As enterprises grapple with the rapid advancement of Generative AI (GenAI), leaders find themselves at a crossroads, just as they did amid previous technological revolutions. The difference this time is the unprecedented velocity and scale of GenAI adoption. Instagram's journey to 100 million users in 2.5 years was a considered a digital marvel, yet ChatGPT shattered this record by achieving the same feat in just six weeks. This scale of adoption is an indication of the transformative potential of GenAI in business.

Consumers enjoy the perks of GenAI for tasks like homework, music creation, art, and general creativity without much complexity. Enterprises, on the other hand, face a more complex landscape filled with immense opportunities and significant challenges. In the business world, finance departments are leveraging GenAI for revenue forecasting and detecting leakage. HR is using GenAI to boost employee engagement. R&D departments are accelerating research with GenAI’s rapid data processing and sense-making capabilities. Marketing teams increasingly rely on GenAI for content generation and personalized customer interactions. GenAI use cases are permeating all corporate functions and lines of business.

However, the return on investment (ROI) from these ubiquitous GenAI initiatives remains elusive for many. This often stems from an unbalanced focus on rapid proofs of concepts. They solve immediate problems, without a broader strategic perspective. To fully capture the value of GenAI, it's essential to broaden our approach to a comprehensive view that encompasses enterprise value chains, the larger industry value network, and the surrounding ecosystem.

Consider the retail and consumer products sector as an illustrative example. Companies now have access to vast amounts of data about customer buying patterns found in both private and public datasets. Retailers can aggregate candid product reviews and consumer advice scattered across YouTube, Reddit, TikTok, blogs, and numerous forums. The insights from both verified influencers and everyday consumers resonate worldwide through various external social platforms. Data on global inventory levels, distribution logistics, and product returns are available across the value network. But how are these disparate datasets being used to drive more precise and forward-looking decisions within the merchandizing and procurement organizations? Are these datasets being used to collect feedback for product design improvements and enhancements? While it might be humanly impossible to achieve such precision, speed, and scale of intelligent action, GenAI agents are ideal for empowering employees and helping businesses derive value from seemingly disparate opportunities across value networks.

This wealth of information provides the potential to develop near-omniscient GenAI agents capable of supporting functions across customer service, sales, operations, and beyond. Unfortunately, many of the promising GenAI proofs of concept (POCs) fail to progress to full-scale production due to their narrow focus, limited datasets, and isolated application. Usually, ROI fails, funding runs out, and the POCs are shelved. Small-scale, free-form GenAI playgrounds are an important initial step to introduce the new technology across the enterprise, and they are needed to foster a culture of innovation. However, POCs need a path for scaling into production and achieving a wider field of impact.

The success of enterprise GenAI programs is dependent on leadership alignment, an outside-in perspective, IT readiness, agility in execution, and responsible adoption. Here are six simple steps that organizations can follow to transform into an AI-powered enterprise:

1. Form an Enterprise AI Steering Committee

Successful GenAI-powered enterprise transformation strategies start at the top. An Enterprise AI Steering Committee, led by C-suite executives such as the CEO or CIO, is essential. This committee should also include other key stakeholders like the CFO, CMO, CHRO, and COO, who together lay down the strategic vision and oversee the momentum of GenAI initiatives. Leaders from the value network and ecosystem must be co-opted into the steering committee.

Depending on the specific context or enterprise priority, similar multifunctional steering committees could be set up at unit levels. In a retail context, for example, that might mean an Omnichannel AI Steering Committee, a Customer Service AI Steering Committee, and a Supply Chain AI Steering Committee. Meanwhile, the broader Enterprise AI Steering Committee can ensure that the most impactful ideas, tools, and approaches flow freely from one unit to another. The primary role of these Steering Committees at any level is to align GenAI strategies with the overarching enterprise goals and unit level business objectives.  The steering committees must remove impediments that tend to slow down progress and enable a “one team mindset”. Most importantly, the Enterprise AI Steering Committee would be empowered to make decisions on responsible sharing of data and would review and approve proposals for new GenAI programs.

2. Onboard a Transformation Advisor

A transformation advisor with a strong background in GenAI and industry-specific knowledge is invaluable. This advisor helps bridge the gap between technological capabilities and business needs, ensuring that GenAI solutions are not just technically sound but are aligned to deliver measurable business outcomes. They play a critical role in managing adoption, addressing skepticism among stakeholders, and integrating GenAI into the fabric of the enterprise.

3. Conduct a GenAI Readiness Assessment and Develop a Plan

Prerequisites for the success of an enterprise GenAI strategy include a robust enterprise cloud strategy and a data consolidation roadmap. A composable architecture will better align with the changing needs of business and C-level business imperatives. Before making significant investments in new GenAI capabilities, a comprehensive GenAI readiness assessment is essential.

This assessment should review existing infrastructure, data availability, tooling, and skillsets to identify gaps that need bridging before full-scale GenAI project portfolios are commissioned. It is important to factor in the requirements of entities in the value network and the ecosystem to the extent desired and feasible. Based on this assessment, organizations should develop a phased plan of iterations that deliver measurable and progressively increasing business value. 

4. Take an Ecosystem View

A holistic ecosystem approach ensures that GenAI solutions are scalable and can leverage data and insights across the enterprise and beyond, including partners and customers. This approach will contribute to robust and versatile GenAI applications that deliver value not just within isolated departments, but across entire value chains and value networks. In domains like healthcare, with high levels of regulatory scrutiny, one promising solution is the data hub. Rather than a one-time massive cloud migration, individual datasets can be safely and securely moved into a cloud data hub while other datasets remain in their approved on-premise legacy systems. As the data in the data hub scales, business users inevitably uncover new and unforeseen opportunities to apply GenAI to data in a way that opens new efficiencies and revenue streams. 

5. Set Up a GenAI Champion Squad

This squad consists of GenAI advocates and leaders from various business units who ensure that GenAI initiatives are relevant, executed well, and adopted in their respective units. They are accountable for the success of their unit-level GenAI projects and the achievement of associated business metrics. They act as liaisons between the GenAI steering committees and the operational teams, fostering a culture of collaboration and innovation.

6. Establish a GenAI-First Culture

Leadership needs to actively foster a culture that embraces GenAI, focusing on continuous learning and ethical use. Wipro’s own billion-dollar investment in AI, for this reason, includes a provision for scaling AI knowledge and increasing awareness on how to use AI responsibly across all roles. Such efforts should explain the basic functionality and benefits of GenAI, establish metrics for success, and assure that all employees understand their responsibility in this transformative journey. Unit-level AI Academies, meanwhile, can advance continuous learning and certification of employees aligned to their career aspirations and business needs. AI hackathons and AI idea jams will increase active participation from innovation employees and enable the formation of communities of practice.

Riding the GenAI wave demands a fundamental transformation in how enterprises operate and innovate. Organizations that approach GenAI adoption through these six steps will enhance their operational efficiency while gaining a significant competitive advantage in the evolving business landscape. This structured approach ensures that GenAI is a foundational element of enterprise strategy, driving long-term growth and innovation. This doesn’t mean slowing down the creativity of the many teams who have been applying GenAI in various niches across the business. It simply means providing talented innovators with guidance, tools, knowledge sharing forums, consolidated datasets, and governance frameworks that consistently allow GenAI proofs of concept to serve the overarching business strategy. This effort will rapidly supercharge and scale the most promising POCs, minimizing wasted effort and harnessing both GenAI and human creativity in sustainable programs that deliver measurable value and desired impact.

About the Author

Sandhya Arun
Vice President & Sector COO, Americas 1

With 30 years of professional experience, Sandhya has worked in various roles including strategy, consulting, delivery, operations, transition, and transformation. She is intentional about working with enterprises that create a positive impact on people, profit, and the planet.