Unleashing the Power of Female Leadership in Generative AI: "Building Cross-Functional Teams for Successful GenAI Integration"
- Susan Mcleod
- Apr 1
- 6 min read
GenAI Is Not Just a Tool: It's a New Operating System for Enterprise Value!!
Guest: Michelle Ortega
Head of Delivery and Transformation, Envira Global, Ltd.
By 2030, over 70% of enterprise value creation will be shaped by AI-enhanced decision-making. Yet, despite soaring investments, most organizations are not structured to unlock GenAI’s full potential. The reason? GenAI is not just a technology deployment—it is an organizational redesign mandate. Success depends not on code but on collaboration—cross-functional, cross-disciplinary, and cross-silo.
Organizations that lead the GenAI curve build high-performing, multidisciplinary teams that not only execute but also orchestrate AI transformation. These teams are the connective tissue between strategy, ethics, operations, and innovation. Having led enterprise transformations at the intersection of AI, strategy, and operational excellence, I have seen firsthand that the most significant barrier to AI scale isn’t technical—it’s organizational.
This blog outlines a next-generation blueprint for building cross-functional teams that enable GenAI integration at scale—with foresight, integrity, and enterprise agility.
GenAI is not an isolated technology project—it is an enterprise-wide paradigm shift that impacts every facet of business, from customer engagement and supply chains to compliance and workforce productivity. The reality is that AI adoption often fails due to siloed execution, unclear leadership roles, and inadequate cross-functional collaboration.
High-performing AI-driven organizations structure their AI operating model around cross-functional teams that:
1. Ensure strategic alignment – AI initiatives must support broader corporate objectives.
2. Mitigate ethical and regulatory risks—Legal, compliance, and cybersecurity teams must collaborate with AI engineers.
3. Bridge technical and business priorities—Data scientists and process experts must collaborate with executive sponsors to create AI use cases.
4. Drive enterprise-wide adoption – Human capital leaders must manage cultural and workforce transitions to AI-driven workflows.
The highest-performing companies recognize that AI transformation is a leadership challenge as much as a technological one. Success is not simply about deploying AI models—it’s about creating the right ecosystem for AI to drive sustained value.
Key Roles in a Cross-Functional AI Team
Organizations must assemble teams with expertise across multiple disciplines to build AI-powered enterprises. The following roles are essential:
1. AI & Data Science Experts:
Role: Develop and optimize AI models, ensuring data quality and model accuracy.
Contribution: Architecting AI frameworks tailored to the organization's needs.
Leadership Lens: AI leaders must educate business executives on what AI can and cannot do, fostering realistic expectations.
2. Business Strategy & Transformation Leaders
Role: Ensure AI initiatives align with enterprise goals and business value creation.
Contribution: Establishing AI-driven business cases, defining ROI metrics, and leading transformation initiatives.
Leadership Lens: AI must be embedded into corporate decision-making frameworks, ensuring C-suite sponsorship and accountability.
3. Operations & Process Experts
Role: Integrate AI into existing workflows, optimizing efficiency and automation.
Contribution: Identifying high-value AI use cases and reengineering processes for intelligent automation.
Leadership Lens: Avoid disconnect between AI capabilities and business realities by embedding process owners into model lifecycle.
4. Compliance, Legal & Ethics Leaders
Role: Govern AI risk, ensuring compliance with global regulatory requirements and ethical principles.
Contribution: Proactively addressing bias, fairness, and transparency in AI decision-making.
Leadership Lens: Elevate AI ethics to the boardroom, where algorithmic trust becomes an asset.
5. IT & Infrastructure Leaders
Role: Manage enterprise AI architecture, including cloud, data pipelines, and cybersecurity.
Contribution: Ensure scalability, security, and reliability of AI platforms.
Leadership Lens: Modernize core systems to eliminate legacy friction points in AI deployment.
6. Change Management & Workforce Enablement Leads
Role: Guide employees through AI adoption, reskilling, and cultural transition.
Contribution: Foster an AI-literate, agile workforce.
Leadership Lens: Position AI as augmentation, not alienation, build trust before automation.
Best Practices for High-Performing AI Teams
To ensure cross-functional teams drive meaningful AI adoption, executives must implement structured governance and agile collaboration frameworks:
1. Establish AI Governance & Ownership
Define clear leadership structures—appoint a Chief AI Officer or Transformation Office.
Implement an AI Governance Board for ethics, risk, and compliance oversight.
Tie AI investments to enterprise innovation and P&L objectives.
2. Leverage Agile for AI
Apply Scrum, SAFe, or DevOps to AI execution—not just IT projects.
Use cross-functional sprints and product owners to align model development with business outcomes.
Pilot before scaling—treat each AI deployment as a learning loop, not a final product.
3. Build Talent Pipelines for AI Fluency
Launch company-wide AI literacy initiatives.
Incentivize collaboration across technical and non-technical functions.
Elevate AI skills to strategic capabilities, not just operational ones.
4. Design for Scalable Adoption
Frame AI as a value enabler, not a workforce threat.
Embed AI KPIs into team, department, and executive performance metrics.
Use storytelling and vision casting to align hearts before hands.
Case Studies: AI-Enabled Cross-Functional Collaboration
Case Study 1: Financial Services – AI-Powered Client Interaction at Scale
In 2025, NatWest Bank became the first UK bank to partner with OpenAI, embedding generative AI into customer service and financial advisory workflows. The initiative brought together compliance officers, legal teams, technologists, and front-line support leaders in a tightly coordinated governance structure to ensure responsible deployment.
The results were immediate and measurable: a 150% increase in customer satisfaction and a significant reduction in dependency on human advisors, allowing reallocation of talent to higher-value functions.² This case underscores how GenAI success is driven not just by the technology itself, but by cross-functional alignment around ethics, trust, and customer impact.
Source: Reuters, March 2025
Case Study 2: Manufacturing – Accelerated Operations & Customer Happiness at Scale
Dubai Electricity and Water Authority (DEWA), in partnership with Microsoft, implemented generative AI tools across multiple operational and customer-facing processes. The initiative brought together operations leaders, IT, public service, and communications teams to co-create new workflows around Microsoft Copilot technologies.
The result was that task completion times dropped from days to hours, and DEWA achieved a 98% customer happiness rate.³ This transformation was made possible not only by technical capability but by unified leadership and agile integration between AI engineers and operational subject-matter experts.
Source: Microsoft Official Blog, March 2025
Case Study 3: Retail – GenAI-Driven Voice Automation in Fast Food
Yum Brands—parent company of Taco Bell, Pizza Hut, and KFC—entered a strategic partnership with Nvidia in 2025 to roll out AI-powered voice assistants across its drive-thru operations. A dedicated task force comprised of product development, franchise operations, data science, and legal teams oversaw the design and deployment process.
Initial tests showed significant gains in order speed and accuracy, prompting Yum to expand its AI system to over 500 drive-thru locations by Q2 2025.⁴ This illustrates how cross-functional execution can translate AI potential into scalable, revenue-generating outcomes in highly dynamic environments.
Source: New York Post, March 2025
Synthesis: From Execution to Enterprise Intelligence
Across industries, one truth remains clear: GenAI success is less about the brilliance of algorithms and more about the intelligence of collaboration. The organizations delivering measurable results are not those with the best tech, but those with the most coordinated teams. These cross-functional ecosystems become force multipliers—turning individual contributions into enterprise-wide learning, agility, and value creation.
Unlocking Next-Level Value: From Functional Integration to Cognitive Synergy
The future of GenAI lies not merely in integration across functions but in achieving cognitive alignment—where human expertise and machine intelligence co-evolve within organizational workflows. High-performing enterprises design dynamic “intelligence pods” that learn, adapt, and act in real time, turning every AI interaction into a strategic feedback loop.
Elevating Decision Intelligence as the Strategic Core
Cross-functional teams are now stewards of decision intelligence. By embedding AI into the very fabric of enterprise decisions—from forecasting to risk modeling—they shift from asking “What can AI do?” to “How can AI enable superior leadership judgment?” Governance, trust, and velocity become the new success metrics.
Redefining Leadership in the Age of Generative AI
GenAI demands leaders who are both visionary and grounded—able to steward ethics, drive transformation, and scale value responsibly. Executives must move from AI literacy to AI fluency, championing systems that protect dignity while unleashing potential. In this future, cross-functional teams are the engines—and executive courage is the fuel.
Conclusion: The Executive Imperative for AI Leadership
AI transformation is not about technology, it’s about people, leadership, and execution. Enterprises that invest in cross-functional AI teams, structured governance, and workforce readiness will emerge as industry leaders in the AI revolution.
Key Takeaways for Executives:
AI success is a business challenge, not just a technical one.
Strategic alignment and governance frameworks drive AI adoption.
Cross-functional collaboration enables AI-powered business transformation.
AI literacy, workforce readiness, and ethical governance are non-negotiable.
The companies that build AI into their DNA—fostering leadership alignment, operational agility, and responsible innovation—will thrive in the next era of business.
Final Thought: “Without counsel, plans fail, but with many advisers, they succeed.” (Proverbs 15:22, ESV)
AI-driven success is built not on isolated innovation, but on the collective intelligence of high-performing, cross-functional teams.
Executives who harness AI-powered collaboration at scale will define the future of enterprise leadership.
Author’s Note on Source Integrity
These case studies are based on real-world enterprise deployments and reflect the most recent, verifiable data available as of Q1 2025. Metrics and outcomes are drawn from publicly available reports and global news sources.
Source Appendix
McKinsey & Company (2024). The State of AI in 2024: Scaling with Purpose.
Reuters (March 2025). NatWest Seals Milestone UK Banking Collaboration with OpenAI.
Microsoft (March 2025). How Real-World Businesses Are Transforming with AI.
New York Post (March 2025). Taco Bell, KFC, and Pizza Hut Parent to Integrate AI into Drive-Thrus.
Love to see common goals/themes in leadership!