This moment of democratization demands differentiation that stresses unique value built on a promise of flexibility, customization, and rapid prototyping.
Platform companies are rethinking their models for growth.
ChatGPT launched nearly three years ago. OpenAI's large language model (LLM) ushered in a genuine revolution in the way everyone, from individuals to businesses, accesses and experiences information.
We're now in an age of AI democratization. AI has lowered the high barriers to entry, paving a wider opening for the classic "brilliant team in a garage" to break through.
For marketing and sales leaders, this means your 2026 technology decisions should look radically different from those you made in 2022. If you're still signing three-year contracts for marketing automation or sales intelligence platforms, you're likely overpaying for rigidity you don't need.
This same era is also allowing established B2B enterprises to reinvent themselves and their business models more nimbly. That's why tech companies generally will need to embrace business models that go beyond the standard "software-as-a-service" approach.
Given how quickly AI and related tools are advancing, customers are more reluctant to want to lock in long-term agreements. But these same changes are making it easier for all sides to adapt in kind. Some companies seek to experiment more quickly; others are maintaining a more cautious stance that demands greater testing and tweaking.
Emphasizing Unique Value
More enterprise companies are finding they don't need the traditional SaaS model. They've been dying to jettison those long-term contracts and rid themselves of rigid, sometimes cumbersome software integrations.
These feelings simply reflect B2B firms' identity. They are deeply grounded in flexibility. They expect and deliver on immediate, quantifiable outcomes, grounded by evidence of ongoing evolution and growth.
Auto-renewing contracts and ballooning fees have long burned B2B customers. They demand monthly proof of value, not just promises locked in over years. No one has the patience to wait and see how the market shakes out.
It's generally accepted that AI lowers the cost of building custom solutions, undercutting cookie-cutter licensing models. The wider embrace of generative AI has made the evolution of the SaaS model possible with actionable, practical alternatives.
Customization and Collaboration as a Service
As AI expands and speeds up the accessibility and management of data and creativity, enterprises can customize solutions on the fly instead of locking into an off-the-shelf, multi-year software license that merely "updates" and tweaks.
What does this look like in practice?
- A marketing team that previously waited six months—and paid thousands of dollars for custom reporting dashboards—can now use AI-powered analytics tools to build dashboards in days. They iterate based on real campaign data, adjusting visualizations and metrics as their strategy evolves, rather than being locked into dashboards designed before they knew what questions they'd need to answer.
- A sales organization that had to purchase rigid CRM modules—often paying for enterprise packages to access a single needed feature—can now prototype custom workflows using AI assistants. They're building lead scoring models specific to their industry's buying patterns; not generic frameworks designed for the broadest possible market.
- An e-commerce company that paid six figures annually for enterprise analytics packages can now build custom attribution models on-demand for a fraction of the cost. When their customer journey changes from primarily direct-to-consumer to multi-channel B2B partnerships, they can rebuild their attribution logic in weeks, not the 18-month timeline their previous vendor quoted.
For a more detailed example, consider a mid-sized B2B software company that needs industry-specific customer segmentation for their marketing automation. Under the old SaaS model, they would have signed a three-year contract starting at $100,000 annually at least, with customization charged separately.
Instead, they partner with a provider offering monthly AI-assisted customization. Within 30 days, they have a working prototype analyzing their customer data. By month three, they have iterated through five different segmentation models based on actual campaign performance. Total investment in quarter one is reduced to a tenth of its previous spending, with the freedom to walk away if results don't justify continued investment.
The bottom line for tech companies of all stripes involves demonstrating high-touch, high-value service from day one. The new mantra for tech companies is "Ongoing value is everything."
Value depends on flexibility, customization, and rapid prototyping. Keep moving, and keep adapting to specific client needs. What's even better is anticipating needs before anyone else recognizes the need for them. Otherwise, prepare to be commodified into oblivion.
Five Warning Signs Your SaaS Stack Is Outdated
How do you know if your current software investments are holding you back?
Here are indicators to watch out for.
- You're paying for features you requested 18 months ago but still haven't used. The gap between what you thought you'd need and what you actually need has widened dramatically. AI-enabled solutions let you build what you need today, not what you predicted you'd need two years ago.
- Customization requires opening support tickets and waiting weeks for responses. If simple workflow changes demand vendor intervention, you're operating at 2020 speed in a 2026 market.
- Your contracts auto-renewed without a value review. When was the last time your vendor proactively showed you ROI metrics or usage data? Monthly value validation should be the norm, not an annual afterthought.
- Integration costs more than the software itself. Legacy SaaS platforms were built in an era of walled gardens. AI-native solutions assume interconnectivity from day one.
- You can't test competing solutions without a major migration project. The switching cost alone keeps you trapped in suboptimal tools. The new model assumes you might leave at any time, so providers compete on ongoing value delivery.
AI Is Lifting All Boats
The rising tide of AI is making all services more collaborative and more personalized.
Every client and customer wants to work with companies whose products and services they use on more equal terms. Clients demand differentiation and unique value propositions from their enterprise service providers.
This is ultimately a clear win-win for all sides. But it does bring some difficult decisions about how to revise old ways of doing things.
As a result, "software as a commodity," as something anyone can copy and provide, has been coming for all B2B businesses for years. Now, as any capability becomes commodified, you need to be able to prevent and solve problems, not simply create software. If you have the knowledge to describe a software solution, you can capably craft it.
The "no coding necessary" era is real.
Three Steps to Navigate the Transition
Ready to move beyond traditional SaaS? Start with these practical steps.
Audit Your Current SaaS Stack
List every software subscription your team uses. For each, note the last time you used a "must-have" feature that justified the cost, and the last time the vendor shipped something that improved your workflow.
If those dates are six months old or older, you're likely paying for inertia, not innovation. Calculate what percentage of your software budget goes to tools that haven't materially changed how your team works in the past year.
Pilot AI-Enabled Alternatives With 30–60-Day Trials
Don't rip out your entire stack at once. Identify one pain point—reporting that takes too long, customization you can't get, or features you're paying for but not using.
Find vendors offering monthly commitments with AI-assisted customization. Run a contained pilot with clear success metrics. The goal isn't perfection; it's learning whether rapid iteration delivers better results than your current locked-in solution.
Measure Differently
Traditional SaaS metrics focus on feature checklists and uptime percentages. The new model demands different measurements.
How quickly can you go from idea to implementation? How many iterations can you test in a month? How directly can you tie tool usage to business outcomes? Track time-to-value and iteration speed, not just whether the software has the features you thought you wanted two years ago.
SaaS Needs to Keep Pace in the AI Age
Ultimately, the most successful B2B operators will be those who figure out how to match their value proposition to keep pace with the market's changing needs—which is what lasting businesses did before the advent of SaaS.
Staying relevant when change is faster and more constant is to take a page from the pre-digital age while firmly taking advantage of the tools that bring out what's best and most special about your offerings.
More Resources on Marketing Strategy and AI
From Chaos to Control: Orchestrating AI Across Enterprise Marketing
From Services to SaaS: The Secret to Agency Profitability?
Making AI Actually Work: A CMO's Guide to Scaling AI Across the Organization
