- 10th July 2018
- Posted by: Manolis
- Category: Blockchain
We are living at a time of unprecedented discoveries and inventions, many of which are being unleashed into our daily lives.
The concepts of AI and automation, data and machine learning, have become mainstream, and are as relevant to the Private Equity industry as anywhere else. This new technology is supposed to be transformative – for the better. But how can we make sure that’s the case?
This was one of the many questions discussed at the Disruption and Innovation Summit on the first day of SuperReturn International in a rather chilly Berlin.
The case for digital
The digital team at EQT, a Swedish firm with offices in 14 countries across Europe, Asia and North America, made a convincing case for companies going digital.
“We have approached the point where technology is exceeding our expectations and exceeding human capability,” explained Olof Hernell, Chief Digital Officer at EQT.
In addition, explained his colleague, Sven Tornkvist, Head of Digital Business Development, technology was now widening the gap between the winners and the losers, with a few leading companies pulling away from the pack.
“Companies that have developed their ability to absorb and diffuse technology become the winners,” he stated.
While new technology brings huge opportunities for consumer and investors, Ignazio Rocco di Torrepadula, CEO of Credimi wonders whether this is “real disruption or not is a different conversation.”
Making the change
Having made the case for change, EQT explained how they had developed a framework to help companies make that change. It included prerequisites like full C-suite buy-in, as well as looking at the culture of an organisation.
“We have learned that doing digital transformation without seriously evaluating your people is very difficult. You need to bring in new talent, look at culture and ways of working, and often, bring in new consultants to support your existing talent,” explained Tornkvist.
With technology, firms had to consider where change was needed and create a data strategy. Digital had to be applied to the entire business model, from sales and marketing to operations and through to the end customer. EQT applies this framework to its portfolio companies to make digital work for them successfully.
“Through digital transformation, we’re looking to make them leaders in their industry. We want digital to differentiate them,” said Tornkvist.
But digital transformation was not an easy ride, and there was no waving a magic wand. It needed a combination of great leadership talent as well as technology. There also had to be some risks taken, and proper measurements and analysis. The end result would be a data-driven, agile, and customer obsessed company, said Tornkvist.
George Danner, President of Business Laboratory said that one of the problems of this new technology was having to demystify it, AI in particular. There was a belief, chiefly peddled by the media, that this drive towards higher levels of automation would cause mass unemployment.
“Certainly there will be disruptive shifts, but most of the conversations aren’t about using automation as a basis to get rid of humans, it’s really about taking those same humans and having them produce two to 10 times more,” he explained.
“Ultimately, this is a different way of thinking about our businesses and creating whole new business models,” argued Danner. “You can take an existing company and you can infuse algorithms in surgical doses in just the right places and make it run better faster and cheaper.”
“What I haven’t seen enough of is a deliberate algorithm-based strategy where you take a company and bring it to a new level of performance by putting math to the question.
“Take something as established as the underwriting process – there is no notion of efficiency and data brought to bear. Could the same people do 10 times the work?” he asked.
Investment opportunities in AI
How did any of this boil down to actual investment opportunities though?
Alex Bangash, Founder of Trusted Insight, said that it was machine learning that had really propelled the ability of companies to take advantage of AI technology. He felt that any industry which touched vision – radiology, dermatology, autonomous driving, precision agriculture – were interesting propositions. Autonomous driving in particular would continue to be a very interesting case.
He also felt that where machine learning had its biggest impact was when combined with an innovative business model. Interestingly, he noted that tech start-ups were overtaken by incumbents who had taken the technology and moved even faster, citing Vanguard and roboadvice as an example.
The fact of the matter, according to Danner, was that this trend towards higher levels of automation was simply unstoppable. “The economics are too compelling,” he stated. “The only question we really have is – how we are going to exploit this new world?”