2026 Predictions: It’s the Year of Technical Debt (Thanks to Vibe-Coding)

Salesforce has been around since 1999, and 27 years in the tech industry means 27 years of technical debt. In recent years, the cloud company has been busy, moving fast, and well, you likely know how that expression ends.

On the one hand, tools like Agentforce Vibes can automate reviewing processes, bringing to light what needs improving, how to improve it, and where else to look for similar imperfections. ‘Vibe coding’ tools have made coding more accessible than ever, but the flip side of this coin is that, as long as manual processes become problems that automation can provide a quick and easy fix for, corners get cut.

In the Salesforce space, newer tools can lead to a proliferation of newer problems. As Agentforce and LLMs increase their capabilities, so too does that technical debt mount up. “If you can build things faster, that doesn’t necessarily mean you’re going to build better things faster. It just means you’re going to make more, faster,” says Salesforce MVP Hall of Famer and Founder and CEO of Groundwork Apps, Paul Battisson.

SF Ben predicts that tech debt is going to be a big theme of 2026, both in terms of utilizing AI to tackle it – and how increasing tech capabilities (including from AI) will lead to greater buildups of technical debt across Salesforce orgs. Let’s take a look at what this means.

“It’s Going to Blow Up in Their Faces”

Paul says that, in order to actually gain value from AI – including Agentforce – the quality and connectivity data is a central issue, so companies that have been using Salesforce for more than a decade will need to tackle their technical debt by migrating from legacy work.

Paul told SF Ben: “My big prediction for this year is really that I think we’ll see a renewed focus on technical debt management, which is as sexy and exciting as it sounds, but I think one of the big problems that people are finding with getting value from Agentforce and AI is the quality and connectivity of their data and their systems.”

He mentioned that he has a client who “wants to do AI”, but in terms of actually translating that vague desire into reality, we need to establish what is meant by “doing AI”, firstly, but also getting data in the right place.

Paul said: “We know we don’t have the structure. We know we don’t have all the things that are needed to really do it properly and at scale, and that there’s a lot of legacy. Salesforce has been used by companies for over a decade; it’s more common than not. So there’s a lot of historical work in there that people are having to move and migrate away from.”

He added that the emergence of Agentforce Vibes and similar tools might be able to help with the problem of reviewing what you already have, where it needs improving, how to improve it, and problems of scalability.

Salesforce MVP and Marketing Champion, Vicki Moritz-Henry, echoed some of Paul’s sentiments, warning that over-reliance on agents and insufficient upskilling poses a risk of future difficulties.

She also agreed with the possibility of 2026 being “the year of technical debt,” with adoption of AI, particularly for metadata and coding, possibly heaping up technical debt on top of existing issues – with consequences possibly becoming more apparent this year or in 2027.

Vicki said: “I think we had talked about tech debt already last year quite a bit. I heard a lot of conversations and had them myself about tech debt being what you needed to solve before going into AI.

“I think this year, now that there’s more adoption… we’re starting to see more AI projects, we’ll see the outcome of maybe not reducing that tech debt before going into AI, and then, now that we can do things with our metadata with AI, then we’re going to be producing tech debt on top of that.

“It’s more of a negative one, but I would definitely say that tech debt is going to be a topic, if not this year then the following year, once we start to see these projects coming through.”

CRM advisor Gabie Caballero said that smart companies will clean up their data and make the transition to properly using AI slowly, while those who move too fast risk greater technical debt and failure.

She said: “I think that smart companies will do the work in front to clean up their data to get ready to bring in the agents, and maybe they’ll do a slower transition of cutting jobs. Maybe they’re not going to cut everybody. Maybe they’ll keep some key people in the loop, but they’ll probably use the AI for the more junior jobs.”

Gabie added that customer service was an easy use case, because the bot can answer questions, but other companies that “try to go too fast” are going to be more at risk of stepping on proverbial landmines.

“Those traps of technical debt – it’s going to blow up in their faces, and then they’re going to realize, yeah, maybe they saved some money, but now they’re going to have to go back out and rehire everybody to fix the mess that they made.”

Companies that implement AI the right way, making sure their data is all in order, and going “low and slow” would benefit – but there’s still so much hype around artificial intelligence that people feel “frantic”, thinking they have to jump on agentic AI or risk falling behind, Gabie said.

“If you don’t take the time to do it properly, then you’re going to fall into those traps,” said Gabie.

READ MORE: The Technical Debt Every Salesforce Org Needs to Fix in 2026

Final Thoughts

We might consider the question of AI and technical debt the same way we think about AI and Salesforce security. On the one hand, there are so many opportunities for businesses to improve their org security using AI – but it’s a double-edged sword, because the opposite is also true, with malicious actors also capable of capitalizing on AI to get what they want.

So too, on the technical debt question, are there opportunities to make improvements, but also pitfalls to avoid. If foundations and legacy issues are addressed, then potential landmines can be avoided – but if businesses use AI tools to move fast, then, yes, they’re going to break things too.