In February 2026, $285 billion evaporated in 48 hours from the valuations of SaaS giants (Adobe, Chegg, Intuit, etc.). Jefferies analysts dubbed the event the “SaaSpocalypse”The reason? New AI models that make developing in-house tools radically simpler, faster, and more relevant than subscribing to a generic SaaS.
For CEOs, CIOs and CTOs, a strategic question arises: Continue paying licenses to publishers who don't understand your processes, or build a custom, AI-powered tool perfectly suited to your business?
Why AI agents are a game changer for businesses
An AI agent is not a chatbot. It's an autonomous system capable of reasoning, executing actions, querying your databases, and integrating with your business software (Salesforce, ERP, CRM, internal tools). When well-designed, it doesn't just "assist" your teams; it handles entire workflows: contract analysis, sales scoring, customer support, and document processing.
But there's a gap between promise and reality. A poorly designed AI agent project might produce an impressive proof of concept in a demo… but then nobody uses it in production. The real question is no longer "Can we do it?"More "How can we guarantee adoption, security, and ROI?"
Galadrim, a French agency specializing in the integration of AI in businesses
Galadrum is a Parisian agency specializing in custom web, mobile, and business software development, with cutting-edge expertise in integrating AI and autonomous agents within companies. Over 600 clients (startups, SMEs, and large corporations) trust them to transform their processes using generative AI.
The agency operates with an organization designed to maximize adoption: on each AI project, a AI Architect designs the technical solution while a Account Manager This dual role is what distinguishes a project that ends up in production from one that ends up in the archive.
Customer case studies that speak for themselves
Galadrim has delivered concrete, high-impact projects for organizations of all sizes:
- Replacing Excel-based management with an intelligent calendar integrated with Salesforce, coupled with an Agentforce agent that makes recommendations to the 600 monthly sales negotiated by the sales teams.
- Deployment of AI agents for lawyers, capable of analyzing contracts based on their proprietary clause database
- Integration of AI agents into the internal processes of a group of schools.
- Healthcare projects combining regulatory rigor and applied AI.
The method: frame before coding
During its webinar dedicated to 50+ French SMEs and large corporations, Galadrim shared its methodology for integrating AI into businesses. Three principles guide each project:
- Buy vs Make : making a lucid decision between off-the-shelf SaaS and custom development. Not all use cases justify a custom project. Galadrim also clearly states this to its clients when an off-the-shelf tool is sufficient.
- Product framing before the tech A poorly designed AI agent is a project doomed to failure. Defining use cases, success criteria, and the processes to be transformed is non-negotiable.
- Security and sovereignty : integrating AI without creating vulnerabilities, by controlling hosting (Scaleway, sovereign infrastructures), data flows and compliance, particularly critical in the health, finance or education sectors.
What projects should you consider?
If you are a tech or business decision-maker and are evaluating the opportunity to build your own AI-powered tools (rather than piling up SaaS subscriptions), a scoping exchange is probably the first useful step.
The Galadrim team also supports the Proof of concept for a first AI agent (document analysis, customer support, automation of repetitive tasks) that a complete overhaul of business tools incorporating layers of autonomous agents. The question is no longer whether si AI will transform your business, but what processes Attack first to generate a measurable ROI.
To learn more about Galadrim's services and their client case studies: galadrim.fr.
Excellent analysis of this "SaaSpocalypse". The observation regarding the gap between a stunning AI agent POC in demo and the reality of its adoption by field teams is unfortunately very accurate.
The "Buy vs. Make" debate you raise is indeed the crux of the matter for CIOs today. However, I would add a nuance based on my experience: the choice isn't always strictly binary between a rigid SaaS behemoth and a 100% custom development. I often observe that companies underestimate the technical debt and maintenance costs of an in-house built solution, even when generative AI drastically accelerates initial coding time.
Often, a very pragmatic middle ground involves relying on agile software vendors who have already pivoted their core systems to natively integrate autonomous agents. In the area of sales management, for example, this dynamic is found in solutions like Simple CRM (https://simple-crm.ai), where embedded AI allows for fine-tuning workflows to the realities of the company's business, without placing the infrastructure burden of a "from scratch" development on the company itself.
That said, the approach of imposing a strict business framework in pairs (AI Architect / Account Manager) before writing a single line of code is certainly the best method to avoid the "gadget" effect.
On this subject, I would be curious to have some feedback from field experience: how do you anticipate the evolving maintenance of these custom agents at your clients, knowing that the underlying foundation models are evolving (and sometimes making old prompts obsolete) at lightning speed?