Artificial Intelligence: Hype vs Reality recap
On 11 September 2019 we unveiled our Artificial Intelligence: Hype vs Reality report. The event, took a deep dive into the findings of the report and the learnings that can be applied across enterprise when it comes to scaling AI adoption.
For those who were unable to join us, here is a quick recap and we hope to see you at future events.
The evening began with lightning talks from Valeria Cortez Vaca Diez, Data Scientist at Lloyds Banking Group and Andrew Morgan, Head of Data Engineering at 6point6. The talks were followed by a panel Q&A session with the three speakers, moderated by 6point6.
Below is a short summary of each lightning talk.
6point6 on “Artificial Intelligence: Hype vs Reality, the report”
We’ve known anecdotally for some time now that the adoption of Artificial Intelligence in the enterprise isn’t as advanced as many believe; but there hasn’t been much hard evidence to back this up. So we decided to take the pulse of the boardroom. In partnership with YouGov, we surveyed over 1,000 senior executives across UK enterprise to separate the hype from reality; the findings of which have formed the basis for this report.
There were three key findings. Firstly, AI is not on the agenda – there is a distinct lack of strategy, with only 39% of those who have deployed any AI in their business having a strategy to do so again in the future. Secondly, no one is sat in the AI driving seat. Only 16% of UK organisations have someone dedicated to driving AI adoption and strategy. Finally, there is limited deployment of AI in enterprise and few use cases. Only one in five senior executives we spoke to have an idea of the value that is being driven from the AI they have deployed so far.
It’s clear that there’s lots of work still to be done when it comes to enabling AI across enterprise. Businesses must ensure there is a clear strategy that is owned and driven by an AI champion. Alongside this, we have to move to an economy of scale, deploying more AI projects to start seeing tangible results.
Valeria Cortez Vaca Diez, Data Scientist at Lloyds Banking Group on “Hype to Implementation: Crossing the chasm”
Lloyds Banking Group has a rich history of technological innovation spanning the last 300 years, now extending to the use of AI. The Machine Intelligence Programme works to create the right solutions for customers by harnessing data and ensuring AI can be efficiently scaled across the business.
One of the key learnings from the last three years has been how to overcome what has been coined the “infinite loop of sadness”; where a lack of communication and understanding leads to a lack of investment and deployment of AI. The team has found it is essential to have a centralised group of experts in place to tackle the challenges associated with AI in order to successfully scale across the whole business.
To successfully cross the chasm, accountability must be baked into the team’s fabric from the start, and it must work to ensure a responsible AI strategy. There must also be genuine transparency and a focus on explaining not only the technicalities of projects, but also the business use case and how the outcomes will help customers. Ultimately, models must be explicit and there has to be a clear explanation for stakeholders on how it will be used to benefit the business.
Andrew Morgan, Head of Data Engineering at 6point6 on “Leading AI programmes; tips, hints and strategies for success”
When asked “will the world be better with AI?” popular culture would have us believe it will not. From TV shows to books, fear surrounding AI has seeped into C suite conversations; specifically regarding loss of jobs and privacy. As such, adoption is low, fear is high and spending is low – all thanks to this climate of fear that has created the perfect storm for delaying AI strategies.
To overcome this, we need to get broader permission to harness AI. The vast majority of us are happy to use Siri on a daily basis but are nervous when it comes to autonomous driving cars. In the case of insurance, we would be nervous if an AI had the final say over whether we received our life insurance payout; which is why humans still have the final override and say on the matter. However, if we are to see AI adopted at scale in enterprise we need to get broader permission.
The Hype vs Reality report brings up the question of the economics of AI – ultimately the lack of adoption we see comes from a lack of investment. AI is still seen as a risky bet economically. To overcome this, we must stop thinking like the gambler and start thinking like the house. We need to go across enterprise and identify a range of use cases to create a portfolio that will enable us to build an economy of scale to test on. Once this is in place, we will start to see more tangible results at greater returns.
