Last year the NeurIPS conference -- one of the biggest machine learning and computational neuroscience conferences in the world -- accepted nearly 2,000 papers about machine learning. There is some concern in the academic community that too many papers are being published about machine learning, which is creating too much “noise” around the concept.
In other words: do business leaders really know how machine learning can impact their specific industry, or do they just know it’s a hot buzzword of the moment?
Many. It’s about massive data consumption for better decision-making. One of the most cited papers on machine learning aimed at a business audience, which dates all the way back to 2001, lays it out pretty clearly: “data analysis and pattern discovery.”
That said, only about 15% of enterprise companies claim to be actively using machine learning programs right now, with about half in that poll saying they are “exploring” what can be done with ML programs.
We should also stop and quickly define “AI” vs. machine learning. Most get it by now, but not everyone. AI applies machine learning. When you talk about “AI,” it’s a bigger idea where machines can execute smart tasks. To arrive at that ability, it applies machine learning, deep learning, and more.
By 2022, businesses are likely to spend about $78 billion on machine learning platforms and applications, and there is return to be had: ML is expected to add $2 trillion in value to manufacturing and logistics fields, for example.
You can also use it at a micro-level to make more money at the margin in your business. The RedAI app is a good example. That’s tailored towards the fast-moving consumer goods (FMCG) industry, and it allows sales reps to enter stores (POS), take photos, and have their shelf positions analyzed instantly.
As detailed here by the RedAI app, as just one example, there are roughly 3,340 stores nationwide in Austria. What if you could increase volume sales transaction by five Euros per location on a daily basis, via the sales reps being equipped with better technology? That would be an annual revenue increase of over 4M Euros. That’s good money and can signal future investment opportunities.
Honestly, a lot of the problem with AI and machine learning for enterprise comes down to finding real expertise and real solutions that work. That is where companies can struggle. They obviously want to increase revenue, and they have ideas about how to increase revenue and make sales reps and other departments more effective, but they get caught up in current projects or fall for flashy technology that does not do what it has promised. And then the priorities get shifted and the revenue growth is not there, or it’s there but not at the level, it could be.
You need to work with partners that legitimately understand AI and machine learning and can approach the problems and solutions in the space with a DevOps mindset.
The problem is not that you lack ideas, and not that you lack strategy. The problem is often that you work with the wrong people, technology, and processes in terms of executing those ideas and those strategies.
The reality is that there are a lot of people in the world now claiming to be experts in these new technologies -- but they’re not. Machine learning is evolving as a field every day. To capture the most business benefit from it, you need a team of actual experts -- and you get that with outsourcing to a team that has already worked on machine learning projects.
Then they become an extension of your team and they are consultants to you at the same time, because they have knowledge from previous projects with other companies. They’ve seen what strategies worked in other places.
You get a consulting partner and a partner that executes on the work at the same time. This helps you keep up in the market.
If you are curious about what factors to consider when selecting a consultative outsourcing partner, download this checklist we put together. It will walk you through what you need to think about to get the most value and cost-effectiveness from the relationship: