According to a leading thinker in the field of machine learning, Barry Libert, the key drivers in today’s highly competitive digital market are capital allocation and business model redesign. And the business model that leverages data and AI and outperforms all others is called Network Orchestrators. This business model crosses industry boundaries and changes how an organisation creates value, by leveraging networks and machine learning.


Artificial Intelligence (AI)—and its subsets Machine Learning (ML) and Deep Learning (DL)—are hot topics of discussion in many industry sectors. In consumer markets, debates run hot about privacy, ethics and security concerns, yet these cognitive computers can only learn and control what we give them. Although research shows AI and its various subsets drive value for digital disruptors, business leaders in traditional organisations have difficulty understanding how the increasing volumes of data can solve their real-world business problems.

What is Machine Learning?

Machine Learning is the science of programming computers to learn for themselves. Subject matter experts such as marketers and finance analysts annotate historical data and information to train the Machine Learning model. The ML model then analyses the data and identifies patterns. ML uses the results to learn and make future predictions.

Machine Learning Infographic

Cognitive computers are programmed to perform very straightforward, repetitive tasks efficiently and at scale, 24/7 without tiring. For example, American Express relies on data analytics and machine learning algorithms to help detect fraud in near real time, therefore saving millions in losses. These tasks would take humans countless hours to complete. Algorithms can scan, cross-reference and report in seconds without errors and often make better, more consistent decisions than humans.

Deep Learning

Deep Learning is a subset of machine learning characterised by layers of artificial neural networks designed to perform like human thinking. The advantage of Deep Learning over Machine Learning is scalability. Constructing larger neural networks and training them with more and more data, increases their effectiveness. By contrast, other machine learning models eventually reach a performance plateau.

An example is Language Recognition. Deep learning machines are beginning to differentiate language nuances, enabling virtual assistants such as Apple’s SIRI, Google Home and Amazon’s Alexa to understand local dialects. A machine can decide someone is speaking English and then engages an AI that is learning to tell the differences between dialects. Customised language models that take the user’s location into account are known as geolocation-based language models (Geo-LMs). Once the dialect is determined, another AI will step in that specialises in that particular dialect. All of this happens without human intervention and enables us to talk to computers.

Another application of AI is natural language generation (NLG) and natural language processing (NLP). Media companies use these automations to convert data into intelligent content related to news, sports, finance and, most importantly, marketing. Companies can respond almost instantly to new trends, creating timely and relevant stories for their clients.

Why Deep Learning

How AI drives business outcomes

Today, employees, customers and investors are flocking to AI-powered, digital platform companies and rewarding them because of the substantial business value they generate. Dubbed the fourth industrial revolution, this era is characterised by mobile computers, data collecting sensors and machine learning. These developments are disrupting the entire global economy.

The Fourth Industrial Revolution

What are Network Orchestrators?

Network Orchestrators create a network of peers in which the participants interact and share in value creation. Otherwise known as the sharing economy, Network Orchestrators leverage community assets and sentiment. They may sell products or services, build relationships, share advice, give reviews, collaborate, co-create and more. Examples include eBay, Red Hat, Visa, Uber, TripAdvisor and Alibaba. The community also decides whether the service will succeed or fail.

Network Orchestrators outperform traditional asset-centric organisations on several key dimensions. These advantages include higher valuations relative to their revenue, faster growth, scalability and larger profit margins. Digital platforms power these business models and use network effects driven by AI to fundamentally change how an organisation creates and delivers value. They leverage the power of AI, ML and DL to drive long term value, growth and profits.

For example, social media platforms use machine learning to personalise news feeds, suggest people you may like to connect with and target ads more effectively based on your interests. Similarly, when we book with Uber, the app estimates the price of the ride. But how do they guarantee the price? The answer is machine learning. Uber uses ML to define price surge hours by predicting rider demand and traffic flows. ML plays a major role in the entire cycle of the service.

Network-based business models require new technologies and competencies. Most senior leaders are skilled at building, owning, and managing their own physical assets or people. Network Orchestrators, however, rely on intangibles such as knowledge (Gerson Lehrman Group) or relationships (Facebook), or other people’s assets (Uber) by leveraging a network of individuals and their individual assets and relationships.

According to Libert:

“Network orchestrators were eight times more valuable than asset builders, four times more valuable than service providers, and two times more profitable than tech providers. That shouldn’t surprise anybody. If you have to produce something from scratch and market it, that’s expensive. Or you can create a platform that people use.”

Here are Libert’s six principles for success in orchestrating networks:

1. Technology

Shift from physical to digital. You must develop a digitally enabled platform around which people can congregate. These are your evangelists, your customers, your service networks, your influencers, advocates, partners and their networks. Build a platform for them to converge and generate value related to your key value proposition.

2. Assets

Shift from tangible to intangible assets. Physical assets are becoming a liability. Intangible assets are your brand, your people, your customers, who promote your brand and network. Divert at least 5% to 10% of investment capital to activating your networks.

3. Strategy

Leaders must move from operator to allocator. They should be active allocators of capital, like portfolio managers.

4. Leadership

The new leadership mindset is co-creator. Someone who knows how to motivate, inspire and work alongside others to develop the network.

5. Boards

Switch from governance to representation. Directors must find diverse skills to design and support new digital strategies.

6. Fluid Networked Teams

Think less rigidly about roles, processes, products and industries. Focus on value creation rather than hierarchies and silos.


Network Orchestrators place Artificial Intelligence—and its counterparts, Machine Learning and Deep Learning—at the core of their business function, rather than treating it as a separate data analysis function. The task of traditional organisations is to understand their data points, identify service differentiators and leverage the power of artificial intelligence to help re-imagine products, services and entire workforces while driving value and new revenue streams from fewer assets.

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