What is Real and What is Marketing Hype in AI and Big Data?

Manoj Saxena, Chairman, CognitiveScale | May 22, 2018

View an extract of this session held at the London Big Data and Machine Learning Revolution event in April 2018.You can also access the full video and slides.

Artificial Intelligence (AI) is rapidly moving from a mesmeric technology to a powerful teammate and a foundation for consumer and business decision making. However, AI is a young field full of amazing potential. It’s mystery and lack of understanding is also allowing for hype to grow unchecked. Unrealistic claims of an "AI singularity" and portrayals of an "AI apocalypse" are creating a hype machine that is unparalleled in recent history. The reality is somewhere in between these two extreme scenarios. With a strong focus on some of the good practices around practical and high value applications of data and AI across healthcare, insurance, financial services, and digital commerce.

Big Data Machine Learning Finance

AI today is both artificially inflated as well as accelerated innovations. Coming from the CEO of Google was this statement - AI over time is going to be as impactful, if not more, than fire and electricity to mankind.

I do believe that is the case, just like the industrial revolution amplified our arms and legs, I believe that AI is going to amplify our brains and there will be a time when we stop trying to drive a car or fly a an plane.

AI has now become the new frontier for digital transformation. Last year Mobile was the frontier for digital transformation. The decade before that was the internet. So when people use digital transformation, immediately I start talking about AI. CEO’s from Google and Microsoft said this is the next 20/50 year shift in terms of how we are going to make decisions and running processes.

You can use AI two ways, to automate and replace a job, or augment and amplify a job. AI is supposed to take away 8 million jobs in the next 4 years, and this will be incredibly disruptive, but there are 1.2 billion other jobs that AI will amplify and that’s what we focus on. Every big technology always dislocates jobs, but core processing will be transformed.

Augmentation is a much larger opportunity than automation

AI will have more impact on your market than spreadsheets did. It will completely transforms how you work. AI will do this on a much bigger scale.

Real AI and Big Data

AI has become the new frontier for digital transformation

AI is a combination of all six of these technologies. Cloud, big data, social, IOT, machine learning and blockchain, all of these put together is what AI is.

 AI and Big Data

How does AI and big data create value in the enterprise?

AI does just two things - one, it has the ability to reason over unstructured data e.g. over images, over news feeds. Second, it learns continuously from actions.

Real AI and Big Data

How do these various categories relate to one another?

So you have business intelligence market, big data market, machine learning, cognitive computing which mimics the human brain and augmented intelligence which are intelligent business processes.

Real AI and Big Data

AI and digital are powering powerful business model disruption

These are all technology companies engaging with people in a brilliant way and are disrupting business models.

  • World’s largest taxi company has no taxis (Uber)
  • Largest accommodation provider owns no real estate (Airbnb)
  • Largest phone companies own no telco infra (Skype, WeChat)
  • World’s most valuable retailer has no inventory (Alibaba)
  • One of the largest banks holds no cash (Bitcoin)
  • World’s largest movie house owns no cinemas (NetFlix)


It took the telephone 75 years to get 50 million users; it took radio 38 years to reach this; internet 4 years; and it took Angry Birds only 35 days to reach 50 million users and Pokemon Go just 14 days to reach 50 million users.

Three barriers to enterprise AI adoption

  • Lack of c-suite understanding and support for AI as a business capability
  • Unclear use cases and scaling strategy for AI in the Enterprise
  • Unable to align and scale Data Science and AI DevOps

How do I improve understanding of AI as a strategic capability?

Step 1

  • Educate around the art of the possible CognitiveScale case studies

Step 2

  • Activate using these good practices
  • Start from the top down with business outcomes
  • Build a culture of ‘learn fast and pivot’
  • Pay for success vs pray for success
  • Lay down your AI business foundation, ethical AI framework
  • Partner smartly

Step 3

  • Scale Al portfolio aligned with business and IT strategy

By providing your personal information and submitting your details, you acknowledge that you have read, understood, and agreed to our Privacy Statement and you accept our Terms and Conditions. We will handle your personal information in compliance with our Privacy Statement. You can exercise your rights of access, rectification, erasure, restriction of processing, data portability, and objection by emailing us at privacy@ravenpack.com in accordance with the GDPRs. You also are agreeing to receive occasional updates and communications from RavenPack about resources, events, products, or services that may be of interest to you.

Data Insights

Read More