Explore how accountability and earned authority should guide AI agent deployment. Discover the data layer controls that make responsible AI practical rather than theoretical.
Her Story
About Shruti
Shruti Sakhuja serves as a technology manager at Experian. With more than 22 years in technology, she started her career as a developer and progressed through roles including junior software developer and expert software developer before moving into leadership positions. She completed her undergraduate education in India and attended Utah State University for graduate studies after relocating to the United States. She has been part of the Grace Hopper Conference for the past four years and actively participates in the Women at Experian group as both a mentee and a mentor. She is currently pursuing the CTO program with Wharton Executive Education.
Her Interview
Ten minutes with Shruti
01What do you attribute your success to?
I attribute much of my success to curiosity, continuous learning and a willingness to get uncomfortable. I was raised to believe that education is something no one can ever take away from you, and that philosophy has shaped my entire career. Some of my biggest growth came when I stepped outside what was comfortable, whether that was moving countries or reinventing myself after more than a decade as a technologist to become a people leader. I have made mistakes and taken some wrong turns along the way, but I wouldn't change them because every experience has contributed to the person and leader I am today.
For me, success isn't just about how far I can go. It's more about continuing to grow.
02What’s the best career advice you’ve ever received?
The best advice I received came from my father: ‘First deserve, then desire.’ He taught me to work hard, keep learning and prepare myself for the opportunities I wanted. But through my own career, I've added something to that: ‘Don't underestimate what you already deserve.’ I've learned to take opportunities that challenge me, even when I don't feel 100% ready.
03What advice would you give to young women entering your industry?
My advice to young women entering technology is: stay curious, keep learning, and don’t wait until you feel 100% ready. Take opportunities that challenge you, especially the ones that make you a little uncomfortable, because that’s often where the greatest growth happens. Over the years, I’ve added something to what my father used to say: First deserve, then desire, but never underestimate what you already deserve.
04What are the biggest challenges or opportunities in your field right now?
I think AI is both the biggest opportunity and one of the biggest challenges in technology right now. It has tremendous potential to transform how we work and solve problems, but technology leaders also have a responsibility to make sure we use it securely, ethically and with the right governance. It is not just about adopting AI, it is about using it responsibly to create meaningful impact.
The challenge is keeping up with the technology, The opportunity is using it to move people and organizations forward.
05What values are most important to you in your work and personal life?
The values most important to me are continuous learning, integrity, and empowering others. I want to keep challenging myself and growing, but I also want that growth to have an impact beyond me. Whether it’s the people I lead or my own children, I want to help others recognize what they’re capable of. For me, real success is not only what you accomplish, but the positive impact you leave on people along the way.
Her Content Hub
Articles by Shruti
First Deserve, Then Desire: What Accountable AI Can Learn From an Old Idea
Building accountability into AI systems through data governance, not just intentions.
My father had a sentence he repeated so often it became part of how I think: First deserve, then desire. Earn the opportunity before you ask for it.
I have worked for years in fraud and identity, a field where data decisions land on real people. A transaction is approved or declined. A person is verified or flagged. Behind each decision is a chain of data that someone defined, someone controlled, and someone should be able to explain.
That work taught me that accountability is practical. It isn't about good intentions. It is about whether you can show who had access to what, where the data came from, and who stands behind the result.
Now the machines are deciding
We are handing more decisions to AI agents, and my father's sentence applies to them directly. Have these systems earned what we are giving them?
Too often, no one decided. An agent receives whatever access its integration came with, and it goes live. We would never treat a new employee that way. We start people small, watch their judgment, and expand their responsibility as they prove themselves. Authority is earned.
Where accountability actually lives
Most conversations about responsible AI focus on the model: its bias, its explainability, its ethics statement. These matter. But an agent is only as accountable as the data environment it operates in.
Three controls make the difference:
- Entitlements decide what an agent is allowed to touch.
- Data products and semantic layers decide who owns the inputs and what they mean.
- Lineage lets anyone reconstruct, after the fact, which data drove which decision.
This is the focus of my research at Wharton: governing AI agents through the data layer, where accountability can be built and audited rather than simply promised. A policy document states an intention. The data platform enforces it.
What leaders should ask
You don't need to build a data platform to lead this conversation. Whether you run a technology team or sit on a board, start with three questions:
- What can our AI systems access, and who decided that?
- Can we trace any automated decision back to its data and its owner?
- How much autonomy have we granted, and what evidence justifies it?
Women leaders are often the ones who ask the uncomfortable questions in the room: Who reviewed this? What happens when it's wrong? Who answers for it? In the AI era, those questions are not a brake on progress. They are what makes progress trustworthy.
My father was right. First deserve, then desire. It applies to the people we lead, and now to the systems we deploy. The organizations that earn trust in the AI era will be the ones that made autonomy something their systems had to deserve.
Explore how organizations should deliberately establish autonomy budgets for AI agents, defining explicit limits on data access, system permissions, and autonomous actions while maintaining accountability and governance through technical enforcement rather than policy documents.
The Autonomy Budget: Why Liability Should Scale With What Your Agents Can Do
Designing explicit autonomy budgets to align AI agent capabilities with organizational accountability and risk tolerance.
Every organization deploying AI agents is answering a question it never formally asked: How much is this agent allowed to do without a human? In most companies, the answer was not chosen. It emerged from whatever access the integration happened to receive, whatever tools were wired in, and whatever limits someone remembered to set.
That is an odd way to make one of the most consequential decisions in enterprise technology. We would never hand a new hire unlimited system access, spending authority, and the power to act irreversibly, then wait to see what happened. Yet that is how many agents go live.
Autonomy Is Already Being Spent
Autonomy is not abstract. It is granted in concrete units: which data an agent can read, which systems it can write to, how much money it can commit, how quickly it can act, and whether its actions can be undone. Each of these is a withdrawal from an account nobody is tracking.
I call the alternative an autonomy budget: an explicit, deliberate allowance for what an agent may do unsupervised, sized to the evidence the organization can show that the agent behaves as intended. A low-risk, reversible task earns a generous budget quickly. An action touching sensitive data or producing irreversible outcomes gets a small one, and a human stays in the loop.
Liability Should Follow the Budget
Models will always make mistakes. That is not news, and it is the wrong question for accountability. The question a regulator, a court, or a board will ask is different: Did the organization grant autonomy it could not justify?
This is how accountability works everywhere else. The more authority we delegate to a person or a process, the more oversight, evidence, and control we expect to see. AI should be no different: the more autonomy an organization grants an agent, the more accountability it must be able to demonstrate. Responsibility scales with the budget, not with the novelty of the technology.
A Budget in a PDF Is a Wish
Here is where most governance programs fail. They state the budget in a policy document and then rely on prompts and guardrails to honor it. Those are advisory. A clever prompt, a model update, or an edge case can route around them.
The data layer is different. Entitlements decide what an agent can touch, and if the platform says no, the agent cannot say yes. Data products establish who owns and stands behind the inputs. Semantic layers keep agents working from the same definitions as the business. Lineage proves, after the fact, what data drove an action. A budget enforced in access controls and recorded in lineage is not a wish. It is a fact you can show an auditor.
How to Start
- Classify actions by consequence. Separate what is reversible from what is not, and what touches sensitive data from what does not.
- Set budget tiers and enforce them technically. Express each tier as entitlements and approval requirements in the platform, not as guidance.
- Make increases earn their place. Raise an agent's budget only when monitoring, testing, and lineage show it has earned it, and review the decision like any other material change.
The organizations that win with agents will not be the ones that grant the most autonomy. They will be the ones that can afford to grant the most because they can prove it was earned.
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