How to ride the AI mega-wave

Last year had a transformative effect on Artificial intelligence (AI), with tech giants such as Google, Apple and Facebook all spending exorbitant amounts on acquiring AI start-ups. The aim for these companies was to bring the niche AI expertise of these start-ups in-house, signalling that these industry titans recognise the importance of having top notch AI-projects. AI is set to revolutionise how we live our lives, including how we work, shop and socialise, and businesses around the world are striving to ensure they are leading the way.

While AI is set to impact many areas of our lives, one of the most profound changes will be to our workplaces. Recent research showed 87% of workers believe their job will be changed by AI within the next three years. Managing this transition to the seamlessly integrated workplace of the future remains a gargantuan task for many businesses. In particular, most firms face three distinct challenges; tackling the growing AI skills crisis, dealing with limited experience with AI projects, and coordinating humans to work harmoniously with intelligent machines. To successfully pivot towards an AI-friendly working environment, these issues must be resolved.

 

Solving a staff shortage

A shortage of qualified staff is currently the biggest problem companies face when it comes to accelerating their adoption and development of AI technology. Recent analysis from jobs site Indeed shows that there are at least twice as many job openings in artificial intelligence as there are suitable applicants, with the number of roles in the industry rising by 485% in the UK since 2014. The struggle to source sufficiently talented candidates for AI engineering roles is not exclusively a problem for tech giants like Facebook or Google. As AI goes mainstream, every industry will need qualified staff that are able to develop and work alongside AI. As is often the case with hot new technologies, AI specialists are extremely rare and finding people with the right skills can prove a near impossible task.

Businesses need to look internally and focus on upskilling existing members of staff in order to ensure they have enough in-house knowledge. Doing this not only eases the skills shortage, but also helps staff get to grips with how to operate and work alongside AI systems, which in turn reduces the possibility of future job displacement. AI-proficiency is likely to become a necessity for all employees at some point. For the time being, when looking at potential new employees, key traits to focus on should include a background in data-handling, analytics or statistics. The learnings gained from working in these areas are essential when working with AI systems. Upskilled employees from these types of backgrounds are far more likely to successfully handle areas such as the design of agile data structures or modelling the type of real-world uncertainties faced by AI algorithms on a daily basis.

By investing in upskilling existing staff and assisting them to become comfortable with the basics of AI, companies will be able to better focus their hiring resources and concentrate on filling more specialised roles. This can either take the form of permanent hires or by bringing in external consultants who can enhance expertise or provide on-demand advice. Additionally, by accepting from the outset that employees will require significant training, companies will be free to prioritise cultural fit and adaptability in the recruitment process over technical skill, which can help to reduce employee turnover.

 

Bring in the experts

Building the right team is only half the battle. AI projects are still in their infancy, and as a result there is increased possibility of significant misunderstanding when rolling out a new initiative. It is therefore imperative that at the beginning of any new AI project, a consulting team is established that consists of business domain and data science experts alike. By assessing the business requirements, technical challenges and desired outcomes for all stakeholders, this specialist group of consultants can not only ensure projects stay on track but also that engineers deploy the correct level of automation for the task in question.

This is especially important during the early stages of AI adoption. The attention required in later stages will lessen as the technology matures and begins to rely more on open source frameworks and platforms. As a result of this, businesses will have to do much less coding from scratch as a result, though, until this maturation comes to pass, it’s much better to take the time at the start of the project to ensure resources aren’t being wasted or misdirected than to discover this halfway through. As such, many firms launch AI projects with a small team, letting them evolve and scale up as opposed to trying to blanket introduce AI to the entire company in one go.

 

Humans vs Machines

After AI projects have been successfully trialled with select teams within a business, the roll-out to the wider company needs to be managed. Executives will need to assess where they can place human employees to maximise efficacy as AI becomes more sophisticated and integrated into daily life. These decisions will entail determining whether tasks should be undertaken by humans, are better left to machines or can be best executed by combining the two.

Take project management for example – how could AI alter and improve execution of this crucial function of any business? One example of AI’s capacity to transform project management performance is to introduce a platform that functions as Project Coordinator. Such a platform would carry out day-to-day administrative tasks and free up employees to focus on more creative ideas. Alternatively, companies might prefer simply use AI as an assistant to automate simple tasks while retaining a skilled Project Manager to oversee the wider project. The AI assistant could eventually also offer recommendations as it develops an understanding of project performance along the way.

These are only a sample of the possibilities that AI can provide. However, they are only achievable if companies have a healthy balance between employees with sufficient technical skills and a corresponding corporate culture. The introduction of AI is a continuous process and many departments may not feel the effects for a few years to come, however, it is inevitable that they will recognize the benefits at some point in the near future. Therefore, executives need to think about how to survive the shift towards AI or risk getting left behind by more digitally-savvy competitors.

 

The article was originally published on The C-Suite and is reposted here by permission.

Frank Palermo

Executive Vice President - Global Digital Solutions, Virtusa. Frank Palermo brings more than 24 years of experience in technology leadership across a wide variety of technical products and platforms. Frank has a wealth of experience in leading global teams in large scale, transformational application and product development programs. In his current role at Virtusa, Frank heads the Global Technical Solutions Group which contains many of Virtusa’s specialized technical competency areas such as Business Process Management (BPM), Enterprise Content Management (ECM) and Data Warehousing and Business Intelligence (DWBI). The group is responsible for creating an overall go-to-market strategy, developing technical competencies and standards, and delivering IP based Solutions for each of these practice areas. Frank also leads an emerging technology group that is responsible for incubating new solutions in areas such as mobile computing, social solutions and cloud computing. Frank is also responsible for overseeing all of the Partner Channels as well as Analyst Relations for the firm. Prior to joining Virtusa, Frank was Chief Technology Officer (CTO) for Decorwalla, an emerging B2B marketplace in the interior design industry, where he was responsible for the overall technology strategy, creative direction, and site development and deployment. Prior to that, Frank was CTO and VP of Engineering for INSCI Corporation, a supplier of digital document repositories and integrated output management products and services. Prior to INSCI, Frank worked at IBM in the Advanced Workstations Division, and took part in the PowerPC consortium with IBM, Motorola and Apple. He was also involved in the design of the PowerPC family of microprocessors as well as architecting and developing a massive distributed client/server design automation and simulation system involving thousands of high-end clustered servers. Frank received several patents for his work in the area of microprocessor design and distributed client/server computing. Frank holds a BSEE degree from Northeastern University and completed advanced studies at the University of Texas.

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