enterprise, then, is an organization that has trustworthy AI embedded across the business.
At Alteryx, we say the results of any AI system must follow our VURA framework: an AI system and its outputs must be visible, understandable, repeatable, and auditable. In other words, two people need to be able to go to AI with a question and arrive at the same answer; anyone who uses AI in their workflows must be able to explain how their AI system arrived at that answer.
Who’s responsible for operationalizing AI?
Enterprise intelligence is about trustworthy AI deployed throughout key business processes, but who’s ultimately responsible for these AI systems and processes: IT teams or knowledge workers?
Let’s say you want to use AI in your Sarbanes-Oxley process, e.g., your journal entries, revenue recognition, access controls, etc. Before IT can help you build a new AI workflow, IT must first understand your Sarbanes-Oxley process in great detail. Then, they have to code a tool your finance team can trust.
It’s possible, sure. But creating this solution would take an inordinate amount of time. Then, when a new regulation comes along or you have an acquisition, the whole thing falls apart. You have to get back in line with IT to retune everything.
Moreover, if your books don’t balance out or if you fall out of compliance, IT does not want to have that responsibility fall on them. You can see why ownership of AI systems and workflows must sit with line-of-business workers. They are the only ones with the expertise to ensure the veracity of AI’s outputs. They are the only ones who can successfully shape and define its logic and oversee its ongoing execution.
Data is the fuel. Business logic is what keeps AI on course.
Finally, there’s the question of data. We’ve all heard “bad inputs, bad outputs.” Seeing as I’m the CEO of a data analytics company, you might expect me to say that reliable data is the end-all, be-all when it comes to trustworthy AI outputs.
And while it’s absolutely essential, it’s only the first step.
Aggregating your enterprise data into a cloud data platform is immensely useful. All of that data becomes readily accessible. You gain a single source of truth across teams and workflows. But you can’t point your LLM at a cloud data platform and ask it to make sense of your data for a complex business process.
Again, you need the people who understand these critical processes to guide your LLMs to interpret the right data in the right way. This is what will make your AI systems visible, understandable, repeatable, and auditable. Yes, you need clean, reliable data. But more than that, you need business logic around that data, and that can only come from your knowledge workers.
The five pillars of enterprise intelligence
At the highest level, enterprise intelligence rests on five core pillars:
- Trustworthy, transparent data
- Empowered business analysts
- Shared responsibility across the C-suite
- Cross-functional collaboration
- Leadership that evolves alongside AI
Each pillar reinforces the same core idea: AI only becomes valuable when it’s grounded in reliable data, shaped by real business expertise, supported by executive ownership, and scaled across teams that can put it to work to improve their daily processes.
Tap into the intelligence all around you
As a business leader looking to build an intelligent enterprise, the most important questions you can start asking yourself are the ones around operationalizing AI in key business processes. What would it take for you to trust AI’s outputs? What would make AI-powered processes superior to your current ones?
Once you have those answers, engage your line-of-business workers immediately. Give them ownership and autonomy. Rather than asking AI to replace them, lean into their intelligence. Let your knowledge workers use their expertise to amplify, shape, and govern AI. Their business mastery is what makes enterprise intelligence possible