As Google and OpenAI Race to Automate Work, Trust Is the Next A.I. Battleground
Observer ·
As Big Tech pushes A.I. deeper into everyday business processes, Templafy’s Christian Lund unpacks why the companies that build the right infrastructure—not simply those with the smartest models—will be best positioned to turn A.I.'s potential into reliable business outcomes.
Google CEO Sundar Pichai speaks during a keynote address at Google I/O on May 19, 2026 in Mountain View, California" width="970" height="647" data-caption='From proposals and contracts to customer communications, enterprise A.I. requires more than impressive demonstrations: it needs clear permissions, consistent standards and systems that keep businesses accountable for the outcome. Photo by Benjamin Fanjoy/Getty Images '>
On Oct. 8, Google Cloud introduced a new Gemini agent designed to handle work across email, documents, spreadsheets and other business tools, planning tasks and connecting to company systems from a single interface. OpenAI , too, is pushing its A.I. offerings beyond the chatbot , developing workplace agents that can take on tasks across business applications. The direction is clear: the next stage of enterprise A.I. is about getting systems to do work. But as A.I. takes on more responsibility, how can businesses trust the work it produces?
The first phase of enterprise A.I. was a race for intelligence: speed, benchmarks, reasoning and capabilities, driven by the bet that better models would produce better business outcomes. The next phase will be about turning that intelligence into work that businesses can consistently trust.
Intelligence alone does not guarantee a usable outcome. A model can be highly capable and still produce the wrong result. It can use outdated information, miss an internal rule or create something convincing but not fit for purpose. Once that output becomes part of a proposal, contract, audit report or customer communication, the problem no longer belongs only to the technology team. It belongs to the company.
That is why enterprise A.I. governance is becoming a question of corporate responsibility. Businesses cannot delegate accountability to the model, just as they cannot delegate it to any other piece of software. If an organization puts A.I. into a business-critical process, it is also taking responsibility for the conditions under which that A.I. operates.
Early enterprise adoption focused heavily on what the technology could do. Now, as companies move toward A.I. capable of executing multistep tasks, they must consider both its capabilities and the risks of leaving its use entirely in the hands of individual users. The answer is not to slow down adoption or require people to manually check every word a model produces. It is to build systems that make the right outcome more likely from the start.
Companies often talk about A.I. as though it were an independent decision-maker. But a model does not decide what information it can access, which rules should apply or when a human should review its work. The organization makes those decisions. Executives therefore need to be deliberate about which work A.I. should support, which work it should complete and where human judgment must remain.
An important distinction is between A.I. that helps people make decisions and A.I. that makes or executes decisions on their behalf. A.I. can help someone reach a stronger foundation for a decision more quickly by gathering information, surfacing options and doing much of the work required to get there. But informing a decision is different from setting direction or taking consequential action. The higher the stakes, the more important it becomes to establish clear accountability and determine where human oversight is necessary.
One way to think about this is to replace A.I. with an employee. A business would consider what information that employee can access, which systems they can use, what they are responsible for and where their work needs review. A.I. should be approached in much the same way. This becomes particularly important as A.I. agents gain access to business tools and internal information. A system that can draft a proposal presents one set of risks, while a system that can pull confidential information, modify a document and send it to a customer introduces others. The more an A.I. system is trusted to do, the more carefully a company must define its permissions and responsibilities.
Even capable A.I. users can produce different results from the same tools. For enterprises, that makes consistency as important as quality. The business needs to determine what informs the work, where human judgment is required and what a trustworthy result looks like.
Ultimately, accountability sits with the business. If A.I. is producing work on behalf of the company, the company remains responsible for the outcome. Setting accountability is the first step. The next is making sure those expectations show up in the work itself.
“Governance” has become a catch-all term in enterprise A.I. But much of what businesses actually need is more practical: direction. Companies have always directed how work gets done. Before A.I., that direction was often established before work began. An employee creating a document might select an approved template, follow a prescribed workflow and answer predefined questions.
A.I. flips that dynamic around. The user increasingly starts by describing what they want to accomplish, and the business then needs to understand that intent and bring the right context, instructions and guardrails into the process.
If someone is creating a proposal, they should not have to know every piece of information to include, every internal rule that applies or the perfect prompt required to get there. The system should understand what that person is trying to achieve and guide the work accordingly.
This is also where the industry’s focus is beginning to broaden beyond model performance. Google’s latest enterprise agent offering, for example, combines access to company information and business applications with administrative controls and governance features. The challenge is to make an agent capable of completing a task within the boundaries the organization has established.
For leaders assessing whether an A.I. system is ready for enterprise use, the output is the clearest test. Does it meet the business’s quality standards? Does it produce that quality consistently? Can it do so at a cost that makes sense? And across all three, does it introduce an acceptable level of risk?
An impressive A.I. demonstration can prove what is possible. An enterprise-ready system has to deliver that standard repeatedly in the work the business actually depends on.
Once companies begin looking at A.I. as a complete system rather than a standalone model, the competitive landscape changes. Model performance will continue to improve and access to capable models is expanding, but brainpower is very quickly becoming a commodity. As businesses gain access to many of the same underlying technologies, simply choosing the smartest model becomes less of a durable advantage. What matters increasingly is how that model is deployed: the information it receives, the instructions it follows, the tools it can access and the checks that shape its output.
There is an old saying that there are no stupid questions, only stupid answers. A.I. complicates that idea because it will answer almost anything it is asked. A poorly framed request, missing context or incorrect assumption can quickly become a convincing, but flawed result. Even the smartest person will struggle with a bad question and none of the context they need. A.I. is no different.
Businesses should therefore focus less on constantly choosing the smartest model and more on the infrastructure surrounding it. What is the user trying to achieve? What company knowledge should inform the task? What instructions should influence the result? Which model makes sense for that job? What needs to be checked before the work can be used?
This is the role of the orchestration layer: connecting user intent with company knowledge, relevant tools, instructions and controls, then directing the work through the appropriate steps. Different tasks may call for different models or levels of human involvement. The model provides the brainpower, but the orchestration layer determines whether that brainpower produces something the business can actually use.
Enterprise A.I. is no longer just a technology initiative. As A.I. becomes embedded into everyday business processes, leaders are being asked to make decisions about trust, accountability and governance, not just technology investments. No longer is the question whether A.I. can generate content, but whether organizations can confidently rely on that work within the systems and standards that govern their business.
Businesses should continue to invest heavily in A.I., but be much more deliberate about where they focus. Too much attention still goes toward what the technology can do versus why a business needs it, what it should accomplish and how success should be measured.
The potential of A.I. is no longer the question. The challenge is how to put that potential to use. A useful starting point is to replace A.I. with a person. Businesses still need to set direction, provide the right information and remain accountable for the outcome. The same goes for when the work is performed by a model or an A.I. agent.
The next enterprise A.I. race will not be won simply by having the smartest model. It will be won by the companies that know what they want that intelligence to do and build the systems that allow them to trust the result.