How AI brings corporate might to small teams, reshaping business for all

While this shift distinctly enables startups, paving the way for the “one-person unicorn,” it can also help large corporations achieve levels of speed and innovation never possible before, as it empowers teams and individuals in an unprecedented way.

Understanding what’s in progress can help leaders better navigate change and build AI-powered enterprises that elevate human ingenuity.

GenAI is accelerating the democratization of advanced technologies. The integration of AI, analytics and automation into software products is enabling the rise of a modern, outcome-driven IT stack, where the output of largely automated workflows is not simply facts and figures but inferences and recommendations.

Furthermore, commands or requests of the software can now be accomplished with natural language instead of code, increasing accessibility beyond programmers.

For instance, Cognizant Neuro® AI helps users analyze complex scenarios with multiple objectives and constraints using a ChatGPT-style interface. With this platform, workers can identify ways to optimize stock levels and reduce spoilage in inventory management, dynamically adjust prices to maximize revenue and customer retention or allocate support staff efficiently to meet customer demands.

As output, Cognizant Neuro® AI presents a menu of potential actions, detailing the implications and risks of each action for different aspects of the business. Tools like these help employees amplify their experience by expanding their knowledge of the options and empowering them with the resources they need to make informed decisions quickly.

AI agents – software programmes designed to perform specific tasks or functions within an AI system – are providing a new pathway to scaling talent. Virtual teams of specialized workers can be rapidly created with AI agents, thereby broadening access to skills and introducing a new, complementary mode of on-demand knowledge work.

Modern AI agents can learn and adapt in real time and collaborate to handle complex tasks. A real-life example would be using AI agents in a sales process – assigning them distinct roles to gather information, analyze data on target clients, markets and competitors, develop win themes, role-play to anticipate client objections and identify a set of win-win strategies.

Soon, employees can leverage multi-agent applications to tap into specialized domain expertise – faster and at scale. Furthermore, a multi-agent approach allows for sophisticated problem-solving and optimization in complex scenarios and can provide a novel perspective on leaders’ recurring challenges.

As AI democratizes corporate might, large businesses are facing a series of pressing questions:

How can they capitalize on their strengths – data assets, global presence and risk management capabilities – while enhancing agility?

What operational changes are needed to foster adaptability and innovation, allowing them to compete with AI-native businesses?

What is the optimal timeline for transformation to balance early-adopter risks with readiness for new operating models?

Here are some of the changes enterprise leaders will need to consider.

In the short term, large companies retain advantages in capital access, established supply chains and risk diversification capabilities. Yet adapting quickly and leveraging unique competencies will be imperative as AI empowers more nimble rivals.

Hybrid sourcing models are gaining traction and offer creative new ways for enterprises to accelerate transformation. In these models, partners help businesses more rapidly develop an outcome-driven IT stack and offer support by developing specialized AI agents.

As AI democratizes access to enterprise-scale capabilities, it also elevates the value of uniquely human attributes. While businesses of all sizes stand to gain efficiency and productivity, differentiation will increasingly hinge on human ingenuity.

The AI-native IT stack will blend standardization with opportunities for customization through human interaction and distinctive character. The latter will be an important foundation of differentiation for businesses.

Human creativity will also be the source of entirely new products and services that don’t exist today and are made possible by technology. In the past, we referred to this as “problem-finding.” AI can help us address known issues but humans will be the ones finding new uses for AI in areas we are not aware of today.

The power of this scale revolution ultimately lies in enabling small teams to make big impacts. Looking ahead, we might see large companies evolve towards a network-based setup. The next-era enterprise would thus be a network of networks combining small, AI-empowered teams operating with high autonomy at the grassroots yet deeply interconnected.

These networked teams could drive innovation at high speeds, launch new products and services and activate new revenue streams in a lean way while still benefiting from the resources, capital and market distribution reach of their parent organization.

Broad access to powerful technology and specialized skills will introduce new competitive dynamics and continue to catalyze change in how humans work alongside machines.

For large enterprises transforming for the AI age, success will undoubtedly hinge on how swiftly and effectively they can adapt, turning potential disruption into a springboard for their own evolution.