AGI: Enough with the hype; let’s ask the important questions
Today’s horse-race coverage of artificial general intelligence fails to consider its implications.
April 26, 2024
As the generative AI revolution progresses, the initial excitement has given way to the realities of integration and scaling. There is palpable pressure to act quickly—yet the myriad opportunities, fast technology evolution and significant complexity associated with implementing generative AI are causing businesses to get caught up in lengthy analyses as they try to determine where to place big bets.
Authors Mike Turner, Shveta Arora and Andreea Roberts believe leaders can get unstuck by moving quickly on four foundational levers of AI transformation, and by pondering one strategic question to guide big investments.
The AI landscape today is complex. Nevertheless, based on our work with hundreds of clients, the business world is experiencing a surge of generative AI pilots and proofs of concept in both back and front offices—speeding product design, sharpening procurement decisions, making suggested fixes to field services techs, or providing data and insights to customer service agents.
But how can companies advance from a series of disconnected experiments to a cohesive, change-the-business, change-the-game, strategic AI whole?
To cut through the complexity and advance with confidence, leaders must focus on two critical actions: They need to prioritize and fast-track the foundational shifts necessary for AI readiness, such as data modernization, workforce upskilling and agile governance, while scaling early gen AI success. Simultaneously, they should confront a pivotal strategic question that will guide their most significant AI investments and shape the course of their transformation journey.
Businesses that have navigated other big technology-driven changes—cloud computing, mobile technologies, automation, machine learning—might feel well-equipped to handle the business transformations that follow in the wake of technological disruption.
Companies able to compress innovation cycles in this way will capture significant first-mover benefits.
Given the current pace of technology evolution, “AI maturity” is really about how well the business is equipped to compete and thrive in a future where AI is incorporated into every process, system and job role. AI-led transformation is as much a mindset, modus operandi and culture shift as it is a physical retooling. Companies need to both insulate against new risks and be able to quickly seize new opportunities created by AI.
We outline below a series of moves businesses can make with confidence to advance toward preparedness on four key dimensions: data, technology, the workforce and change itself.
Figure 1
Data is the fundamental fuel for AI. To take full advantage of the next generation of AI and analytics, businesses have to modernize their data estates and evaluate opportunities to capitalize on data in a value ecosystem context.
To drive enterprise-wide AI, leaders need to:
Figure 2
Businesses should think of AI components less as one-off projects and more as platforms. Doing so will accelerate the integration of intelligence into solutions. If enterprises focus on core enablers like software engineering and evolving their enterprise architecture to a modular design, they will be able to rapidly integrate new capabilities and leverage new technologies.
To prepare for the new speed and agility required to succeed, enterprises will depend on the flexibility and scalability of a modern, hyper-automated, cloud-native architecture:
Figure 3
Of all the changes along the journey of AI-led transformation, the changes related to people present some of the greatest unknowns.
As a team of researchers from MIT, Harvard and Wharton suggested last year, it will take seamless collaboration to unlock the greatest value from the human and AI partnership. When humans know when to engage AI, and when to rely on human knowledge and intuition, they realize much better outcomes compared with human-only or machine-only work.
Here are two concrete actions businesses can take to prepare employees for the next level of human-machine collaboration:
Employees need to learn how to best work with AI to augment their capabilities, and they need to be inspired to experiment and innovate. With these critical elements, businesses can turn employees into champions in the transformation journey.
Figure 4
To quickly drive change across the organization, businesses require ongoing change management that reflects the needs of a multi-generational workforce—especially when it comes to building trust, increasing agility and enabling a culture of innovation:
As the evolution of AI continues at an unprecedented pace, the organizations that will thrive are those that can successfully navigate the human dimensions of the transformation. By building trust, fostering agility, investing in continuous upskilling and establishing strong AI governance and risk management practices, they can create a workforce that not only adapts to the AI revolution but also harnesses its full potential to drive innovation and growth.
As leaders navigate this transformative journey, they will face critical forks in the road. Of all the decisions they’ll need to make, however, we believe there is one choice that will help crystalize their thinking about the purpose of using AI, and thus facilitate the decision on where to focus first.
Should leaders take the optimization path, leveraging AI to maximize productivity, building a distinctive knowledge core and subsequently delivering disruptive value propositions? Or rather, should they boldly pursue a disruptive challenger path, harnessing AI's power to create radically new products, services and business models?
The answers to these questions will act as a North Star, guiding investment decisions and illuminating the sequence of steps one must take to forge a path to AI-led transformation.
We see this as the no-regrets path, and it’s also the route most traveled. The priority with this approach is to optimize the organization’s existing operations with generative AI, and then establish and leverage an industrialized change engine to build further productivity use cases quickly. Disruptive innovation is then evaluated and funded from the resulting efficiencies.
These initiatives are large in scale but executed incrementally. Organizations opting for this direction need to:
By pursuing this route, businesses can build a solid foundation for future disruption. They can take efficiency gains from automation and workforce augmentation and invest them in building next-generation data estates, knowledge systems and innovative customer offerings.
While the optimization route does not preclude businesses from leveraging generative AI, it does not fully exploit the disruptive potential of the technology until the later stages of implementation.
Disruptive AI uses cases cannot be built on top of current business without specialized knowledge and often do not make sense within the current cost structures of an established business. Therefore, for a certain segment of businesses, there is another option.
This choice is a “big-bet” approach, in which companies focus on creating entirely new, game-changing propositions from scratch, and enabling that proposition to grow outside of “the system.” IBM’s PC, Amazon Web Services, the Toyota Prius and Google’s AdSense were all developed in autonomous units that enabled focus and speed.
By incubating these initiatives in a separate, unconstrained environment, these trailblazers can nurture and scale their disruptive propositions without the limitations of legacy systems, processes or mindsets. As these pioneering ventures gain traction and prove their value, the rest of the organization can gradually pivot toward the new operating model, ensuring a smooth transition that maximizes the potential of generative AI.
Alternatively, some organizations may choose to maintain their disruptive AI ventures as separate entities, allowing them to operate with the agility and autonomy needed to stay at the forefront of innovation. This approach enables the core business to maintain stability and continuity while still reaping the benefits of its trailblazing offspring.
The steps for businesses choosing this direction are:
This is a higher-stakes strategy that requires an appetite for bold bets. However, for companies with the right assets and ambition, it presents an opportunity to create an enduring competitive advantage that others will struggle to replicate.
Whichever path you choose to take, Cognizant can help to jumpstart your journey and partner with you all the way to results. Our AI practitioners can assist across strategy, proof of concept, scale-up and refinement of AI solutions, while also ensuring AI use is sustainable at scale through domain data, hybrid models, platformization, automation and quality assurance.
The generative AI revolution presents businesses with an unprecedented opportunity to reshape their industries and redefine what's possible. But to fully harness this potential, leaders must navigate a complex and rapidly evolving landscape that can often feel overwhelming and paralyzing.
However, even in the face of such complexity, progress is possible. By prioritizing the foundational shifts necessary for AI readiness—such as next-gen software development, data modernization, workforce upskilling and R&D—and applying generative AI in areas with proven returns today, enterprises can optimize performance and achieve early success. Furthermore, recognizing and acting on opportunities to disrupt and create new value will position businesses for long-term growth and competitive advantage.
For more information on how to confidently transform for the future, visit our Rewire for AI page.
Sign up for the Cognizant newsletter to gain actionable AI advice and real-world business insights delivered to your inbox every month.