How to Position Your SaaS in the AI Era
The AI-native software market will grow 200% in 5 years. Here's how software company leaders can position themselves for leadership — with concrete actions and examples from market leaders.
Jean de Bodinat
CEO & Founder, Rakam AI — April 30, 2026
Our conviction
All business software will be transformed by AI. The productivity gains it delivers consolidate the software's value and open new revenue streams. Companies that move now are building an advantage that latecomers won't be able to close.
This is what we observe across our 15+ software vendor clients: the first AI system generates revenue in 3 months. The second consolidates retention. The third creates a moat that's impossible to replicate.
Observation
The earthquake is already here
$467B
AI-Native Software
+207% projected growth over 5 years
$774B
Enterprise Software
+174% projected growth over 5 years
Sources: ABI Research, Spherical Insights, 2025
These numbers are not an optimistic projection. This is the trajectory already underway. The leaders of the world's largest tech companies aren't beating around the bush.
"AI agents will replace SaaS interfaces as we know them."
Satya Nadella — CEO of Microsoft
"Every company will need to become AI-first."
Marc Benioff — CEO of Salesforce
"Generative AI is the most important technology of our time."
Larry Ellison — Founder of Oracle
"Software automated business processes. AI automates the business itself."
Harvard Business Review
All business software will be transformed. Not in 10 years — in the next 3 years. Those who don't move now won't catch up to those who have already started. If you're running a software company, the question is no longer "should we integrate AI?" It is: "how do we not miss the turn?". To understand the different maturity levels, check out our article on the 3 levels of AI integration in a SaaS.
Benchmark
How leaders are positioning themselves
Talk is cheap. Let's look at what companies taking the lead are actually doing.
Qonto: the fintech that became AI-native
Qonto didn't just add a chatbot. They redesigned the experience around two AI agents: The Operator (banking operations in natural language) and The Analyst (on-demand financial analysis).
Source: Planet FinTech, 2026
Salesforce: from Einstein to Agentforce
A radical choice: retiring Einstein to launch Agentforce, a platform of autonomous agents with a low-code Agent Builder. AI is no longer a feature — it's the core of the product.
Source: Salesforce Connectivity Benchmark, 2025
Pennylane: AI-powered accounting
ComptAssistant cross-references the Chart of Accounts, tax regulations, and client data to automate accounting entries. The agent reasons on normative sources — it doesn't guess.
"2026 is the year when AI will become part of your daily work."
Arthur Waller, Co-founder
Source: BLC Conseil, 2025
ClickUp: AI coworkers
Brain + Super Agents: "AI coworkers" that users can @mention in tasks and assign work to. We're no longer talking about an assistant — we're talking about a virtual colleague.
Source: ZenPilot, 2026
Economics
The AI business model: beyond the buzzword
Talking about AI without talking about money is just making slides. Here's how to structure the economic thinking.
Costs
Compute (GPU / inference)
The cost of each AI request. It drops 40 to 60% per year, but it will never be zero. It needs to be modeled from the start.
Infrastructure (cloud architecture)
AI requires streaming, memory, vector storage. The technical architecture is not the same as a traditional SaaS.
Human resources (specialized engineering)
ML/AI profiles are rare and expensive. This is often the most underestimated cost.
Revenue
Usage-based revenue
Billing per token, per request, or per completed task. The price is indexed to the value delivered. The customer pays when they use it.
Base subscription
AI is included in a premium tier. Stable and predictable ARR. The risk: the customer doesn't always see the value.
The right model often combines both: a subscription base that guarantees ARR, and a usage layer that captures incremental value.
The equation that matters: AI-EBITDA
ROI = productivity gains delivered to customers / inference and infrastructure costs
When this ratio is positive and measurable, AI is no longer a cost center. It's an EBITDA lever. The software vendors who understand this are building an AI-EBITDA: the margin generated specifically by AI features.
Playbook
5 concrete actions for a software company CEO
Enough theory. Here's what you can do starting Monday morning.
Identify 3 high-ROI use cases
Don't launch "an AI project." Identify three specific use cases, prioritized by user impact x technical feasibility. The three that come up most often across our clients:
Automated support
70% reduction in L1 tickets. The universal quick win.
Natural language BI
Your users ask questions in plain language, AI queries the database.
Intelligent import
Document recognition, structured extraction, automatic mapping.
Measure the cost of inaction
Every month without AI in your product is potential churn. Your users are already on ChatGPT. They copy-paste data from your software into an external LLM. They find it faster than your interface. How many customers have you lost — or will you lose — because a competitor shipped AI features before you?
Structure the investment
The classic mistake: hiring 5 AI engineers without a roadmap. The approach that works (we detail the method in our article on how to build an AI roadmap):
Phase 1: Validation
A first targeted system to test a specific use case, measure ROI, and convince the board. Duration: 4 to 8 weeks.
Phase 2: Industrialization
A continuous program with your team to deploy, scale, and build a lasting competitive advantage.
The goal of phase 1 is not to deliver a finished product. It's to prove that the ROI exists.
Choose sovereign vs. cloud
For software vendors in regulated sectors — employment, training, healthcare, finance — data sovereignty is not a nice-to-have. With the European AI Act, it's a legal obligation. European hosting, models deployable on-premise or on sovereign cloud, complete traceability of processing. Our products are designed to meet this requirement from day one.
Set a 12-month AI revenue target
Not a vague goal. A number. A first AI revenue line in 3 months — that's what our clients achieve on average. At 12 months, AI should represent an identifiable percentage of your ARR. If it doesn't, it means you haven't treated AI as a product, but as an R&D project.
Pitfalls
The 3 fatal mistakes
I've seen these mistakes at dozens of software companies. They are predictable and avoidable.
The never-ending POC
A POC without a measurable business objective is a hobby. Define a success criterion before you start: "-50% processing time on this workflow" or "+20 NPS points on this feature". If the POC doesn't prove its value in 6 weeks, pivot or stop.
Underestimating data
AI without business data is a generic chatbot. Your users don't want that. They want an agent that knows their context: their customers, their business rules, their history. The quality of your AI will be proportional to the quality of your data pipeline. That's where differentiation happens, not in the choice of language model.
Hiring an army vs. partnering with experts
Building an internal AI team of 5 people takes 18 months minimum. During that time, your competitors are shipping. A specialized partner delivers in 3 months. The internal team can be built in parallel, capitalizing on the work already done. It's not build vs. buy. It's buy-then-build. Discover our services to understand how this approach works in practice.
Conclusion
What separates leaders from followers
The software market is restructuring. Vendors integrating AI into their product aren't innovating: they're responding to an expectation that already exists. Those who wait aren't being prudent: they're falling behind.
The signals are clear. Qonto, Salesforce, Pennylane, ClickUp — these companies are no longer experimenting. They're shipping. They're monetizing. They're widening the gap.
That's exactly what we do at Rakam with 15+ software vendors. We help them go from "we should do AI" to "our AI generates revenue". In 3 months, not 3 years.
Structure your AI strategy
30 minutes between executives. No slide deck, no sales pitch. Just a conversation about what works and what doesn't.
Book a meetingJean de Bodinat
CEO & Founder of Rakam AI. Helps software vendors turn AI into revenue.
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