Rise Up CEO spins out AI startup Forge as enterprise learning model shifts
Rise Up CEO and co-founder Arnaud Blachon has launched a new AI-focused company, Forge Learning, after internal efforts to integrate AI into the company’s existing platform failed to deliver at pace.
In a LinkedIn post, Blachon described how Rise Up initially followed a common SaaS approach to AI, writing that “when the AI wave hit, we did what every SaaS company does: we spun up an AI team inside Rise Up.” Despite early momentum, he said progress stalled, adding: “For months, nothing shipped.”
Internal AI development fails to translate into delivery
Blachon said the company had a clear view of AI’s potential but struggled to convert that into working products, pointing to what he described as “a massive gap between what we knew was possible and what was actually getting built.”
He explained that this became more apparent after spending time in San Francisco reviewing AI products already in production, noting that while other companies were deploying tools with “real clients” and “real traction,” Rise Up was still debating readiness internally. “It was ready. We just weren't set up to move fast enough,” he wrote.
That realization led to a shift in strategy. Rather than continuing to layer AI into the existing platform, Blachon said the company decided to split its approach, keeping incremental improvements within Rise Up while building something new separately. “Incremental AI value onto Rise Up. For the next frontier? Start from scratch,” he wrote.
Forge developed as standalone AI performance platform
Blachon positioned Forge Learning as a separate company rather than an extension of Rise Up’s LMS and LXP model, stating: “That's how Forge Learning was born. Not a feature. Not an upgrade. A new company.”
The move reflects a broader shift in enterprise learning, where providers are moving away from static, platform-based delivery toward systems embedded within day-to-day workflows.
Forge is designed to operate inside business tools, using real-time data to identify performance gaps, generate tailored content, and deliver coaching at the point of need. The model focuses on continuous, in-workflow support rather than structured courses or standalone learning environments.
Separation highlights structural challenges in AI adoption
Blachon said the decision was also driven by organizational constraints, arguing that existing teams are often not set up to deliver both incremental improvements and more fundamental product changes at the same time. “Because you can't ask the same team to think incremental on one product and transformational on another. You have to split,” he wrote.
He added that the process required rethinking the role of Rise Up’s existing platform, describing it as a foundation rather than a finished solution. “The hardest part wasn't the decision. It was accepting that what we'd built for 10 years was the foundation. But we needed so much more,” he wrote.
The move comes as enterprise learning providers face increasing pressure to demonstrate how training connects to performance, with AI increasingly positioned as a way to deliver more immediate, contextual support within the flow of work.
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