Generative AI has captured the attention of business leaders, employees, consumers, and governments alike. Unlike previous technological debuts, it holds the potential to transform operating and business models entirely. However, realizing its full potential requires a strategic approach.
Cognizant surveyed senior business and technology decision-makers in large businesses across the US and UK. The survey revealed that 61% of executives expect generative AI to result in complete business transformation. This optimism has spurred predictions of economic and productivity growth driven by generative AI-powered systems, models, and tools.
Senior leaders must move past the hype to realize generative AI’s true potential, adopting it economically and considering risks. Cognizant has created a framework (image below) to support enterprise innovation, providing strategic guidance and insights to maximize value. This framework helps businesses connect the dots across their operations, no matter their current stage.
Establishing a North Star
To harness the power of generative AI effectively, businesses need to establish a “North Star”—a set of core strategic objectives to drive activities forward. This involves defining guardrails to accelerate innovation and creating a project map. Without a clear strategy, businesses risk locking value away in siloed solutions or incurring runaway costs due to poorly tracked initiatives.
An example is the proliferation of software-as-a-service (SaaS) applications. Without effective oversight, businesses have found themselves managing sprawling estates of non-standard tools, driving up costs and embedding inefficiencies. In 2022, organizations were running an average of 130 SaaS applications, up from just eight in 2015.
Pursuing Risk-Conscious Innovation
Businesses must adopt a deliberate approach, weighing the potential of AI with its risk considerations. Nearly all senior leaders (96%) in our survey recognized the need for a central control point to manage new generative AI use cases, costs, and resource allocation. This ensures that AI initiatives align with the business’s overarching corporate strategy.
Risk-conscious innovation involves setting boundaries informed by regulatory commitments, information security protocols, and corporate processes. For example, healthcare organizations must protect patient data, while finance organizations must be wary of involving core systems that require always-on availability.
Connecting the Dots Across Use Cases
Collecting business cases from various parts of the organization and mapping out a clear constellation of projects as they evolve is essential. Real business value emerges from connecting disparate initiatives that add value to each other. For instance, an automotive sales team using AI for customer insights could collaborate with product development teams using AI to enhance vehicle designs.
Turning on the Value Amplifier
Once a business establishes a North Star, a clear project map, and evolving guardrails, the focus turns to building a value amplifier. This involves forming a cross-functional team with experts from HR, IT, compliance, and corporate strategy. This team guides business leaders in organizing pilots, designing experiments, and developing proofs of concept. More importantly, it helps shape the overarching strategy, prioritization, success measurement, and coordination efforts.
Scaling Experiments to Market-Ready Realities
To deliver value, experiments must scale. This requires resources, highly-skilled people, and the capacity to evolve propositions as challenges emerge. The goal is to mesh various projects into a well-resourced program of initiatives prioritized for their impact and potential to interlink with other projects for multiplied value.
For example, as an automotive product design team accelerates its plans, they will need live customer feedback from the sales team. By managing activities and redirecting resources centrally, businesses can ensure joint success and alignment with market realities.
Fine-Tuning Strategy with Real-World Experiences
Businesses need to collate data, experiences, successes, and failures from every use case to inform and evolve their strategy. Insights generated from past experiences can inform future use-case KPIs and resource allocation, ensuring more value for less expense. This iterative approach allows businesses to adapt to the evolving demands of various projects, creating a flexible and skilled team capable of handling the dynamic needs of the business.
Conclusion
Generative AI holds immense potential to transform businesses and drive innovation. However, unlocking this potential requires a strategic approach that aligns AI initiatives with the business’s core objectives, manages risks, and connects disparate efforts. By following a structured framework, businesses can amplify the value of generative AI, turning hype into real business value.
For a deeper look at our insights into generative AI, see our report “Gen AI and the Future of Work: What Businesses Need to Know.”
For more on the value of Generative AI, read Cognizant’s blog article.
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