Navigating a new roadmap to growth
Business leaders discuss four areas experiencing tectonic shifts: organization and people, experimentation for innovation and new revenue streams, partnership ecosystems and building dynamic processes with AI.
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.
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.
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.
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.
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