“Artificial Intelligence, deep learning, machine learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise you’re going to be a dinosaur within 3 years.” — Mark Cuban

American entrepreneur, Mark Cuban, is definitely provoking. Artificial Intelligence, Machine Learning and Deep Learning are not simple subjects, and we cannot expect the whole workforce to turn into AI engineers. The core idea is rather that these technologies are going to be so deeply intertwined in our lives, that everyone should know its basic principles.

Everyone may not know how to code complex functions into a spreadsheet or know how to build an Excel-like software program, but more people are expected to know the basic principles of the software and how to use it at work (and at home). In the same way, professionals aught to get their heads around the basic concepts of AI so that they can identify and evaluate areas of their work that could benefit from it. And understanding the implications is added bonus!

It is of course possible that building AI models will become easier for everyone, as new tools from companies like Google continue to be launched at a steady pace. It’s hard to foresee exactly how these will be used and if they will ever become as simple as spreadsheets, but the trend is certainly aiming in that direction.

Understanding the basic principles of AI technologies is not hard, and it’s definitely possible for anyone to learn and acquire the necessary knowledge.

The energy sector’s investment in big data and AI have ballooned by a factor of 10 in 2018, according to a new report by accountancy firm BDO. The firm found that mergers and acquisitions involving energy companies and AI startups had soared in average value from around $500 million in the first quarter of 2017 to $3.5 billion in the second quarter.

These figures would make any entrepreneur working in the energy market sit upright! A prolific M&A market and fluid investment landscape are key to a startup’s success. The question is: how does one take advantage of this situation?

The first hurdle for any startup to overcome is data. Data is the fuel that drives AI and often the most important asset for any company. The challenge for startups is that reliable data can be tough to collect or acquire. It’s a classic chicken-and-egg situation: the more the data, the better the product, but the better the product, the more likely you are to gather qualified data. So where should a startup begin?

• Start with a niche and dominate it. It’s probably impossible to compete with large utility companies when it comes to their user data. It’s far more plausible to master a smaller niche and gradually build the largest data set for that particular niche. In turn your data could become a very important asset and potentially a reason for an acquisition.

• If possible, partner up with larger companies that have the data you need. Typically large companies have tons of data but may lack the speed and flexibility to adopt new technologies fast. Many startups succeed by exploiting this situation, selling their AI services to large corporates, built on using the corporate’s data.

Corporates usually own strategic data assets that enables them to deploy AI products that are much more powerful than what a small company could achieve. However, many corporates have encountered challenges along the way to integrating AI. There are a few things that corporates aught to take into account in order to be successful:

• Recruiting and retaining AI talent is very hard. Talent, and AI talent in particular, is very hard to find and retain. And competition from companies such as Google and Amazon makes this especially hard. In order to attract and retain talent, it’s crucial to have an environment that allows employees to thrive. If AI plays a key role (or is going to play a key role) in the overall strategy of the company, it’s a good move to invest in developing these skills internally.

• If you’re planning to build a Data Science Team (or hire external consultants), make sure there’s strong synergy between the business units and the Data Scientists. It’s crucial to have fast, free-flowing knowledge and data sharing between these parties.

• Ensure your company allows fast iterations, both from governance and IT infrastructure standpoints. Developing an AI solution is an iterative process, and data scientists need to be supported by other elements of the company at all times. Having an IT infrastructure in place that allows them to easily test their ideas, will make implementation and your chances of success a lot easier.

Overcoming these challenges often require some degree of change management and IT investment. What’s key is that senior management is aligned with the mission and aware of the potential and added value of said investments. The trait d’union that links the needs of professionals, small and large companies is awareness.

In times of technological advancements and markets disruption, being aware of where the wind is blowing and what this means for your career or your business is the single biggest asset to have.

InnoEnergy has partnered with the AI Academy to offer a professional learning course on AI for Energy Business Managers. The course will empower participants with a clear and deep understanding of AI, its principles, the challenges and the opportunities that it represents for energy players.

This non-technical course is free of hype and rich in practical, hands-on insight into real-world application. The course is a fast track to harnessing the power of next-generation digital innovation for your career or your organisation at a strategic level.

By Gianluca Mauro, Founder, AI Academy and Author, Zero to AI