When to buy and when to build AI

One of the most important questions when starting to work with and implement AI in your organization is also one of the most complicated to answer: Should you buy off-the-shelf AI products, build your own in-house or have it built custom by consultants?

There’s no one size fits all answer here, but there are some considerations that can help you to understand what is best for you. I’ll try to go through the considerations and let you decide in the end what suits your business the best.

Is AI strategic for your business?

First of all I believe you should ask yourself: Is AI development a strategic feature to my organization? That can be a bit of a vague question so I’ll boil it down to this: Will AI solutions provide you with a competitive advantage that you will try to protect and keep improving to stay a head?

If the AI is just something that is meant to make an improvement that it’s likely your competition can easily copy then you should definitely buy the solution off-the-shelf or have made from experts you hire in. Building up the needed know how and organizational capabilities to make an AI that is only here for a small tactical advantage is not necessary. That will take your focus away from the more important problems. So ask yourself the hard question: If the business would need to do cutbacks, would you keep investing in building your own AI as a strategic priority? If not, you should consider not to do it in the first place.

On the other hand if you believe that one or more AI-solutions can be a competitive advantage that your competition can not easily copy then you should try to build it in-house. In this case you have to be clear on what makes it hard for your competitors to copy. Do you have some access to data that they don’t? Do you have a better position to build the AI capability or something else? Make very sure that you are actually in a position to be competitive here. If not, your competition will copy you by buying from an experienced vendor at a lower cost than you paid to build your own AI.

Research the market

You will be surprised how many off-the-shelf AI solutions there are out there that solve all kinds of problems. People tend to in my experience not do the research and end up making expensive investments that take forever to get done and still it won’t compare to the products already on the market. You really have to have scale to make a business case for building your own solution when there’s already a lot available out there.

I actually once met someone building a solution in-house that was exactly what my AI company was doing. We needed massive scale to get anywhere near a good business case and yet these guys tried to do it themself. We had more than 14.000 business customers at the time and this one business wanted to make the same AI for their business only. They of course had to close their project since it was too big an investment but they still spent a lot of money. Once a project has been kicked off it can be hard to pull back since a lot of ego and prestige can go into corporate projects.

In for a penny in for a pound (of AI)

I have a rule of thumb that never fails me. “When an organization does something it doesn’t do regularly it will execute it poorly”. I made this rule of thumb to explain to myself why very competent organizations sometimes completely flops relatively simple endeavours. I guess the reason is that working in a new domain for an organization is not only not supported by the current processes and culture but might require the organization to work against them. Whatever the reason I see it consistently and I also see it being the case with AI. If you don’t do AI projects regularly you will see massive overhead and probably fail it. So if the frequency of your AI projects are low you should probably look to outsource as much as possible. This is not an attempt to scare anyone away from AI projects, but it takes effort to build the AI capability and that’s a conscious choice you have to make here.

Size matters

AI projects require a minimum investment that is usually larger than traditional IT projects. In AI the skills from engineers, machine learning developers, data scientists and product managers are quite unique. So as a result your organization just has to be a certain size for in-house AI projects to make sense. AI usually also is a trial and error workflow that doesn't promise revenue or profit right away.

There’s no fixed amount of employees or revenue but when the AI team has to be 4-5 people at minimum then you probably shouldn’t do it before you can handle a team of that size for a while not providing any revenue or cost saving for a good while.

Get your data straight

Data is a big part of many AI projects and I always recommend that you get your data straight before you go into the actual AI development. In my mind it’s more important(And more competitive) to get a smooth data operation with low costs and high quality data. I would always prefer to get the data operations in-house and the AI-development is second priority. Getting the data operations right is more of a competitive advantage than building the AI. It’s like supermarket chains competing - The chain with best purchasing of goods and more low cost warehouse operations can provide cheaper consumer prices and are more competitive. Data is the same way. If you can get better data at a better quality and a lower cost, your AI projects will be superior to your competitors even if their AI capabilities are superior to your businesses. So make data the priority if you have to choose.

Building AI is getting easier

One last thing I think you should take into account is that AI projects are getting easier and the barrier to get started is getting lower. AI used to be a very difficult domain to work in, requiring both Phds in data science, machine learning engineers and often thousands of hours of coding to make a useful AI. Today a lot of that can be done at a much lower buy-in with techniques such as Transfer learning and AutoML. It also seems that the bar for getting started is getting lower and lower. As a result building AI in-house is clearly becoming more accessible and with time more business should have a go at it.

That’s it. From here, the decision is yours.