CIONET Trailblazer: Unlocking Value: Strategic AI Investments for Business Success

Published by Charlotte Coen
May 29, 2024 @ 9:13 AM

In this edition of the CIONET Trailblazer, we sit down with Pierre Marchand, Chief Data Strategist at Cognizant, to discuss the strategic considerations behind AI investments. Pierre provides valuable insights into how organisations can identify AI projects and use cases that offer the greatest value. We explore the importance of aligning business and IT strategies, leveraging data products, and fostering collaboration to enhance AI's impact. With practical advice and proven strategies, Pierre offers a comprehensive guide to achieving successful AI adoption and maximising value creation.

How do organisations determine which AI projects or use cases are expected to deliver the greatest value, and thus, merit investment?

Determining which AI projects or use cases are expected to deliver the greatest value and thus merit investment is a multifaceted process. Organisations are often challenged to do more with the same resources, necessitating a bridge between strategic business objectives and IT and AI initiatives. This is where Objectives and Key Results (OKRs) come into play. They serve as a tool to align business and IT strategies and ensure that investments deliver the desired outcomes.

Data products play a significant role in this process. They should be well-defined, organised in a common marketplace, and reused to increase speed and reduce costs. The Value of Investments (VOI) framework can assist in prioritising use cases and data products based on their contribution to OKRs while considering multiple value drivers.

Collaboration is another crucial aspect of this process. The synergy between business, IT, and data stakeholders is vital for successful AI adoption and value creation. AI and generative AI have the potential to enhance employee productivity and process efficiency and enable new business opportunities.

However, the success of these initiatives is contingent on several foundational elements. Data quality, compliance, governance, architecture, and a data-centric culture are essential foundations for an AI-enabled organisation.

Cognizant-Pierre-MarchandFinally, the “Game of Value” is a strategic approach that involves value poker, investment estimation, and priority award, to collectively prioritise backlog items. This game plays a significant role in addressing concerns and fostering a positive attitude towards AI adoption. Thus, it’s a comprehensive process that requires strategic alignment, collaboration, and a strong data foundation to ensure the successful implementation and value creation of AI projects.

How do the perspectives of business stakeholders and technologists differ regarding AI investment, and what strategies prove effective in aligning these viewpoints?

The perspectives of business stakeholders and technologists often differ regarding AI investment. To align these viewpoints, several strategies prove effective.

  1.  Establishing clear communication channels is crucial. This can be achieved by scheduling regular meetings between business and technology teams to discuss AI initiatives, share progress updates, and address concerns. Additionally, developing unified reporting frameworks that present both technical progress and business impact ensures all stakeholders have a comprehensive understanding of AI projects.
  2.  Joint strategic planning is essential. This involves using frameworks like Objectives and Key Results (OKRs) to align AI projects with business objectives, with both business and technical teams involved in setting and reviewing these goals. Shared roadmaps that outline the timeline, milestones, and expected outcomes of AI initiatives, highlighting both business benefits and technical milestones, are also beneficial.
  3.  Integrated decision-making is key. Forming cross-functional teams comprising business leaders, technologists, and data scientists allows for collaboration on AI projects from conception to implementation. Techniques like value poker can be utilised to involve all stakeholders in the prioritisation process, ensuring a balanced consideration of both business value and technical feasibility.
  4.  Education and training play a significant role. Providing training for technologists to understand business strategy, financial metrics, and market dynamics, and offering workshops and resources for business stakeholders to gain a better understanding of AI technologies, data governance, and implementation challenges, are both effective strategies.
  5.  Initiating pilot projects and demonstrating success can help bridge the gap between technical potential and business outcomes. Starting with small, manageable pilot projects that can quickly demonstrate value and build confidence among all stakeholders is a good approach. Sharing case studies and success stories that highlight the tangible business benefits of AI projects can also be beneficial.
  6.  Using balanced metrics and KPIs for comprehensive evaluation is important. A balanced set of metrics and KPIs that measure both business impact (e.g., ROI, customer satisfaction) and technical performance (e.g., data quality, system reliability) should be used. The Value of Investments (VOI) framework can be applied to include non-financial value drivers such as employee engagement, innovation, and customer experience, providing a more holistic view of AI’s impact. This comprehensive approach ensures that all aspects of AI investment are considered, aligning the perspectives of both business stakeholders and technologists.

You explored potential future operating models for AI-enabled organisations. Based on your findings, what do you predict will be the most significant changes in organisational structures or roles over the next decade as AI becomes more integrated into business processes?

AI-centric roles are emerging, expanding existing roles and creating new ones like AI Product Managers and Data Ethicists. Cross-functional AI teams are adopting agile methodologies for AI development. AI governance structures and decentralised data ownership are being established for ethical and efficient AI deployment. Enhanced collaboration between business and IT is achieved through shared goals and unified metrics. Organisations are focusing on AI literacy and training, with continuous learning programmes and AI academies. Leadership roles are evolving with data-driven decision-making. The workforce is being augmented with AI, with traditional roles evolving to focus more on human-centric tasks. Ethical AI practices are being institutionalised with regular audits. Scalable and modular AI infrastructure is being developed, with increased reliance on cloud-based platforms.

In summary, navigating the complexities of AI investments demands a strategic approach that harmonises business goals with technological capabilities. Leveraging frameworks like OKRs, promoting cross-functional collaboration, and maintaining a robust data foundation are crucial for maximising AI's value. Pierre Marchand’s insights offer a valuable roadmap for businesses aiming to effectively integrate AI, ensuring their investments deliver meaningful and sustainable outcomes. As AI evolves, staying informed and adaptable will be essential to maintaining a competitive edge in this dynamic landscape.

These insights are derived from a joint study conducted by Cognizant and CIONET on the "Game of Value" in AI-enabled organisations. For a deeper dive into our findings, download the full report here.

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