Page last updated: April 12, 2023

How to lead the next wave of disruption with Artificial Intelligence

Note: This is a work in progress.


AI is poised to be the next major disruptor, reshaping industries, sectors, and departments across the globe. Similar to previous disruptions dating back to the first industrial revolution, AI is unlocking the full potential of human capabilities, leading to a more meaningful and productive workforce. 

The opportunity

Though the technology behind AI has been around for decades, it is now that it has become commercially viable for every organisation — big and small to adopt and disrupt owing to the advances in computing power and the ease of adoption. 

Early AI adopters and high performers are already reporting greater outcomes across industries, sectors, and departments resulting in meaningful cost decreases and revenue increases as a direct result of AI adoption.

Here are some interesting data points that help in understanding the scale of the opportunity — 

  1. PwC’s Global Artificial Intelligence Study estimates the potential contribution to the global economy by 2030 from AI to be $15.7 trillion which is more than the current output of China and India combined. If AI were a country, it would be the second richest
  2. McKinsey’s State of AI 2022 study reports that AI high performers spend at least 20% of their digital technology budgets on AI-related technologies. These high performers are also realizing meaningful cost decreases and revenue increases.
  3. AI Index 2023, published by Stanford University’s Institute for Human-Centered Artificial Intelligence confirms that AI is not just an academic concept. In 2022, there were 32 significant industry-produced machine learning models compared to just three produced by academia. This means that we are at an inflection point where 

AI is also fueling the emergence of new business models, products, and services. For example, AI-powered platforms and marketplaces are connecting businesses and consumers in innovative ways, and AI-driven technologies like virtual assistants, and personalized healthcare are creating entirely new product lines for businesses. 

Apart from driving productivity improvements, AI is also paving the way for the development of innovative and sustainable technologies like hydrogen fusion — a promising source of clean energy and generating new antibodies — improving treatments and cures for diseases. 

AI is propelling us towards a more sustainable and advanced future, where the power of human innovation combined with AI’s capabilities can create positive impacts on various aspects of our lives.

The challenges

Organizations embarking on their AI journey often face significant challenges that require careful consideration and strategic planning. Implementing AI technologies can be complex and daunting, requiring organizations to navigate through various hurdles to ensure successful adoption and integration. Some of the common challenges — 

  1. Cost intensive — Training new models is becoming more expensive as the parameters used to train models are increasing. For example, PaLM, one of the flagship models released in 2022, cost 160 times more and was 360 times larger than GPT-2, one of the first large language models launched in 2019.
  2. Talent Shortage — Building and deploying AI models requires a skilled workforce with expertise in areas such as data science, machine learning, and software engineering. Demand for AI-related professional skills is increasing across virtually every industrial sector increasing competition.
  3. Ethics and Regulation — Mentions of AI in global legislative proceedings have increased nearly 6.5 times since 2016. As policymakers’ interest in AI rises, organizations must navigate through complex regulatory frameworks and adhere to ethical principles to ensure the responsible and ethical use of AI in their operations.
  4. Integration with existing processes and systems — Implementing AI technologies may require integrating them with existing processes, systems, and workflows. Organizations may face challenges in identifying and integrating AI solutions seamlessly into their operations, which may require significant changes to existing processes and systems.
  5. Model explosion — The number of AI models are exploding. In 2022 alone, there were 35 significant models. This makes it difficult and expensive to select the right model suitable for an organisation. 

The solution

For organisations, the only logical way forward is to

  1. Research well — Identify the areas of business that can benefit from AI adoption and directly improve the bottom line. There are plenty of use cases to take inspiration from. In 2022, the most commonly adopted AI use case was service operations optimization (24%), followed by the creation of new AI-based products (20%), customer segmentation (19%), customer service analytics (19%), and new AI-based enhancement of products (19%)
  2. Plan your integration — AI models rely heavily on data, and organizations must ensure that they have accurate, reliable, and relevant data available for training and inference. Moreover, prepare your existing processes and systems to integrate with AI solutions. API integration is often the simplest and universal. 
  3. Go commercial —Adopt commercially available AI models. Most models allow customisation using your own organisation data. This will ensure quick adoption at a lower cost than building your own model. It also exposes you to an open data ecosystem and allows you to access external data to improve your own models. Going commercial allows you to skip the talent war as most of the heavy lifting is done by the model owners. It is like adopting a CMS or an ERP solution. 
  4. Reskill —If you take the commercial path, you can reskill your existing talent instead of recruiting and retaining top talent with the necessary skills and expertise. Engage non-technical ones by leveraging low-code or no-code platforms to speed up adoption. 

The Product

AI Library has been design to accelerate AI adoption in companies and for individuals. We do this by making the following processes easier

  1. Evaluation — Test enterprise-ready AI models in a no-code environment. This provides an easy way to identify appropriate AI models even by non-technical team members
  2. Integration — Connect models to existing applications using APIs. This helps in faster adoption at a lower cost without the need to hire additional talent
  3. Differentiation—Fine-tune models to customise for business use-case
  4. Extension—Combine models to create new models without coding
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