5 AI Predictions We Got Right in B2B SaaS
In 2023, we published five predictions about how AI would reshape B2B SaaS. Every one of them has come true, most faster than we expected.
The scorecard
Here is the original list, updated with what actually happened and what we are seeing across the Quadshift portfolio.
1) Natural language application interfaces have proliferated
User: “Generate a chart showing revenue and net profit by product, by year for the past 5 years, and provide the supporting information in a table below the chart. Summarize for me in a paragraph below any idiosyncratic transactions that are having an unusual and significant impact on the results in any one year.”
This is what finance users expect from an ERP today.
Natural language interfaces have transformed how B2B SaaS users access their data. Instead of relying on engineers to write custom queries, applications now let users interact with their data through plain language using AI models like ChatGPT, Claude, and Gemini. The result is a dramatically better user experience, fewer custom development requests, and a lower learning curve for non-technical users.
What we are seeing: Across the Quadshift portfolio, natural language interfaces are in production and in daily use by real customers. These are not prototypes.
2) Engineering productivity has dramatically increased
AI developer tools have fundamentally changed how software gets built. Engineers and developers now generate working code using natural language, spending less time writing boilerplate and more time on architecture, product decisions, and customer problems. Development cycles are shorter, costs are lower, and software quality is improving. AI coding tools are standard equipment for modern dev teams, and on the best teams they write most of the code.
What we are seeing: Our dev teams are fully AI-assisted. The gains were large enough that traditional project management became the bottleneck, so one of our teams built an AI-native project management tool to keep up. Read the full story in our AI-assisted development case study.
3) User experience expectations have risen
Users now expect software to do more of their jobs for them. Natural language interfaces, agents that complete multi-step workflows, and AI support that resolves issues rather than deflecting them are table stakes. AI agents are replacing entire human workflows in support, onboarding, and data processing. The bar for what “good software” means has permanently shifted.
What we are seeing: Our platforms ship AI support tools that give users instant, sourced answers instead of manual lookups. Customers notice when software gets smarter and support gets better. They also notice when it does not.
4) Market leaders or emerging leaders that embrace AI have built on their lead
AI has enabled the best B2B SaaS companies to move faster, ship more, and serve customers better. Market leaders with the resources to invest in AI are pulling further ahead. Nearly every major software product has shipped significant AI capabilities in the past few years, making it harder for competitors to close the gap.
This extends beyond the product itself. Customer support, billing, ticketing, sales, and every functional area within a software company has improved through AI-enabled solutions and processes. Companies in traditional industries, particularly smaller ones, often lack the capability to develop and implement these solutions in-house. They lean on their software providers to bring AI to them.
What we are seeing: This is the core of Quadshift’s thesis. Most vertical software companies know AI matters but do not have the team, the tools, or the playbook to move. Quadshift provides all three. We train every employee to build with AI, embed a centralized AI enablement team across the portfolio, and share breakthroughs from one company to every other. A breakthrough at one platform accelerates every other platform.
5) Legacy B2B software has fallen further behind
The gap between legacy software and AI-enabled SaaS has widened dramatically. On-premises solutions tend to have slower update cycles, making it difficult to keep up with the pace of AI development. Cloud-native SaaS companies can ship AI updates continuously, offering customers the latest capabilities without waiting for a release cycle or an on-site upgrade. Businesses are increasingly opting for modern SaaS over on-premises alternatives, and AI has accelerated that shift.
For vertical market software buyers in particular, the question is no longer “should we move to the cloud?” It is “can my team, and my team’s AI agents, actually work with this software?”
What we are seeing: In our own acquisition conversations, AI capability has moved from differentiator to diligence item. Buyers and customers ask about the AI roadmap before they ask about the feature list.
What the scorecard means now
Every one of these predictions played out, most faster than expected. The companies that moved early are compounding their advantage. The ones that waited are now playing catch-up against competitors with a multi-year head start on AI adoption, AI-trained teams, and AI-enhanced products.
