According to analysts from Siemens, “Industry is evolving. It’s no longer just about being automated; it’s about being autonomous.”[1] In a previous article, I agreed with that assessment.[2] However, I also agree with Chris Sluty, Co-Founder and Chief Product Officer at FloQast, who argues that autonomous enterprises still need a steering wheel so that humans can step in when the unexpected happens. He compares the autonomous enterprise to his self-driving car. He writes, “I have no desire to own a car without a steering wheel. Why? Because there are moments — a sudden construction zone, an erratic driver, a patch of black ice — where I need to take control. The same holds true for the self-driving enterprise.”[3] He adds, “The goal isn’t total autonomy. The goal is trust. And trust requires a steering wheel. … The true value of enterprise-level AI isn’t in replacing humans, but in shifting the human’s role. We should be aiming for AI to handle 80% to 90% of standard, routine transactions. This is the straight road driving.”

The Need for a Steering Wheel

“The enterprise debate over whether to adopt AI agents appears to be over,” writes journalist Sean Parker. “However, the next challenge is deciding how much authority organizations are willing to give them.”[4] He cites a study by Caylent that concludes, “For the past several years, organizations have focused on whether generative AI could deliver value. Now, the focus is shifting toward a more complex question: how can businesses allow AI systems to act while maintaining security, accountability, and oversight?” Dave McJannet, CEO of Dome Systems, agrees with those findings, although he couches the current situation in slightly different terms. He writes, “Every enterprise is running the same experiment right now: handing more decisions to A.I. agents and watching to see what breaks. Some are further along than others, but few are proactively asking the question that ultimately determines whether agentic A.I. can move beyond pilots: what happens when an autonomous system takes an action no one can fully explain?”[5]

When the goal, as Sluty noted, is trust, relying on “black box” models to make decisions can undermine that goal. Tech journalist Shrikanth Govindarajan explains, “The biggest barrier to enterprise AI execution is no longer model capability alone. It is trust. Once AI begins to act inside live workflows, the questions become harder and more consequential. What did the system do? Why did it do it? Can the action be explained, audited, reversed, and governed?”[6]

At Enterra Solutions®, we understand the importance of Causal AI as we advance the field of Autonomous Decision Science — a field that deals with data-enabled prescriptive and anticipatory analytics and insights for companies across a broad range of industries. Enterra® automates a new way of problem-solving and decision-making, going beyond advanced analytics to understand data, perform analytics, generate insights, answer queries, and make decisions at the speed of the market. And those insights and decisions are explainable.

McJannet adds, “An AI agent needs the same basic infrastructure as any new employee on day one: a verified identity, a clearly defined scope of authority, and a record of every action it takes. Companies that build those controls into the foundation are scaling agents with confidence. Those that postpone governance until after deployment are discovering how difficult — and expensive — it is to bolt accountability onto systems already embedded across the business.”

Gaining Trust in the Autonomous Enterprise

The autonomous enterprise is more than a rules-based business. As Govindarajan explains, “In any business, the standard operating procedures (SOPs) are easy to document.” He adds, “The challenge lies in the non-standard items, the tribal knowledge that lives only in the heads of senior practitioners.” Enterra’s Autonomous Decision Science® (ADS®) platform can autonomously analyze data, generate insights and make subtle, contextually informed, judgment-based decisions quickly, accurately and with limited human intervention, and then learn from the results of those decisions. It can effectively reshape the way companies structure and optimize their value chain. Enterra’s proprietary math and human-like reasoning engines can to generate insights and recommendations with the subtlety and judgment of a company’s best experts — but at a level of speed and accuracy humans can’t achieve. Enterra’s AI platform pushes the bounds of what’s possible by running countless scenarios to find the combination of actions that maximize business goals most effectively, freeing up employees to attend to other tasks and to support loftier business goals. Enterra’s System of Intelligence™ has proven 90% accuracy.

Parker suggests strong guardrails are important if autonomous systems are to gain corporate trust. He believes guardrails should include: security scanning and approval gates; automated testing and quality checks; explainability; audit logging and traceability; policy-based limits; and rollback capabilities. He concludes, “Organizations are no longer evaluating AI only by intelligence or accuracy. They are evaluating whether AI systems can operate safely inside complex business environments.” McJannet adds, “Many organizations have built technically capable agents only to discover that their own teams are reluctant to let those agents touch any consequential business processes. The hesitation usually comes down to a simple question nobody can answer with confidence: if this agent makes a mistake, how will anyone know what happened? Capability without accountability doesn’t scale.”

Concluding Thoughts

“The C-suite is facing an unprecedented challenge, writes Woongsik Su, a consultant with Dassault Systèmes. “As autonomous AI agents increasingly make independent decisions across enterprise operations, traditional command-and-control leadership models are becoming obsolete. We're not just managing technology anymore — we're governing digital entities that can reason, plan, and act with minimal human intervention. This shift demands a fundamental reimagining of executive leadership, organizational structure, and strategic oversight. The question facing today's leaders isn't whether to embrace agentic AI, but how to govern it effectively while maintaining strategic control and ethical alignment.”[7] Like the experts cited above, Su believes keeping a human behind the steering wheel is essential. He explains, “Smart leaders are shifting from micromanagement to macro-orchestration. Instead of controlling every decision, they're establishing frameworks that guide autonomous behavior while preserving strategic alignment. This means defining clear decision boundaries, establishing ethical guardrails, and creating feedback mechanisms that ensure AI agents remain aligned with business objectives — even as they operate independently.”

Business executives feel the pressure to transform into autonomous enterprises and, simultaneously, understand that fully trusting AI agents can be risky. That’s why Su concludes, “The most successful enterprises are developing sophisticated governance architectures that balance autonomy with accountability.” I would be remiss if I failed to mention one other growing concern about autonomous systems and autonomous enterprises: cybersecurity. Journalist Kelly Knight explains, “Cybersecurity leaders are confronting an inflection point as agentic AI security becomes the defining challenge of this year’s threat landscape, with attackers and defenders racing to harness autonomous tools at unprecedented speed and scale.”[8] It, too, requires a steering wheel controlled by humans. Jon Oltsik, principal analyst in residence at SiliconAngle and theCUBE Research, told Knight, “We have to be much more sensitive to where the human in the loop takes place. We have to guide this technology. We have to understand this technology. But humans are still necessary to make decisions, and will be.”

Footnotes

[1] Siemens, “The autonomous enterprise: Why automated is no longer enough,” Fast Company, 30 July 2026.

[2] Stephen DeAngelis, “The Autonomous Enterprise is Becoming the Baseline Business Model,” Enterra Insights,

[3] Chris Sluty, “Self-driving enterprise still needs a steering wheel,” Fast Company, 5 May 2026.

[4] Sean Parker, “The Agentic AI Debate Is Over. Now Enterprises Are Building Guardrails,” Redmond Magazine, 8 August 2026.

[5] Dave McJannet, “As A.I. Agents Gain Authority, Governance Becomes the Primary Constraint,” Observer, 6 August 2026.

[6] Shrikanth Govindarajan, “Why trust is the real architecture of AI execution,” Dataquest, 14 June 2026.

[7] Woongsik Su, “Governing the Autonomous Enterprise: Leadership in the Age of AI Agency,” LinkedIn, 12 October 2025.

[8] Kelly Knight, “Agentic AI security tests enterprise defenses as scale outpaces strategy,” siliconANGLE, 6 August 2026.