AI is now a board-level priority in almost every organization. The pressure to define a strategy is real and, in many cases, immediate.
Most companies are starting in a familiar place: internal use cases. Copilots, productivity gains, workflow automation. The assumption is straightforward — AI should make the business more efficient.
The reality has been less clear. Many organizations are still struggling to translate AI investment into measurable financial returns, particularly at scale. Recent reporting shows that while enterprise adoption is accelerating, productivity gains remain uneven and ROI is still difficult to quantify.
That does not make these initiatives unwarranted. But for software and SaaS companies in particular — where pricing, packaging, renewals, and partner motions already create complexity in how customers buy — they may not be the most consequential place to start.
Because the more significant shift is not happening inside the organization. It is happening in the market.
For software and SaaS companies, that shift has direct financial implications: how efficiently demand becomes revenue, how much margin is lost to manual processes, and how clearly leaders can forecast what happens next.
AI is already reshaping how software is bought
AI is no longer a peripheral influence in the buying process. It is becoming embedded in how products are discovered, evaluated, selected, and ultimately purchased. Buyers are using AI to, among other things, shortlist vendor options, compare pricing and packaging, and pressure-test decisions internally.
This is not theoretical. Nearly half of B2B buyers reported using AI tools during a recent transaction, while the vast majority plan on using LLMs to support a purchase in the near future.
The practical effect is a compressed buyer journey. The traditional journey — research, comparison, internal alignment — happens faster, and often before a buyer meaningfully engages with a vendor.
As a result, by the time a prospective customer reaches your business, the evaluation phase is largely complete. They arrive with intent.
For finance leaders, this creates new pressures around revenue predictability, forecasting accuracy, and channel economics — particularly in businesses with high transaction volume, recurring revenue models, or multi-channel sales motions.
High-intent buyers need a faster path to purchase
Many organizations have recognized this shift and are investing accordingly. Ensuring visibility in AI-driven discovery — through structured content, SEO, and emerging AEO strategies — is quickly becoming a priority.
That is necessary. But it is not sufficient. What happens after discovery is increasingly where revenue is won or lost.
Buyers arriving from LLMs are among the highest-intent audiences you will see. They have already narrowed options, validated requirements, and are often ready to move forward.
If that intent is met with friction (e.g., manual quoting, delayed follow-up, or rigid sales and partner processes), the experience breaks down. The buyer doesn’t restart the journey — they bounce to a competitor.
In an AI-influenced buying journey, speed becomes a competitive advantage, and ease of transaction determines how far a buyer progresses.
Friction has always existed. What has changed is the tolerance for it. As AI surfaces more alternatives and accelerates evaluation, buyers will follow the path of least resistance — increasing revenue leakage for businesses that make purchasing unnecessarily difficult.
Agent-mediated buying will raise the stakes even further
AI is already influencing how software is evaluated and selected. It is increasingly moving into how those decisions are executed. In fact, Gartner predicts 90% of B2B buying will be intermediated through AI agents by 2028, channeling over $15 trillion in autonomous exchanges.
That timeline may prove aggressive, particularly outside consumer markets, where these behaviors are likely to emerge first. But the direction is clear: as AI becomes more capable and adoption accelerates, buying activity will increasingly favor companies with structured, low-friction transaction paths.
That’s because agents are unlikely to wait for a sales response, navigate multi-step approval processes, or complete a “contact us” form. They will transact where it is possible to do so — whether that’s completing a purchase directly from an AI-generated recommendation or moving on to a vendor that allows it.
This is taking shape across the board. OpenAI’s work with Shopify and early commerce integrations within Google’s Gemini ecosystem point toward a model where discovery, evaluation, and transaction collapse into a single flow. What is emerging in consumer markets will extend to B2B, sooner rather than later.
This is not a question of replacing existing go-to-market models, but of ensuring they can support increasingly digital and automated buying processes without introducing unnecessary friction, cost, or revenue risk.
Digital buying improves the economics of software growth
For many organizations, the idea of introducing a digital buying path still feels complex. Products are too nuanced. Pricing and packaging are too variable. Sales and partner motions are too deeply embedded.
Those concerns are understandable — but they are increasingly outdated.
This is not an all-or-nothing decision. Most businesses do not need to digitize every transaction overnight. They need to identify where a digital path makes sense and start there.
That might mean enabling self-service for new customer acquisition, introducing a “buy now” option for specific product lines, or supporting renewals and expansions alongside existing sales or partner-led motions. In many cases, it also means reexamining long-standing assumptions about how much complexity the buying process truly requires.
Buyer expectations have already shifted. Research shows that 67% of B2B buyers prefer a rep-free experience, and 73% are willing to spend more than $50,000 through self-service or remote channels.
The technology has also evolved. Modern digital commerce platforms can support invoicing, quoting, complex billing models, and the kinds of workflows that previously required manual intervention — while also improving scalability, operational visibility, and revenue efficiency.
The benefits are immediate. Digital buying allows businesses to capture high-intent demand more efficiently while giving customers the flexibility to transact on their own terms. It reduces cost-to-serve, materially improves margins, and preserves sales rep and partner effort for larger, more strategic opportunities.
AI is only as effective as the data behind it
As buying evolves and AI adoption accelerates, the underlying requirement becomes clearer: data needs to be structured, consistent, and accessible.
A digital buying path does more than improve conversion. It creates a consistent system of record for how revenue is generated — capturing pricing, transactions, and customer behavior in a way that can be analyzed, optimized, and acted upon.
This is what makes revenue legible, not only to internal teams, but to the systems increasingly shaping how buying decisions are made.
Without that foundation, even well-funded AI initiatives struggle to deliver meaningful results. Models can only optimize what they can reliably access and interpret.
Start where manual work is costing revenue
For organizations under pressure to define an AI strategy, think about it in stages: in the short term, be discoverable and give high-intent buyers a low-friction path to purchase; in the medium term, collect and structure the data needed to inform AI-driven optimization; and over the long term, establish the scalable digital foundation required for increasingly agent-mediated buying.
You can also identify what’s actionable by asking these three questions:
- Are high-intent buyers arriving from AI search given a clear and immediate path to purchase?
- Are we capturing usable data across renewals, expansions, and other key lifecycle touchpoints?
- Do we have a digital foundation in place to support AI-driven and agent-mediated buying?
Our suggestion: start small. Pick a product line, geography, customer segment, or average order value threshold where cost-to-serve is high, but the transaction itself does not require significant human involvement. Add a “buy now” option to test demand. Introduce digital buying paths for renewals, expansions, or other transactions without overhauling existing sales or partner workflows.
Agent-mediated buying is coming, whether businesses are prepared for it or not. The companies that succeed will be the ones that build the right foundation now — capturing demand more efficiently, structuring the data AI depends on, and allowing the flexibility to adapt as buying behavior continues to evolve.
