The Economy 

Economic predictions expect the UK GDP to fall by around 11.5%, much more than the other large producing countries in Europe. Productivity has definitely been hit hard due to restrictions and legislation on companies, changing (maybe forever) the way they operate. 

So what does this mean for tech and Ai? 

The UK has incurred 730,000 unemployed between March and July. People from a wide range of industries have lost their jobs. 

With more working from home and many reliant on funding to stay profitable or even operational in this time, new ways to operate and ensure efficiency are more relevant than ever. 

What has AI done during lockdown?

As with any demand in analytics and predictive systems, AI has been heavily involved. Using Machine learning along with other predictive systems, multiple AI engines have been used to model, evaluate and provide real information on how Covid has been spreading, how it effects people and how it has been receding. Every country has been operating quickly to engage civil data scientists and engineers, refocusing them onto the big data analysis of Covid using AI. 

Hospitals have been using the technology more too, from resource management to analysing diagnosis, AI has been contributing to care and health in a big way. 

The American National institute of Health (NIH) has been using AI to create new tools to diagnose the virus using the ever increasing amount of data that has been generated since the initial breakout. 

Features have been exposed by using AI to analyse the images of infected lungs, along with the data attached to the prognosis. This has allowed two things, better identification of Covid-19 from analysis of CT scans of lungs and it has created new features of how Covid infects and what the prognosis of patients can be. 

This is leading to much faster assessment and can have big repercussions on how viruses get analysed in the future. 

Furthermore, with the huge time pressured demand of a vaccine, AI has been used to assist in the generation of drug development. Since may drugs take many years and billions to formally create and get into circulation, only 12% of which gain approval, the pressure on drug discovery has never been higher. 

AI has been used to involve active learning into the process to reduce costs and increase the time taken to reach biological human testing. The project data is integrated into a unique AI engine that utilises generative design to plan the drug testing; active learning then is used to add speed and efficiency to the outputs of each trial. After the entire cycle is completed, the overarching system learns from the outcomes and generates new project data to reinforce new projects concerning iterations of the same drug. 

With a demand for improved efficiency, speed and cost saving AI can offer real benefits that have improved how we have been able to deal with Covid-19. These steps in the right direction will no doubt be able to impact the future. 

A long road ahead?

Artificial intelligence along with other new systems that can push the barriers of what is possible require investment. It is clear that with the reduced capital investment that will occur over the coming months that this industry could endure a hit if the value of the systems do not out weigh the benefits.  

The value is the important factor, technology systems used to be nice to haves, systems that were more a USP than a part of a company. Belief in this view negates the real value of software, more specifically artificial intelligence. 

With the exception of some fringe AI systems the vast majority output efficiency, which manifest in cost saving. It’s a natural thing for a system that can think 1 million times faster than a human, can scale ensuring little to no cost and in some cases operates better and longer than any human. 

Yet this is not fully how the technology is perceived. In 2018 a LinkedIn survey found that only 30% of people who were using AI were doing so because it “saved money”. 

Views that the process of AI development and delivery will be long and complex, which would lead to a black box that sole purpose of sounding good to investors are alive and well in the corporate world. Big companies need to switch to becoming reliant on the tech to add real value post lockdown.

IBM saved $300 million using the technology in 2011 in hiring related productivity.

What about smaller scale companies? True accord are a San Francisco based debt collection agency that started by integrating technology to give them a cutting edge, they use AI to engage with a larger audience and provide scalable services, more so that most small companies because they are using AI to provide real cost saving efficiencies that can scale. 

Start-ups like True accord and smaller companies can leverage AI tech to get an edge, or in modern times, stay afloat. 

The smaller the company, the better an AI integration can be, in which case the most cost effective. Huge solutions are rare because the system being integrated is “more expensive” because there is typically a much larger cost in formatting the big data at the beginning, to allow the algorithms to run. 

ETL or extract, transform and load is where a large proportion of the time and effort in any AI project is seated. Big banks with huge amount of legacy data have to pay the price.

In a startup or small company, the systems can be built in the foundations, the big data is yet to come so it gets accounted for before it requires huge data lakes and teams of data scientists to transform into something remotely useful. 

This then turns the view of what AI can offer into something much more simple, a cost investment. A cost investment that is becoming more and more relevant in economic downturn. 

Froward thinking organisations are needed. Because the AI technology can be vital to provide forward thinking efficiencies and cost savings that will make a huge difference once Covid-19 has receded from our lives.