Can adding AI cause tech stack fragmentation?

Business leaders and researchers considering layering on artificial intelligence often ask, “Can adding AI cause tech stack fragmentation?” And, in some instances, the answer is yes. So today, we’re going to discuss tech stack fragmentation and how it can be avoided with providers who offer an all-in-one platform like Agxio.

What is tech stack fragmentation?

Tech stack fragmentation is a state where businesses have too many, overlapping, isolated, redundant or obsolete technology platforms within their estate. The process of streamlining this is called tech stack consolidation. Dealhub explains, “Tech stack consolidation [happens] by reducing the number of software tools and platforms in use. It involves identifying and eliminating redundant or overlapping technologies, integrating essential tools, and often moving towards a more [centralised] or unified system.” If you’ve not yet added AI to your stack, launching with just any provider may cause more harm than good.

Why fragmentation can be compounded by AI

Here are some reasons why AI can cause or make tech stack fragmentation worse:

• Siloed tools for different AI functions – If you don’t find a single-platform provider, you could end up with separate tools for AI-driven analytics, customer service bots, personalisation engines and more. This increases the likelihood that you’ll be making decisions without access to all the data in a single view.
• Integration issues – To link in what you already have, some AI vendors may require custom APIs, data pipelines and middleware, adding complexity, costs and extra failure points to your stack.
• Inconsistent UI – Plus, all these different AI tools will have their own interfaces and design philosophies, meaning your team needs to learn several ways of working just to do the same tasks.
• Security risks – Lastly, for GDPR and other compliance frameworks, you’ll be increasing your risk profile with additional suppliers. Each one will have different systems for managing data and privacy; something you’ll now need to account for.
• Redundancies – Lastly, you might be paying for features in one platform that you already have somewhere else, duplicating computational resources, ML pipelines and models across platforms; wasting time & money when you’re meant to be saving it.

How a single AI platform solves this

Agxio provides a unified AI stack with centralised AI workflows where you can build, train, deploy and monitor your AI models in one place. There’s consistent data governance, too. This includes unified access control, auditability and full compliance with regulations, as standard. Having full oversight also allows for seamless scalability. You won’t need to stitch together different services; instead, you can scale within the same architecture in line with your business needs. With shared libraries, tools and integrations across teams, there are fewer knowledge silos and actions are taken on all available data. As a result, there’s a lower total cost of ownership through reduced infrastructure and better data quality, driving your decisions in the day-to-day.

See how that works in practice by looking over our recent work in biotech, agriculture and more.