The AI landscape has changed dramatically over the last few years. Organisations have rapidly been expanding their data stack by adopting data lakes, data warehouses and cloud technologies. But while they have a lot of data, they’re not always sure how to put it to good use – or whether much of it is even valuable.
This new horizon has enabled AI vendors to proliferate. Put aside the fact that some sell basic solutions at best and snake oil at worst; of the group you have left, who do you buy from and when do you decide to build in-house?
There are lots of factors that make choosing between buying and building AI difficult, not least the problem you are trying to solve or the area of your business you are trying to grow. In-house capability, measuring value creation and conflicting prioritisation all contribute to the dilemma.
And for many leaders who have started thinking about AI, the myriad of use cases can be off-putting. What do you want your AI to achieve: small-scale problem solving or large-scale organisational transformation? Both? In one function of your business or across many?
There is a seemingly straightforward way to answer these questions. Many leaders have stuck to the rule: buy market-ready AI solutions where they are available, and build in-house when they aren’t. But the ‘build vs buy’ dilemma isn’t so clear cut anymore, and is actually a false dichotomy.
You could argue, as many internal teams do:
- If you are looking to advance your competitive advantage and have a strong data science team, build.
- If you are looking for a specific solution to your problem and have limited capability in your internal team, buy.
But here’s the thing. You shouldn’t be choosing between build and buy. If your tech leadership is telling you to do one or the other, they’re wrong. If you only build, your technology can take you as far as your in-house resources and expertise. And if you only buy off-the-shelf, you certainly can’t achieve anything competitive.
‘Build’ in particular, bears another warning: just because you can build something, doesn’t mean you should. If you opt to build all of your AI, you’ll end up building the legacy systems of the future. That will stack up to wasted time and money, as well as a future headache you’d rather avoid.
However, thanks to surging interest in AI adoption, vendors are now offering a much wider range of solutions, including products that sit between build and buy, as well as hybrid solutions. Most notably, open-source tools and frameworks, bodyshopping, and AI-as-a-Service (AIaaS).
Open-source tools and frameworks provide prebuilt algorithms with ready-to-use interfaces, and bodyshopping is where you hire contractors to expand your in-house capabilities for the duration of a project. If you’re not in the market for disruptive technology but have a strong data science team, you could try one of these options.
That being said, they are usually limited to when you have a specific project in mind. While there’s nothing wrong with that, it’s probably not going to help you embed performance-enhancing AI into your business. For any organisation where data is a key part of their competitive advantage, their AI should be too.
AIaaS is when an AI vendor licences the use of a system or software to an organisation. The customer subscribes to a managed service which can include: an AI audit, strategy setting, data engineering, product customisation, embedding the AI as well as a transformation programme, and ongoing maintenance. AIaaS gives you a strategic balance of all the options above, and more. It allows you to work in partnership with your AI provider, and blend buying and building solutions. All the while being supported by deep specialists in data science and machine learning.
Think of your AI partner as an extension of your in-house team: they understand your business objectives, KPIs and the stakeholder landscape, and together you’ll co-create the right AI strategy to deliver your goals. It gives organisations the best of all worlds – custom built models and use of proprietary AI engines, as well as the support to enhance in-house data science capability. All of this is ready to scale and generate value quickly, while being tailored to your solution.
By pairing your AI partner’s deep expertise with your team’s organisational knowledge, you can deploy technology that’s fully integrated across your infrastructure. So, not only can AIaaS solve specific problems, it can also help you focus on your competitive advantage and hold your hand through your AI transformation journey.
AIaaS partnership doesn’t fit within the confines of ‘build vs buy’. Instead, it reaps the benefits of both. It directly facilitates ‘build’ by freeing up and supporting in-house data science talent, and ensures what you ‘buy’ will always deliver the best value for your organisation. And crucially, you get the ongoing support to develop the service that you’ve bought so it continuously generates value.
Could Faculty be the right AI partner for you? Learn more about Frontier, our decision intelligence software that’s enabling organisations to improve their performance through better decision making.