Enterprise AI is entering a different phase. For the past few years, much of the discussion has focused on the technology itself: which models are best, which copilots companies should deploy and where generative AI can improve productivity.
Those questions still matter. But executives are now dealing with harder questions about management, economics and accountability.
Where does AI create measurable value? Which workflows should be redesigned around it? How much authority should AI agents have? Who is accountable when an AI system takes an action? And what does the board actually need to know?
These are questions we have been examining through Open Future Forum's executive communities and enterprise AI research. They also overlap with the work of Silvio Sangineto, a Microsoft executive and enterprise AI leader who has participated in Open Future Forum's AI leadership programming.
Sangineto's forthcoming book, The AI Leader Mindset, is due to be published in October 2026. The book and its leadership framework have recently received additional media attention, including a USA Today press release discussing earlier Forbes coverage.
For us, the interesting part is not simply the publication of another AI book. It is how closely the leadership questions surrounding it match what we are hearing from CEOs, CFOs, CISOs, CMOs, General Counsel and AI leaders.
AI is now a management question
An organization can deploy AI without materially changing how it operates. Employees can use copilots. Engineers can generate code. Marketing teams can produce content faster. Finance teams can automate parts of their analysis. All of that can be useful.
The bigger change comes when companies start redesigning workflows, responsibilities and decision-making around capabilities that previously did not exist. At that point, AI stops being solely a technology project.
The CFO wants to know whether it improves productivity, margins or return on investment. The CISO needs to understand identity, permissions, data leakage and the risks created by autonomous agents. The General Counsel has questions about liability, intellectual property, contracts, governance and accountability. The CMO is looking at customer acquisition, personalization, content production and the changing economics of marketing. The CEO has to connect those perspectives and decide what they mean for the company.
That is why AI leadership increasingly has to be cross-functional.
What Open Future Forum research is seeing
Open Future Forum has been tracking enterprise AI adoption across different executive functions. Our research includes the Executive AI Leverage Report, CFO AI Leverage Report, CMO AI Leverage Report, CISO AI Leverage Report, CEO AI Leverage Report, AI Transformation Report and our executive AI market maps.
Across that work, we keep seeing the same underlying shift. The enterprise conversation is moving from access to execution.
Powerful models are increasingly accessible to everyone. Access alone therefore provides less differentiation. The harder questions concern what companies do with those models: how they integrate them into workflows, what proprietary data they combine with them, how they govern their use and whether they can translate greater capability into better business results.
That is where leadership matters.
AI has to produce an economic result
One of the most noticeable changes in executive conversations is the increasing emphasis on economics. Eventually, an AI investment has to answer fairly conventional business questions.
- Did it increase revenue?
- Did it reduce cost?
- Did it make an important process faster?
- Did it allow the company to operate with fewer resources?
- Did it improve the customer experience?
- Did it create a capability the company did not previously have?
This is particularly visible in our conversations with CFOs. A demonstration of an impressive model is not the same thing as an investment case. As AI spending moves from experimentation into operating budgets, executives need to connect deployment with measurable enterprise value.
AI leaders therefore need to be able to discuss business outcomes, not just model capabilities. See how CFOs should measure AI ROI for the scorecard we use in that conversation.
AI agents change the governance question
The move toward agentic AI makes the leadership issue more complicated. Traditional enterprise software usually waits for a human to initiate an action. AI agents can increasingly execute parts of a workflow themselves.
They can retrieve information, communicate with customers or employees, interact with software, make recommendations and, depending on their permissions, take actions.
This creates a practical governance problem. Companies need to decide what an agent can access, what it can change, what requires human approval and how its actions are recorded.
They also need an answer to a deceptively simple question: who is accountable when the agent gets something wrong?
This is where AI stops being the responsibility of one department. Technology, security, legal, finance and business leadership all have a role.
General Counsel is moving closer to the AI decision
AI touches contracts, intellectual property, employment, privacy, cybersecurity, corporate governance and M&A. Agentic systems add another layer because software may increasingly act rather than simply provide information.
General Counsel therefore need to understand issues that were less prominent in conventional SaaS procurement:
- 01Responsibility: who bears responsibility for an incorrect action?
- 02Data: what happens to company data submitted to an AI system?
- 03Output rights: what rights does the company have over generated output?
- 04Subprocessors: what happens when an AI vendor relies on another model provider?
- 05Evidence: what evidence is retained showing what the system actually did?
- 06Escalation: when does an AI incident need to reach senior management or the board?
These are among the issues Open Future Forum is examining through our General Counsel Executive Forum and General Counsel AI research. They are not theoretical questions. They affect how companies buy, deploy and govern AI systems today.
CISOs have a similar challenge
Security leaders are approaching the same transformation from another direction. The important security question is no longer simply whether a model itself is secure. It is also what that model or agent can access.
An AI assistant producing a bad answer is one type of problem. An autonomous agent with excessive permissions and access to sensitive enterprise systems is potentially a much larger one.
Identity, permissions, monitoring and auditability consequently become important parts of enterprise AI architecture. Our CISO AI Leverage research tracks how security leaders are approaching these issues and where AI is creating both leverage and new forms of risk.
CMOs are seeing the economics of marketing change
Marketing offers another view of the same transition. Our CMO AI Leverage research examines how marketing and growth leaders are using AI and where they expect it to create leverage.
The opportunity goes well beyond generating copy. AI is affecting research, content production, personalization, analytics, customer acquisition, sales enablement and the amount of work a marketing team can produce.
For CMOs, the strategic question is therefore becoming less about finding isolated AI tools and more about designing the marketing organization around new capabilities. If a team can produce substantially more research, creative work and analysis with the same resources, that eventually affects budgets, organizational structure and expectations of the CMO.
CEOs have to put the pieces together
Ultimately, somebody has to decide what all of this means for the company. Technology leaders can deploy systems. Finance can measure returns. Security can manage technical risk. Legal can establish governance. Marketing and sales can identify new applications.
The CEO still has to make decisions about capital allocation, organizational design, competitive positioning, talent and speed.
The companies that create the most value from AI may therefore not be the ones that adopt every new model first. The more important advantage may be learning faster than competitors how to reorganize around what AI makes possible.
Why the AI Leader Mindset matters
This is what makes the idea behind The AI Leader Mindset timely.
The models will continue to improve. More companies will gain access to them. The cost of intelligence will continue to change.
The difficult part is organizational. Executives have to determine what to automate, what to augment, what to protect, what to measure and what should remain under human control. They also need organizations capable of adapting as the underlying technology changes.
Knowing how to use AI is useful. Knowing how to lead an organization whose economics and capabilities are being changed by AI is a different skill.
Silvio Sangineto and Open Future Forum
Sangineto has contributed to this discussion through Open Future Forum's enterprise AI programming, bringing his perspective as an enterprise technology leader to conversations with executives and practitioners in Silicon Valley.
That relationship matters to us because Open Future Forum is designed to connect research with the people responsible for making these decisions. Our communities bring together CEOs, CFOs, CISOs, CMOs, General Counsel, investors, founders and AI leaders.
Each group sees a different part of the transition. Putting those perspectives together helps us examine a question that is increasingly central to our work: what does building and running a valuable company look like when AI becomes part of the operating model?
That is a much bigger question than which model a company chooses. And it is one we expect executives to spend considerably more time answering.
Last updated: September 28, 2026
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