Why a New Type of Cross-Functional Teams are Essential for AI-Driven Success

As Artificial Intelligence (AI) becomes a cornerstone of modern business, organisations face new challenges in unlocking its full potential. Traditional, tech-led approaches to AI can no longer deliver the competitive edge needed to thrive. To leverage AI’s power effectively, businesses need cross-functional teams that blend technical expertise, departmental insights, and data fluency. Here’s why assembling the right team is essential for meaningful AI impact and how organisations can build these teams effectively.

The Evolution of AI in Business: From Standalone Models to Integrated Systems

Initially, organisations approached AI as standalone projects managed within single departments—typically in isolated, experimental initiatives like chatbots or data analytics. While these standalone models delivered some value, they rarely connected meaningfully to wider business goals. Today, AI’s real potential lies in its ability to integrate across departments, driving decision-making, optimising operations, and enhancing customer engagement.

The Importance of Cross-Functional Teams: When AI is implemented in silos, it lacks the business context needed to make a strategic impact. Integrated, cross-functional AI teams ensure that AI projects are aligned with real business needs, helping to embed AI into core processes and deliver holistic solutions. These teams can connect technical capabilities with departmental goals, creating a seamless flow of insights that amplify AI’s effectiveness.

Why Traditional IT Departments Alone Can’t Lead AI Projects

Historically, many organisations relied on their IT departments to spearhead AI projects, assuming technical skills were enough. But an IT-only approach often falls short because IT lacks the domain-specific knowledge required for meaningful AI application. Effective AI demands an understanding of customer needs, marketing strategies, operational bottlenecks, and more. Each of these areas brings unique insights and requirements that influence how AI should be structured and deployed.

The Role of Interdisciplinary Collaboration: For AI to drive real value, it needs a collaborative team that blends IT with department-specific knowledge. Marketing teams, for instance, may understand customer sentiment and sales drivers but lack the technical skills to manage data architecture. Conversely, IT may know how to handle data but not how to apply it for customer segmentation or predictive marketing. This interdisciplinary approach ensures that technical and business perspectives work in tandem, allowing AI to make a meaningful business impact.

The Growing Importance of Predictive AI in Competitive Advantage

While Generative AI (GenAI) has gained popularity for its accessibility, predictive AI often offers a greater competitive edge by allowing organisations to anticipate trends, forecast demand, and make strategic decisions. Predictive AI relies on vast amounts of structured and unstructured data—customer behaviour, purchase history, seasonal trends—which makes data management central to its effectiveness.

Bridging IT and Departmental Knowledge: Predictive AI is complex, and its potential often goes unrealised because it requires significant data management—traditionally an IT function. Marketing and sales departments may not have the technical know-how to access or structure this data, while IT might not grasp its strategic value for customer targeting or inventory optimisation. A cross-functional team that includes both data architects and departmental strategists bridges this gap, ensuring that data storage, access, and usage support long-term business goals.

The Role of Data in AI Success: Moving from Collection to Connectivity

Data is the backbone of any AI initiative, but to make AI truly successful, data needs to be more than just collected—it must be connected, accessible, and used strategically. Organisations often view data in terms of security and compliance, especially for AI. However, how data is stored, accessed, leveraged for different use cases, and monetised is a fundamental part of AI’s value.

AI-Driven RevOps and BizOps: The Next Phase of Operational Efficiency

To align operations with AI potential, many organisations have adopted Revenue Operations (RevOps) and Business Operations (BizOps) frameworks. These frameworks unite revenue-generating and operational teams around shared goals, breaking down silos and fostering better communication.

• RevOps: Aligns sales, marketing, and customer success around consistent metrics and a shared view of the customer journey.
• BizOps: Centralises operational functions, from IT to finance, under a strategic framework.

How AI Transforms RevOps and BizOps: While these frameworks improve coordination, they still require the power of AI to become truly data-driven. AI enhances RevOps by identifying customer trends or cross-sell opportunities through predictive analytics, while in BizOps, AI automates processes, improves forecasting, and cuts costs. However, RevOps and BizOps teams can only access AI’s full potential with cross-functional expertise—IT provides technical capability, marketing or sales teams provide insight into customer needs, and data specialists enable robust, usable insights.

Building the Ideal Cross-Functional Team for AI Deployment

Given these trends, the ideal AI team structure must encompass a mix of technical, operational, and strategic expertise. By combining these skills, AI projects are more likely to achieve meaningful outcomes aligned with organisational goals.

Core Roles

1. AI Expert (In-House or Consultant): Guides AI methodology, selects suitable models, and ensures alignment with best practices.
2. Departmental Leader and Sponsor (e.g., Chief Marketing Officer): Represents department-specific needs, ensuring AI aligns with practical demands and strategic objectives.
3. Technologists:Data Storage and Access Specialist: Manages data architecture and accessibility, crucial for advanced AI applications, especially predictive models
4. Cloud Solutions Expert (e.g., Azure Specialist): Builds scalable, cloud-based solutions, ensuring data security and integration with other systems.
5. System Engineer: Develops and maintains AI tools, ensuring they function optimally within the organisation’s infrastructure.
6. Use Case Expert: Understands the specific workflow or process targeted by AI, bridging technical capabilities with practical applications.

Additional Roles for Comprehensive AI Deployment

1. Data Governance and Compliance Specialist: Ensures AI initiatives comply with data regulations and ethical standards, balancing innovation with security.
2. Change Management Lead: Manages adoption, training, and smooth integration into existing workflows, ensuring AI tools are embraced and used effectively.
3. Customer Experience (CX) or UX Specialist: Designs user-friendly interfaces, making AI tools accessible and intuitive for non-technical users.
4. Ethics or Responsible AI Advisor: Focuses on issues such as bias, transparency, and responsible AI practices, essential for trust and fairness.
5. Performance and Optimisation Engineer: Monitors AI performance, optimising for accuracy, efficiency, and resource use.
6. Project Manager with AI Specialisation: Manages timelines, resources, and cross-functional coordination, aligning AI projects with broader business goals.

How Multi-Agent Systems Will Reshape AI-Driven Teams in the Future

Looking to the future, multi-agent systems—networks of interconnected AI agents—are poised to redefine how AI teams operate. These systems require deeper collaboration between roles as agents work together to tackle complex tasks.

Preparing for New Skills and Roles: As multi-agent AI evolves, new roles may emerge, such as "Agent Orchestrator" or "Memory Optimisation Specialist," dedicated to managing the interaction and efficiency of interconnected agents. Organisations that invest in these skills will be well-prepared for the next generation of AI applications, where collaborative systems redefine both AI potential and team structures.

Conclusion: Embracing a Collaborative Approach for AI Success

As AI grows more sophisticated, organisations that build robust cross-functional AI teams will gain a significant competitive edge. These teams blend AI expertise, technical capabilities, and domain-specific insights, creating intelligent, user-centric solutions that are both scalable and ethical.

The future of AI lies in multi-agent systems, predictive capabilities, and data ecosystems that extend beyond basic automation. Organisations that embrace a cross-functional, collaborative approach now will be well-prepared to lead in this AI-driven era, positioning themselves as innovators at the forefront of transformative change.

The old technology deployment models are unfit for purpose in the AI enabled organisation.