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Navigating a new roadmap to growth

Imagine embarking on a grand expedition, where the terrain constantly shifts, and the map you hold in your hands redraws itself to reveal new and exciting destinations.

This is how generative AI is unfolding, placing boundless potential at our fingertips, democratizing access to insights and skills and setting the stage for new value chains to emerge.

Successfully navigating this journey will require leaders to relinquish traditional notions of control and embrace a more fluid approach to organization and value-creation.

To explore performance imperatives for an era of pervasive AI, Cognizant spoke to business leaders and subject matter specialists about four areas experiencing tectonic shifts: organization and people, experimentation for innovation and new revenue streams, partnership ecosystems and operations.

Organizational genetics

Organizational genetics are about the composition and culture of an organization, which determine its identity and capabilities. Traditional command and control structures have become too rigid and restrictive, inhibiting teams' ability to adapt quickly to changing constraints. Leading organizations are experimenting with flatter structures and ways to establish flexible frameworks that create the right context for collaboration and innovation.

The multi-generational workforce of today brings unique values, expectations and preferences. Engaging and motivating them to build new skills and perform at their best requires a deep understanding of what moves them and gives purpose to their lives.

Furthermore, as human-machine collaboration is being redefined, an employee base that is cognitively diverse will be essential for speed and innovation, as well as for building AI systems that are ethical and user-centric. 

What leaders are asking:
  • How can companies best communicate with, engage and incentivize a multi-generational workforce?
  • How can leaders build the right organizational context to foster collaboration and innovation?
  • What are some possible alternatives to traditional organizational structures?
  • How will new skill acquisition have to change to best enable workers in the age of AI?

Experimentation—an evolutionary imperative

In a quickly shifting environment, the ability to experiment—be it with different business models, revenue streams, talent models or ecosystem plays—is an evolutionary imperative.

Consistently activating new revenue streams has long been a growth strategy for digital disruptors like Amazon, Google and Stripe, for example. More broadly, companies prioritizing new business building outperform other companies on revenue growth, even during times of economic volatility1.

However, while for digitally native businesses experimentation is built into the fabric of everyday operations, more traditional companies need focus in order to make it a habit. Complex organizational structures, bureaucracy and rigid financial frameworks can hinder experimentation.

What leaders are asking:
  • How can enterprises best leverage new technologies, including generative AI, for experimentation and innovation?
  • How can leaders engender curiosity and a growth mindset in a diverse workforce?
  • How can businesses achieve radical innovation versus incremental improvements?
  • How can teams overcome innovation fatigue: sustain motivation, financing, avoid burnout and celebrate successes?

Purpose-built partnerships

Generative AI is likely to have a significant impact on how businesses collaborate and partner. While competition to bring novel solutions to market will increase, there will be compelling reasons for companies to pool resources and expertise to create more robust AI solutions that address a specific purpose.

For example, a technology company could partner with a pharmaceutical company and a healthcare provider to develop a platform that would improve the effectiveness of new drugs. This partnership could significantly speed up the drug discovery process and also make it more patient-specific, leading to better treatment outcomes. Each partner in the value chain would bring a unique contribution—AI expertise, drug development know-how, patient data and clinical expertise—and benefit from each other's knowledge.

What leaders are asking:
  • How should businesses think differently about partnerships in the light of accelerated AI and generative AI adoption?
  • What types of partners should business leaders look for, to develop an ecosystem that will support growth at the new pace of business?
  • What are the benefits and risks of co-creation partnerships, where partners create joint solutions in a value chain, and how to best evaluate them?

From rigid to dynamic processes

Most business process automation efforts today use classic machine-learning algorithms to automate static, pre-defined processes. While enabling speed, simplification and personalization of processes, these models depend heavily on labelled data and human expertise.

The emergence of generative AI brings a seismic shift towards dynamic, data-driven business operations. Unlike traditional automation, gen AI can simulate and generate countless scenarios in real-time, utilizing its ability to learn and create. Rather than just react, processes can be dynamically created to address a specific goal. This shift enables businesses to proactively address changing conditions, making automation more versatile and adaptive.

The new levels of flexibility and speed will enable companies to optimize workflows across a multitude of operational aspects, from supply chain management and sales forecasting to customer relationship management.

For example, in traditional travel planning, online platforms might use static machine-learning algorithms to recommend vacation packages, hotel bookings, or flights based on a user's past preferences and behavior. This is a mostly reactive system, where suggestions are primarily based on historical data. With generative AI, systems learn from a user's past choices, current searches, and overall behavior on the platform. For example, if a user typically prefers beach vacations but is searching for a winter holiday, the AI doesn't merely suggest the most popular ski resorts. It simulates various scenarios and generates an itinerary that includes a historical mountain lodge, a beginner-friendly ski school, and a local winter festival—all because it learned from the user's past behavior that they enjoy unique accommodations, learning new skills and cultural experiences.

What leaders are asking:
  • How can I best transform my operations to take advantage of the power of gen AI? Where do I start?
  • How should I empower my most knowledgeable employees to contribute to business operations innovation using gen AI?
  • How can I best incorporate sustainability into my operations design?

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