Sarah Philipp, Global Lead of AI Enablement & Adoption at Altimetrik – Interview Series

Sarah Philipp, Global Lead of AI Enablement and Adoption at Altimetrik, focuses on helping organizations bridge the gap between rapid AI innovation and real-world workforce adoption. Her work centers on building AI literacy programs, workflow enablement strategies, and role-based training initiatives that help employees and managers integrate generative AI into everyday operations. At Altimetrik, she has helped shape enterprise adoption programs around technologies such as ChatGPT Enterprise, the OpenAI Agents SDK, and enterprise-scale AI workflows, while also co-creating “LAByrinth,” a project-based GenAI simulation platform designed for engineers and technical program managers. Prior to joining Altimetrik, she spent years leading customer education and implementation initiatives at SaaS platforms such as Nudge Coach, giving her a strong foundation in learning systems, behavioral enablement, and digital transformation. Her career reflects a growing trend within enterprise AI where success increasingly depends not just on deploying models, but on making employees confident, capable, and productive with AI tools in their daily work.

Altimetrik is an AI-first digital engineering and data solutions company focused on helping enterprises modernize legacy systems and scale generative AI across business operations. The company works across industries including financial services, healthcare, manufacturing, retail, and life sciences, combining product engineering, cloud modernization, data infrastructure, and AI deployment services. Altimetrik has positioned itself as a major enterprise AI implementation partner through collaborations with OpenAI, with a strong emphasis on agentic AI, workflow orchestration, governance, and production-ready AI systems. Its ALTI AI Lab serves as an internal innovation hub where the company prototypes enterprise AI applications ranging from intelligent assistants to multimodal workflows and AI-native business operations. In recent years, Altimetrik has expanded aggressively through partnerships and acquisitions, strengthening its global engineering footprint and enterprise AI delivery capabilities.

You’ve built your career at the intersection of enterprise transformation, learning design, and emerging technology — what pivotal experience first convinced you that responsible AI adoption would hinge as much on human enablement as on technical capability?

I usually end up as a pretty late adopter of things. I tend to be skeptical of hype and stubborn about sticking to the way I’m used to doing things. This was true with AI. I was a late adopter, and moved very slowly in the beginning.

What changed for me hearing, in very human and non-technical language, how other people were actually using AI in their day-to-day lives and work. Once I could connect it to real and personal friction points, curiosity just followed.

That experience reinforced something I have seen across every transformation effort I have been part of: adoption rarely starts with capability alone. It begins with relevance, trust, and meaning. People need to first see themselves in the change and is why responsible AI adoption depends just as much on human-led enablement as it does on technical readiness.

In your role leading global AI enablement at Altimetrik, how do you define what successful enterprise-scale generative AI adoption looks like beyond pilots and proof-of-concept experiments?

Successful enterprise-scale adoption does not live in silos. One of the most common patterns we see is compartmentalized adoption. One team becomes very advanced in its AI maturity, while another team in the same organization has no clear strategy yet and may even be using fragmented tools or personal accounts. That kind of uneven adoption creates inconsistency, and risk.

True success comes when an organization is building shared habits, shared standards, and connected ways of working. It requires both top-down and bottom-up change management. Top-down, leadership has to set direction, establish governance, and create clarity around what responsible, valuable adoption looks like. Bottom-up, employees need to understand how AI fits into their day-to-day work, where it solves real problems, and how to use it confidently within their role.

The real sign of maturity is when workflows run smoothly end-to-end and across teams, not just within isolated pockets.

Many organizations invest in generative AI tools but struggle to operationalize them. What are the most common execution gaps you see between ambition and measurable business impact?

Governance ownership is often unclear. Who is responsible for documenting what is being used, versioning prompts or workflows, monitoring quality, and keeping track of where AI is actually showing up in the business? Without that clarity, organizations end up with scattered usage and very little operational discipline.

Many organizations have not decided what kind of AI usage they actually want to encourage. Are they aiming for role-specific prompting habits, individual productivity use cases like brainstorming and drafting, or purpose-built agents tied to defined workflows? If that is not clear, enablement stays vague and impact becomes hard to measure.

There is also often a disconnect between rollout and real work. Companies invest in tools before they have identified the moments where AI can remove friction, improve quality, or speed up execution in a meaningful way. Ambition is easy, but operationalization requires specificity.

You work closely with executive leadership teams — what does effective AI fluency look like at the C-suite level, and how does leadership behavior influence adoption across the organization?

It looks like modeling, curiosity, and a beginner’s mindset. It looks like leaders being vulnerable in a room with folks who report to them.

The leaders who make the biggest difference are the ones who demonstrate a beginner’s mindset. They are willing to learn publicly. They are comfortable saying, “I am still figuring this out too.” That kind of vulnerability matters more than people realize, because it gives everyone else permission to engage without feeling like they have to be experts first.

Leadership behavior sets the tone. If executives treat AI like a side experiment, the organization will too. If they model thoughtful use, ask practical questions, and visibly invest in learning, adoption becomes culturally safer. Confidence is just as important as capability when it comes to scaling AI in the enterprise.

What does a structured experimentation framework for generative AI look like in practice, and how can companies encourage innovation without creating fragmentation or risk exposure?

We want to start with real workflow pain points, not random suggestions. Teams should be encouraged to identify repetitive tasks, decision bottlenecks, or existing knowledge gaps. From there, organizations can prioritize experiments based on business relevance, feasibility, and risk.

The healthiest environments are the ones where people can try things safely, share what works, and avoid reinventing the wheel in isolation.

You co-created LAByrinth, a generative AI project simulator designed to give engineers hands-on experience building with AI tools in a safe, guided environment. What inspired its creation, and how does simulation-based learning accelerate real-world AI capability?

I’ve always loved forms of hands-on, experiential learning. Specifically, the kind of “over your shoulder” mentoring that exists in art studio environments.

When Martin Gaida and I started bouncing around ideas for this kind of training structure, the whole project ended up being a lot of fun to create. It felt like we were creating a puzzle for folks to solve. That’s how I knew we were on the right track. People are more likely to engage with new material when they’re finding it interesting and feel supported.

Simulation-based learning accelerates capability because it closes the gap between theory and application. It gives them a safe place to practice judgment, not just memorize concepts. And with AI, that matters enormously, because real capability is not just knowing what a tool can do. It is knowing how to use it thoughtfully and safely.

How should enterprises approach workforce upskilling differently in the era of generative AI compared to previous waves of digital transformation?

Collaboratively and creatively. With tools like Notebook LM and Figma make, there’s so many ways to make engaging and fresh-feeling content. We love a slide deck, but we also love making interactive workbooks, animated videos, and other methods of delivering the curriculum.

Personally, I learn best with infographics and other visuals.

Responsible AI is often framed around governance and compliance. From an enablement perspective, what does responsible adoption look like in day-to-day workflows?

Consult with the folks actually using the tools on what makes them useful, then make sure that everyone using the tools knows what the policy actually means. How does it translate into how they’re using AI day-to-day?

Responsible adoption means a human-at-the-helm model. AI can help draft, summarize, analyze, or accelerate, but people still own the judgment, the quality check, and the final decision. That human responsibility is key.

Just as importantly, responsible adoption depends on confidence. Discernment comes from experience. That is why organizations need low-risk, non-judgmental spaces where employees can practice and build real fluency. When people gain enough hands-on experience to make thoughtful judgment calls, responsible use stops being abstract and starts becoming habitual.

As generative AI evolves rapidly, how can organizations design enablement programs that remain adaptable rather than becoming obsolete within months?

It’s a challenge, but there are a lot of great tools out there.

The biggest mistake is building enablement entirely around the current tool landscape.

We are often asked whether we offer tool-agnostic training, and the answer is yes. Absolutely. We focus on durable AI skills and then teach people how to apply those skills within whatever tools the organization is using today. That approach gives programs a much longer shelf life.

Adaptable enablement also has to be modular. Fractal. Instead of treating training as a one-time event, organizations should think in layers: foundational literacy, role-based application, advanced experimentation, and ongoing updates as tools and priorities shift.

Looking ahead, what shifts do you believe will define the next phase of enterprise AI adoption over the next two years?

Role based and agentic. Enterprises are already embracing using agents. These organizations want to make sure their technical teams are upskilled in all of the necessary ways to build and govern this kind of AI use.

Thank you for the great interview, readers who wish to learn more should visit Altimetrik.