A network approach to AI transformation
A network-centric view of organizations can drive AI adoption faster, more organically and sustainably
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In the era of artificial intelligence (AI), leading transformations has become more critical than ever. AI technologies have the potential to revolutionize industries by automating processes, uncovering deep insights from vast amounts of data and fostering the creation of innovative products and services.
To harness the full potential of AI, businesses must undergo comprehensive transformations, adapting their operations, organizational structures and collaborative approaches. This involves integrating AI-driven tools, upskilling the workforce and cultivating a culture of speed.
While a few organizations have quickly capitalized on these opportunities, most have been scrambling to catch up, pouring substantial investments into AI advancements. Today, organizations face the challenge of rapidly adopting AI to maintain competitive advantage and market relevance. Moving too slowly can result in missed opportunities and being outpaced by more progressive competitors, leading to loss of market share and potential obsolescence.
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The network perspective
Consider a division of a large technology company renowned for its unwavering commitment to privacy and security. With 315 employees spanning various functions – product development, global sales, customer experience, professional services, contract management, legal, security and compliance – the firm prioritizes rigorous compliance standards and focuses on privacy management.
The firm has been notably slow to market with new AI innovations, having not introduced any major AI advancements in over a year. In contrast, its primary competitor seems to release new solutions monthly. These delayed launches highlight a significant collaboration challenge that is operating as a drag on the organization. The network diagram (Figure 1) representing the organization’s structure reveals a high degree of interconnectedness among 22 different teams, reflecting a collaborative environment where cross-functional engagement is highly valued. However, this level of interconnectedness often leads to frequent delays in decision-making, creating a collaborative drag on the organizational AI solutions.
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Despite leveraging Agile methodologies to respond to changing requirements and deliver value rapidly, the product development team (depicted by the red nodes on the left) operates at a relatively slow pace. The emphasis on cross-team connections, while beneficial for risk mitigation, hampers the effectiveness of agile methods. Scrutiny from other product development teams, as well as legal, compliance and security, frequently stifles advancements prematurely. High degrees of interconnectedness prioritize organizational alignment over speed, which impedes the firm’s ability to bring AI innovations to market.
Figure1: Diagram of a highly interconnected technology firm. Source Michael Arena
This cross-functional connectivity is evident in operational functions like professional services, contract management, legal, security and compliance (dark blue, light blue, olive, dark green, purple and brown nodes) on the right of the diagram. These interactions typify a compliance-driven organization focused on risk mitigation and alignment. However, this connectivity is problematic in the product development teams (red nodes), consisting of software engineers, AI specialists and machine learning experts. Despite some separation from operational functions, the red nodes are highly intermingled, reflecting the same caution and resulting in slow development and operational progress.
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Our research has shown that concentrated teams that prioritize internal interactions, 75 percent or more of their collaborative efforts are highly correlated with speed to market. By minimizing external team dependencies and reducing distractions, these groups operate with heightened levels of concentration. Their emphasis on internal cohesion and intense focus fosters accountability, enabling rapid decision-making and execution.
Benefits of a network approach
A network approach provides a new lens for understanding team interactions by focusing on the informal connections and relationships within an organization. It offers a fresh perspective to significantly accelerate the development and scaling of AI solutions. Here are some key ways a network approach drives greater velocity and scaling of AI solutions.
- Enhanced collaboration: A network approach emphasizes informal connections and relationships across teams and departments. By fostering collaboration beyond formal structures, it encourages knowledge sharing, cross-pollination of ideas and collective problem-solving, which are crucial for developing innovative AI solutions.
- Efficient information flow: Informal networks often facilitate faster and more effective communication compared to rigid hierarchical structures. This enables timely sharing of insights, data and updates related to AI initiatives, thereby speeding up decision-making processes and reducing bottlenecks.
- Adaptability and flexibility: Networks are inherently adaptable to change and can evolve quickly to respond to new challenges and opportunities. This agility is essential in the fast-paced AI landscape, where technologies and market demands can shift rapidly.
- Identifying key influencers: Network analysis helps identify key influencers and opinion leaders within the organization. These individuals can play a crucial role in championing AI initiatives, driving adoption and overcoming resistance to change.
- Optimized resource allocation: By understanding the informal networks within the organization, leaders can better allocate resources such as talent, funding and technology to AI projects where they can have the most significant impact. This strategic allocation improves efficiency and maximizes the return on investment in AI.
- Improved decision-making: Insights gained from network analysis can inform more informed decision-making processes. By understanding how information and decisions flow through the organization, leaders can make decisions that are more aligned with the business’ strategic goals and objectives.
By taking a network-centric view of the organization, leaders can drive AI adoption in a way that is faster, more organic and ultimately more sustainable than traditional top-down approaches. This allows for greater velocity in implementing AI solutions and enables scaling across the organization more effectively.
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