AI for Business Growth (Part 2)

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This is Episode 2 of the "Embedding AI in Business" review: a key conversation between Paul Daugherty, Accenture’s Chief Technology and Innovation officer and Azeem, founder of the exponential view.

This exchange was so dense of lessons learned that we simply could not fit into a single post!

Here's the lessons learned of this episode:

1. AI has arrived as a torrent.

AI has grown so fast in the enterprise world, for three main reasons: firstly, through AI we can solve customer problems faster. Secondly, data and infrastructure to implement AI is already there. Finally, access to AI is not democratised: all the discoveries in the areas of Machine Learning and Computer Vision

2. Start with AI now and beat competition with the data network effect

Machine Learning needs data to learn models from. If your models are performing well in your products, customers will use them more. This will result in more data created by your customers that you can use to further improve your AI. This is what we call AI locking loop: A reinforcing effect, a winner-take-all approach that privileges the companies that start early with AI.

If there's no data at the core of the DNA of your company, you will struggle in the medium and longer term to compete with the players leveraging the data network effect.

 
 
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3. Uses Machine Learning in every part of your business.

Amazon adopted this strategy early after the Deep Learning revolution in 2012. Jeff Bezos in one famous memo stated: " we need to embed AI in everything we do".

They started with a program that would train all their managers and senior execs on the capability of Machine Learning and AI. Moreover, in business plans of every amazon program manager they introduced a mandatory item: how can I improve my part of the business through AI.

4. Next wave of AI business value is created by combining Symbolic AI and Machine Learning

For a lot of business applications, especially in verticals such as Manufactoring and Talent Management & HR, the name of the game is to take into previous knowledge and rules devised by human experts. For this reason, Adam and Azeem argue that traditional AI methods as symbolic graph reasoning are regaining a "raison d'etre" and complement more modern learning techniques.

Listen to the full podcast.

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