Paula Goldman is Salesforce's Chief Ethical and Humane Use Officer, where she leads a cross-disciplinary team of data scientists, engineers, political scientists, and researchers dedicated to shaping responsible AI governance and trusted technology. With a Ph.D. in anthropology from Harvard University, she brings a uniquely human-centered perspective to enterprise technology, driving internal culture and technical guardrails that align artificial intelligence with human values. A recognized global leader in technology ethics, she is also the author of Manage the Machine: How to Harness Human-AI Collaboration at Work, which explores practical frameworks for balancing cutting-edge AI capabilities with original human judgment, creativity, and strategic discernment.
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How Salesforce Navigates Responsible AI
Alex introduces Paula as Salesforce's Chief Ethical and Humane Use Officer—a pioneering role created seven years ago to build operational guardrails around rapidly advancing technologies.
Paula explains that solving complex AI ethics issues requires diverse perspectives. Her team pairs data scientists and engineers alongside anthropologists and political scientists to tackle technical, social, and cultural challenges.
Rather than relying solely on a dedicated ethics team, Paula emphasizes the necessity of embedding a culture of critical questioning across the entire company.
Alex and Paula highlight how trust serves as a core foundational value for enterprise software, with Alex sharing his past experience as a Salesforce partner navigating their rigorous security and user-experience standards.
"A lot of the work is very technical, and I have a really cool cross-disciplinary team where I have data scientists, engineers, political scientists, and I'm an anthropologist. The cool thing is that you kind of need to think about these problems from all these different angles to get the right answer." — Paula Goldman
Building Authentic Communities in the Age of AI
Alex reflects on Salesforce’s ability to stay innovative while maintaining strict standards to protect and improve customer and partner experiences.
Paula shares a story from her book about Harley-Davidson’s 1990s turnaround, which succeeded by focusing deeply on its enthusiast community (the Harley Owners Group) and local partner networks, turning a brand into a core personal identity.
Drawing a parallel to Harley-Davidson's fan loyalty, Paula notes that the Salesforce ecosystem creates deep human impact and opportunities—even mentioning a Salesforce admin who got a company tattoo out of gratitude for the community's support during a hard time.
Paula highlights that as AI agents proliferate on both the business and consumer sides, filtering out noise will become easier. Consequently, authentic human relationships and meaningful connections will matter more than ever.
"As everyone's personalizing messages and automating marketing and how you reach humans... how you reach people and really connect with them makes a massive, massive difference, right? And how authentic that is, how the message stands out, and how you deepen those relationships." — Paula Goldman
Governance, Substance, and Human Connection in AI Communication
Paula notes that successful organizations will combine curated human communities (like Dreamforce) with AI capabilities to stay competitive in an increasingly noisy world.
Alex emphasizes that industries like healthcare, pharma, insurance, and investor relations face a unique challenge: they must back up their claims with heavy evidence, yet modern audiences lack the patience to digest dense, multi-page documents.
Effective AI communication should meet users where they are—providing guided, digestible journeys while keeping rich, validated substance accessible beneath the surface.
Alex cautions that audiences quickly spot pseudo-personalized, AI-generated content. Paula agrees, pointing out that strong governance is vital to ensure outputs are both accurate and culturally sensitive, citing historical marketing automated blunders as a reminder of what happens when oversight fails.
"How do you have that sort of governance that ensures not only that what you're saying or sending out is accurate, but that you're not tripping over things that could either get you in trouble or be really embarrassing?" — Paula Goldman
Probabilistic and Deterministic AI in Enterprise Workflows
Paula outlines how Salesforce develops controls for AI agents, allowing administrators to define specific topic limits, tone parameters, and toxicity filters to keep AI outputs safe and accurate.
Alex explains the distinction between traditional "deterministic" software (rigid, rule-based systems that produce the exact same outcome every time) and modern "probabilistic" generative AI (flexible systems that adapt fluidly but vary in their outputs).
While deterministic systems were often frustratingly rigid (like old phone menu trees), purely probabilistic systems risk giving incorrect information or making unauthorized commitments (like promising out-of-policy customer refunds). Successful enterprise deployment requires combining both approaches.
Unlike casual personal uses of AI, regulated and high-impact enterprise workflows require strict predictability because errors carry direct financial, legal, or reputational penalties.
"A lot of what my team works on with our agent-first team is: how do you create essentially a control pane where the person setting up an AI agent is able to say, 'Here are the topics; please don't go outside these topics'?" — Paula Goldman
Safe Experimentation and the Human-AI Handoff
Alex notes that amid widespread AI market noise, enterprises will gravitate toward trusted platforms and dedicated startups that provide real rigor for high-value or regulated workflows.
Paula stresses that while organizations cannot afford to wait until every detail is perfected, experimentation must happen inside a secure sandbox rather than live, high-risk operational environments.
Using tax advisory firm 1-800Accountant as an example, Paula explains that while AI can easily handle routine status queries, strategic or regulated decisions (like business restructuring) require a seamless handoff to human experts to ensure compliance and build deeper customer relationships.
Alex describes how merging fixed, deterministic FAQs with dynamic, organic AI questions gives users both structured guidance and fluid conversational capabilities.
"When is the human-AI handoff? What is the right time? Especially here, we're talking about when AI escalates to a person. Setting those rules in advance and keeping them in mind to understand as they evolve... those types of things will become standard questions when we're implementing AI." — Paula Goldman
Human Judgment and Curiosity in an AI World
Alex notes that during peak busy seasons—such as tax season or annual employee benefits enrollment—AI can handle repetitive tasks, freeing professionals to focus on high-value human connections rather than fearing job displacement.
Paula argues that as AI capabilities advance, skills like relationship-building, situational judgment, and taste-making will become the rarest and most valuable assets in the workforce.
Drawing from her upcoming book, Paula compares working with AI to managing people and delegating tasks. Effective delegation requires giving clear context and guardrails while leaving enough flexibility for the system to execute efficiently.
In response to Alex's point on curiosity, Paula warns against automation bias—the well-documented risk where individuals, particularly outside their primary expertise, uncritically trust AI-generated outputs instead of applying critical thinking.
"I deliberately called the book Manage the Machine, because I think that's what it is, right? We're used to thinking about management as managing people. It turns out that... there's a lot of similarity in terms of learning how to delegate. Learning not only how to give a task in a way that AI understands how to do it well, but also giving the right context and the right guardrails." — Paula Goldman
To Master the Machine, Master Being Human: AI Collaboration
Paula warns against "cognitive offloading"—the tendency to rely uncritically on AI outputs. Over-reliance risks dulling human analytical skills, particularly when exploring new or unfamiliar concepts.
Using IKEA's prototype design process as an example, Paula explains how intentionally pausing AI usage encourages original human thinking. The IKEA team broke through generic AI suggestions to design a light, foldable couch by focusing on human metaphors like camping first.
Paula emphasizes that even when AI generates ideas or handles routine tasks, humans must retain ultimate ownership and accountability over decisions—especially in high-stakes or regulated domains like financial advising.
Discussing her book (Manage the Machine: How to Harness Human-AI Collaboration at Work), Paula argues that the highest-performing organizations manage the human side of AI as intentionally as the technical side. Developing human judgment, taste-making, and curiosity is the real key to unlocking AI's full value.
"The companies that are getting the best results from AI are obviously managing the technical side of it, but they're also managing the human side of it. They're seeing it as a human management challenge... it's not just a nice-to-have. It's actually how you get the right results from AI." — Paula Goldman
How to Contact Paula
LinkedIn: Connect with Paula Goldman directly on LinkedIn, where she actively engages with professionals, shares insights on responsible AI, and discusses best practices from her book Manage the Machine.
Salesforce: Reach out through official Salesforce corporate channels for inquiries regarding her work as Chief Ethical and Humane Use Officer.
Check the episode's Transcript (AI-generated) HERE.
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