AI Vendor Lock-In: Building Your House On Sand

AI vendor lock-in is a very real concern that's looming over the excitement of artificial intelligence innovation. How can you leverage this revolutionary technology without the risk of becoming trapped on a sinking ship?

Vendor lock-in is always a concern whenever you attempt to leverage innovative technology. The latest iteration of this age-old concern is the revolution of generative artificial intelligence (AI).

Enterprise architects are under tremendous pressure from leaders to implement AI in their tech stack for fear of missing out on a competitive edge. Yet, rushing blindly forward into an AI contract could lead to regret when technology progresses and you find yourself tied into software that's suddenly obsolete.

After all, AI was simply a dream just a few short years ago. Who knows what innovations are on the horizon that could supplant it?

So, how do you avoid vendor lock-in when it comes to AI, or just in general? The key is having complete intelligence on your application portfolio and IT landscape, so you can tell where AI is being used, where it could be useful, and when its use isn't worth being locked into a contract.

To start gathering data on your application portfolio, book a demo of the powerful LeanIX Application Portfolio Management platform:

In the meantime, let's look more closely at why vendor lock-in is such a risk in leveraging AI. More importantly, we'll consider how you can avoid it.

Lessons Learned From Cloud Vendor Lock-In

Artificial intelligence (AI) vendor lock-in is concerning, in part, because many of us remember the sting of being tied to cloud contracts in the rush to leverage that technology. At one time, cloud computing was the technology buzzword for the market and early adopters faced such teething troubles that many are now trying to turn back towards on-premise options.

READ: Are The Benefits Of Application Migration Becoming Cloudy?

A third of cloud migrations failed outright, while 75% of successful cloud transformations went dramatically over budget. Those that managed to complete a cloud migration faced further issues, with 60% of organizations paying more than they expected for their cloud services and more than half yet to see "substantial value" from their migration, according to PwC.

As such, it's no wonder 451 Research found 54% of businesses had moved workloads or data away from the public cloud following a migration. Yet, that was only the companies that were easily able to do so, as more than 80% of cloud-migrated organizations face vendor lock-in issues, according to Gartner.

So, you can see that organizations may be hesitant to rush into restrictive contracts with AI start-ups that could lock them into a technology dead-end in a few years' time. This is particularly worrying given recent headlines.

Watching The Almost-Collapse Of Open AI

Rapid adopters of the very first publicly available generative artificial intelligence (AI) platform watched with bated breath in November as chaos erupted at OpenAI, the primary player in the emergence of generative AI. CEO and co-founder Sam Altman was fired for not being "consistently candid" with the board, while Chairman and co-founder Greg Brockman stepped down.

Any controversy was likely to cause concern about such an influential vendor, but Altman was largely credited with the success of OpenAI and his loss shook consumer faith in the company. Over 500 key OpenAI employees then threatened to follow Altman to Microsoft, where he had found a new position.

Three days after Altman was fired, all but one member of OpenAI's board resigned and Altman agreed to return as CEO. The panic turned out to be a flash in the pan, but anyone who had been quick to adopt OpenAI's large language model (LLM), ChatGPT, into their tech stack had a few sleepless nights.

Between the ongoing issues with cloud vendor lock-in and this wobble in OpenAI's strategic leadership, many began to worry about AI vendor lock-in. If AI doesn't live up to its promise, or if newer, better AI appears sooner than expected, companies that adopt ChatGPT early could be unable to leverage upcoming innovations.

The Risks Of AI Vendor Lock-In

Artificial intelligence (AI) vendor lock-in is a natural consequence of business models that may not be agile enough to keep up with modern technology. Consumer software-as-a-service (SaaS) models are usually pay-as-you-go with rolling monthly contracts, but this isn't going to work for a scaled enterprise IT landscape.

By having an IT infrastructure with interdependent software, you automatically leave yourself vulnerable to software falling out of support, vendors going out of business, and new technology arising that you can't immediately leverage. In the modern age, however, this can happen in a matter of days, rather than months or years, as we saw with the sudden appearance of generative AI.

While cloud technology is more flexible and less dependent than on-premise solutions, it's still nearly impossible to build a functional, large-scale tech stack that's modular enough to swap out applications whenever it might be beneficial. Even if you could, that pace of change is likely to frustrate your employees who will need to constantly retrain on new toolsets.

An alternative is to invest in a managed, monolithic system like Microsoft Office 365 or Google Business Suite that will always offer inter-functionality between applications. This, however, locks you into reliance on one vendor whose monopoly can then allow them to increase prices without warning or reduce functionality.

Companies like Microsoft will try to incorporate their own versions of emerging technologies into their monolith offering as soon as they can. However, we can see from the way that Microsoft and Google are still trying to get their respective Co-pilot and Bard AI tools to match functionality with ChatGPT that this could take time.

While your monolithic stack is working on feature parity with ChatGPT, OpenAI is achieving greater and greater innovation with their tool. Yet, if another company creates another innovative tool, even ChatGPT customers may be unable to leverage it due to lock-in, so no-one is safe.

Using LeanIX To Escape AI Vendor Lock-In

Artificial intelligence (AI) vendor lock-in is, to an extent, unavoidable. When only a few vendors are offering a tool that offers a competitive edge in the market, companies will need to accept their contract terms or face being left behind.

The key here, however, is making the decision about whether vendor lock-in is worth the advantages that the tool the vendor supplies offers. You need to know the potential consequences and the precise value of the AI tools you're considering.

Not to mention, if you do need to pivot your toolset to encompass an emerging technology, then you'll need to know how to extricate it from your tech stack without breaking anything. You'll also need ready access to the contract terms and information about your AI applications to get fair warning of when obsolescence issues are going to occur.

Using this intelligence, you can confidently enter into AI vendor contracts without fear of lock-in. That's why you need the LeanIX platform to document your entire application portfolio, track the applications' lifecycles with vendor information, and enable you to build a road map for AI adoption.

Using LeanIX, enterprise architects can become enablers of AI, rather than the ones blocking innovation for fear of vendor lock-in. To find out more about how LeanIX can enable AI adoption, book a demo: