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Artificial intelligence is no longer a future conversation for automotive. It is already shaping how teams analyse data, build presentations, monitor competitors, and structure decisions. The question for leaders is no longer whether AI will matter, but whether they will use it intentionally.
In February’s Women Automotive Network virtual event, Beyond the Buzz: AI for Automotive Leaders, senior professionals joined from across the ecosystem to explore how generative AI can move from experimentation to practical leverage.
Hosted by Stephanie May, the session featured Irina Grak, Sales Operations at Genesis, who shared hands-on examples from her own role in incentives and competitive analysis. Importantly, Irina is not an engineer or data scientist. Her perspective reflected the reality of many leaders in the WAN community: business-side professionals navigating rapid technological change while balancing commercial accountability.
Live polls during the session revealed a mixed maturity curve. Some attendees are experimenting weekly. Others are early in their journey. What was consistent, however, was curiosity.
The message was clear: automotive professionals are not resistant to AI. They are looking for clarity on where it fits, how to apply it responsibly, and how to avoid being left behind.
Irina framed it directly:
“You don’t have to wait for permission. Start small. Find one task this week and test it.”
For many leaders, the barrier is not access to tools. It is knowing where to begin.
Automotive has already absorbed electrification, software-defined vehicles, and autonomous systems. Generative AI now sits on top of this transformation.
But as Irina emphasised, the real shift is operational:
These are not future scenarios. They are today’s workflows.
AI does not remove leadership responsibility. It removes friction from execution.
For senior leaders, this is leverage.
One of the most practical frameworks shared was a simple filter for delegation. Before testing AI on a task, ask:
If the answer is yes to all three, it is a strong candidate for automation.
This is where AI moves from novelty to productivity.
Irina shared a live example from her team: a monthly reconciliation process previously taking four hours manually. By refining prompts in Microsoft Copilot, the team reduced heavy-lift comparison work significantly, freeing hours each month across multiple teams.
The session resonated strongly around the idea of AI as a “great equaliser.”
Automotive remains a technically dense industry. Institutional knowledge can feel inaccessible. Generative AI, when used well, can break down complex concepts into plain language and accelerate learning.
Irina framed this particularly in the context of career progression and maternity return:
AI cannot replace experience. But it can compress learning curves and rebuild confidence faster.
For a global community of women across all career stages, this matters.
Navigating new technology demands requires leadership alignment.
If this AI masterclass highlighted the shift in capability expectations, the Detroit Summit continues the conversation. Leaders from Toyota, GM and Mitsubishi Motors will explore how to build and lead a future-ready workforce in a fast-evolving automotive landscape.
A recurring theme was that AI performance reflects input quality.
Irina outlined practical guidance for stronger prompting:
AI is not a search engine. It is a dialogue.
Leaders who treat it as a thinking partner, not a shortcut, will extract greater value.
The most advanced demo of the session showcased Claude’s “skills” functionality: reusable structured instructions that enable AI agents to execute complex, multi-step tasks.
Irina demonstrated how her team collects publicly available competitor incentive data across hundreds of combinations monthly. What previously took eight hours manually was reduced to approximately 25 minutes using structured skills and browser integration.
The takeaway for leaders was not tool-specific. It was strategic:
Invest once in structure. Reuse repeatedly.
The session did not ignore risk.
Clear guardrails were outlined:
Irina’s analogy resonated:
“AI can build the kitchen. Your secret recipes stay in your drawer.”
Responsible adoption is leadership maturity.
The closing emphasis was not technical. It was cultural.
Whether through WAN, internal lunch-and-learns, or peer groups, shared experimentation accelerates confidence.
Several attendees noted that internal prompt-sharing sessions within teams have significantly reduced individual hesitation.
For organisations, this is an opportunity. AI capability scales faster when it is normalised socially.
If you attended live or are watching on demand, here are four practical actions to move beyond buzz:
If you would like to revisit the demonstrations or explore the practical frameworks in detail, you can access the full masterclass recording below.
If you found value in this session and want to keep learning alongside the WAN community, our virtual program continues throughout the year with monthly sessions designed to give you fresh perspective and practical insight.
Our next event takes place on Thursday 6th March, a special celebration for International Women's Day discussing how mentorship can accelerate your career progression
With a 50,000+ global community across 139 countries worldwide, and flagship summits in Europe, the US, Mexico & Japan, the Women Automotive Network (WAN) is a trusted global platform for organisations across the automotive and mobility ecosystem.
We partner with leading OEMs, Tier 1–2 suppliers, and technology companies to support leadership development, employer brand visibility, and meaningful engagement with senior industry talent.
For all enquiries, get in touch here.
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