In the last three blog posts, we’ve seen the opportunities of integrating Artificial Intelligence in the energy sector. On the other hand, AI is a technology that has had such a rapid development that many professionals and businesses face troubles in embracing it. In this article, we want to give a few tips aimed at professionals, small startups and corporates.
“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
This sentence by American Entrepreneur Mark Cuban is definitely provoking, and it doesn’t have to be interpreted literally. Machine Learning and Deep Learning are not simple subjects, and we can’t expect the whole workforce to turn into ML engineers. The core idea is that these technologies are going to be so deeply intertwined in our lives that everyone should know its basic principles.
To make an example, not everyone knows how to code complex functions into a spreadsheet or even worse how to build an Excel-like software program, but everyone knows the basic principles of how to use this software. In the same way, professionals should start to wrap their heads around the basic concepts of ML so that they can foresee areas of their work that can benefit from it, and understand the implications.
It’s even possible that building ML models will become easier for everyone, as new tools from companies like Google start to come out. It’s hard to foresee whether they’ll become as used as spreadsheets, but the trend seems to be aiming at that direction.
Understanding the basic principles of these technologies is not hard, and they’re definitely at everyone’s reach.
Energy sector’s investments in big data and artificial intelligence have ballooned by a factor of 10 this year, according to a new report by accountancy firm BDO (Why big data, AI and renewables are the perfect M&A storm). 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.
This picture should make salivate every entrepreneur working in the energy market: a prolific M&A market and fluid investment landscape are key to a startup’s success. The question is: how do we take advantage of this situation?
The first problem that a startup should think of is the data, being the fuel for AI and often the most important asset for an AI company. The challenge is that it can be tough to gather for a startup. It’s a classic chicken-and-egg problem: the more the data the better the product, and if the product gets better and is used more, as a consequence you gather more data which creates a positive feedback loop. The problem is how to get started in the first place!
The pieces of advice here are two:
- Start with a niche and dominate it: It’s probably impossible to compete with let’s say large utilities companies’ users' consumption data. It’s instead a good idea to master a smaller niche and gradually build the largest dataset for that niche, which can 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 these technologies fast. Many startups became successful by exploiting this situation, managing to sell to large corporates AI services built on top of their own data.
If a startup achieves to get those two things right, the upside may be huge.
Corporates usually own strategic data assets that may allow them to deploy AI products that are much more powerful than what a small company could do, but many of them have encountered challenges on their way to becoming an AI company. Here are a few things that corporates have to take into account in order to be successful:
- Recruiting and most of all retaining AI talents is very hard. Talent is still very scarce, and competition from companies such as Google and Amazon is very high. In order to retain talents, it’s crucial to have an environment that allows them to thrive, which leads to the next points. Open collaboration models and piloting with startups are also good options, but if AI plays a key role in the overall strategy of the company it’s a good choice to invest in internalizing these skills.
- If you’re planning to build a Data Science Team (or hire external consultants), make sure there’s a strong synergy between business units and Data Scientists. It’s crucial to have fast knowledge and data sharing between the parts.
- Make sure your company allows fast iterations, both from a 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, and by an IT infrastructure that allows them to easily test their ideas.
These challenges often require some degree of change management efforts and IT investments to be put in place. What’s key is that the upper management is aligned with the mission and is aware of the possible gains of such a major technological advancement.
The trait d’union that links the needs of professionals, small and large companies is the awareness. In times of technological advancements and markets disruption, being aware of where the wind is blowing and what this means is the single biggest asset to thrive.
For this reason, we organized a one-hour webinar, where we will demystify wrong assumptions around AI, and discuss how it can be integrated in the energy sector in a practical, hype-free way.