The focus and investment in AI is increasing
A 2017 survey of more than 3,000 executives by MIT Sloan Management Review and Boston Consulting Group revealed that almost 85% believed that Artificial Intelligence (AI) will give their companies or help them maintain a competitive advantage .
Non-public AI companies raised $15.2B funding in 2017, an increase of 141% compared to 2016 .
Large companies such as Google, Facebook, Amazon and Microsoft are all heavily investing in AI. Google spent $660m acquiring British AI startup DeepMind in 2014, which has been in the spotlight for beating the World Champion at Go and for an exciting (yet controversial ) research project with the UK National Health Service.
AI is thought of as a panacea (or at least, there is a Fear of Missing Out — FOMO!)
There is a huge amount of excitement (and hype) about AI. Companies, big and small, are rushing to make sure they are ‘doing’ AI and startups make sure it’s mentioned in their pitch decks. Everybody wants to do AI and this is positively reinforced because everyone else is doing it.
However, the above survey also revealed that only 39% of companies had an AI strategy in place and that only 20% had incorporated AI in some way into their offerings or processes .
Our experience has been that companies find it difficult to identify what exactly they need from AI and what ‘good’ looks like in a solution. We think that this is due to AI being a relatively new technology with its share of complexities and some of the surrounding hype around AI misleading many.
In some cases, we find that the IT departments have been given the task of ‘doing AI’ without much direction from the rest of the company.
We see three key challenges and points to note:
- AI for the sake of AI — the approach to AI should be driven by the business challenges and objectives and not by what AI can or can’t do
- Automating inefficiency — AI, similar to other technologies, is sometimes used to automate inefficient processes or business functions that are not adding value which ‘bakes in’ the problem
- Getting disillusioned with AI — due to the above two reasons and others, companies can give up on AI and its results. This could lead to missed opportunities, especially as the ‘learning’ nature of AI solutions require companies to take a long-term view
Working with a technology partner
Tools are becoming commoditised and open source. For example, TensorFlow is an open source machine learning framework developed by Google. However, ‘off the shelf’ solutions and generic APIs can’t solve all the problems by themselves.
Not all companies have resources to develop their in-house AI capabilities. Scarcity of talent and the need to implement within a certain timeline may also be challenges. Working with a technology partner could be an option to consider.
We suggest a four step approach to working with technology partners:
Taking a structured and phased approach based on business objectives and challenges will enable companies to make the most of the huge potential AI has to offer.
This type of approach will also enable companies to focus time and resources on areas where AI could add the most value and to identify where AI is not an appropriate solution.
ConscientAI works with clients across different geographies and industries. We have a pragmatic way of working that helps our clients identify key problems and understand the potential benefits before investing in the right solutions. Get in touch for a chat — email@example.com
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