Managing Director of Data & AI
As organizations navigate the AI revolution, we see three distinct AI adoption archetypes emerging. Each represents a different approach to leveraging AI, whether as a growth engine, an enterprise-wide transformation enabler, or a deep functional enhancement.
These companies place AI front & center in their business or operating model, using it as a core differentiator/growth driver. AI isn’t just a supporting tool, it’s a core product or a revenue enabler.
AI-driven personalized playlists and audio discovery models directly impact engagement and subscription revenue.
AI-powered autonomous driving as a central value proposition.
AI-based GPT models offered as a core product.
AI-powered content recommendation drives customer retention and revenue.
Organizations in this category view AI as a cross-functional enabler, embedding it across all business units and operations to drive efficiency, resilience, and decision-making.
AI-driven supply chain optimization, inventory management, and dynamic pricing.
AI integrated across logistics, personalization, fraud detection, fulfillment centers.
AI-powered predictive maintenance, drilling optimizations, energy efficiency initiatives.
AI adoption across marketing, R&D, and sustainable sourcing.
Rather than applying AI broadly, these companies go deep, embedding AI into one key function or business area where it can drive maximum impact.
AI-powered customer service automation, using AI chatbots and predictive insights to enhance user experience.
AI-driven precision agriculture, optimizing crop yields with machine learning.
AI-powered loyalty program personalization and real-time menu adjustments.
AI in customer support and flight disruption management.
As AI adoption accelerates, organizations face a critical choice: How will AI shape their future business model and operations? The three AI archetypes – Outward, Holistic, and Deep – provide a strategic lens to help companies clarify their AI ambitions, align investments, and make informed decisions about where and how AI should drive value.
Understanding these AI archetypes helps companies answer key strategic questions:
By defining an AI archetype early, organizations can ensure that AI adoption is purpose-driven, not just exploratory.
Without a clear AI strategy, companies risk spreading resources too thin or investing in AI without clear ROI expectations.
Each archetype requires different organizational capabilities, governance structures, and AI talent strategies:
Without a structured AI operating model, even the best AI strategies may fail to scale.
AI is becoming a key differentiator in nearly every industry.
A misaligned AI strategy could mean falling behind industry leaders who use AI more effectively.
Some companies start with Deep AI and later evolve into a Holistic AI approach.
Others start with Outward AI innovation, then expand AI across internal functions.
Having a clear AI archetype helps organizations plan for future AI expansion, ensuring that today’s investments align with long-term AI maturity.
Laura Stevens, PhD, is the Managing Director of Data & AI, bringing a unique blend of strategic vision, analytical expertise, and leadership acumen. With a background in neuropsychology, business consulting and organizational transformation, she has successfully navigated a career spanning academia, consulting, and industry leadership. As a former VP Data & AI in an international organization, Laura has led large-scale Data & AI teams covering data science, machine learning, data engineering, data governance, and visualization. She is passionate about leading organizations through their data & AI transformation.
