Tactics for Hard Times in Enterprise Software Sales

When I talk to folks responsible for pipeline in enterprise software companies, I hear the familiar refrain … “nothing is working.”

They say it’s harder to move deals from inquiry to lead to close. Buyers are tentative. Sales cycles and decision processes are stretching from one quarter to the next. One study by digital agency Bloom! found the bottleneck at marketing qualifying lead (MQL) to sales qualified lead (SQL) conversion.

According to a study by BenchmarkIT, key software company growth metrics,  (YoY) recurring revenue growth rates were already in decline in 2025. Private companies in the BenchmarkIT study were growing recurring revenue at a median of 7%, which is almost as fast as public companies below $500M. But these smaller and middle market companies were growing almost 50% slower than the 13% median growth rate of public SaaS companies above $500 million in revenue.

While Gartner projects software growth of almost 11% in 2026, most of that growth will be realized by a small number of companies whose platforms and broad offerings give them pricing power and the ability to dictate terms. According to Deloitte, the top 10 software as a service (SaaS) companies make up half of the market’s total capitalization. This suggests projected revenue growth for the industry will come mostly from pricing changes and the ongoing shift from perpetual licenses and on-premise deployment to subscription-based cloud models that will continue to goose up recurring revenue for a software vendor.

That means teams focused on net new sales of software functionality to net new nameplates, at all but the largest vendors, may have a hard row to hoe despite a generally rosy market outlook. It also means that among these “great unwashed” software companies that still need to sell value to net new customers, this will be a tougher year than most.

What is the etiology of this—the cause? Market uncertainty due to tariffs, promised artificial intelligence (AI) functionality that is not fully realized yet … there are many reasons for this baked into market conditions. But none of these are things a software executive can control.

So what CAN you control?

Sell Don’t Tell

But there are other reasons, beyond economic megatrends, that inquiries from marketing are failing to convert into sales opportunities or advance through a pipeline. According to software industry guru Dave Kellogg, genuine and accurate marketing materials will increasingly win over hype.

“So, what’s a marketer to do? One thing. Focus on trust,” Kellogg writes on his eponymous blog. “Only trust will get people to open your emails. Only trust will get them to sign up for your newsletters and subscribe to your podcasts. Only trust will allow them to believe the reviews and testimonials about your product. Only trust will get them to listen to what you have to say.

“Trust that you produce great content. Trust that you won’t lie to them. Trust that you won’t waste their time. Trust that you won’t sell their contact information to someone else. Trust that you won’t speedbag them with SDR calls. Trust that if someone unsubscribes, you’ll stop. Trust that your product does what the marketing says it does. Trust that customers get the outcomes you promise.”

You may be seeing pipeline attrition due to external market forces. But you will also see prospects drop out of a consideration cycle if they feel part of a qualification machine that built for your convenience rather than to support their decision. They may drop out of a pipeline if communication is impersonal or tactical rather than genuine.

And let’s be clear—overreliance on AI to manage a sales cycle can be a barrier here. The old adage is “tell when you have to, sell when you can.” Selling is more about listening than speaking. AI can consolidate text, personalize an email, craft a communique, or interpret touchpoints and move a suspect further up in a pipeline.

It cannot truly listen, however, or make a prospect feel heard and respected. It cannot have a pure motive, a concern for customer outcomes or empathy. It can't look into a prospect's eyes and feel their pain. AI can tell. It cannot truly build trust, rapport and sell.

Data Maturity Matters

With AI playing an increasing role in enterprise applications themselves, and the benefits they promise, we should consider that prospects may have substantive work to do before they can reap value. While a large language model (LLM) used by consumers may lash together an output based on public information, business applications must be more rigorous in their approach in order to deliver reliable, actionable insight and certainly to automate business processes. An algorithmic optimization engine may be able to structure schedules and routings faster than a human—but here too, garbage in and garbage out applies. This mean the underlying data set AI relies upon must be rigorous, consistent, complete and in and of itself reliable.

Sid Haksar, Vice President, Head of Construction Strategy & Partnerships at Autodesk, lays it out like this in his recent LinkedIn article:

“AI is a force multiplier. It amplifies strengths and weaknesses alike. Consider two contractors deploying the same AI scheduling assistant. One has structured project data, standardized workflows, and integrated cost systems. The other relies on siloed spreadsheets, inconsistent naming conventions, and manual approvals.

“The first compounds productivity. The second automates confusion. AI does not create operational coherence. It magnifies what already exists. Digital maturity lives inside enterprise systems.

“Systems of record house governance, structured data, workflow logic, permissions, and compliance. The better architected those systems are, the more valuable AI becomes. AI does not bypass enterprise software. It feeds on it. That dynamic argues for evolution, not extinction.”

The Human Touch

To sell rather than tell, to sell real value from AI, a software vendor needs to understand, then be understood. In olden times, that meant sitting across a desk from a prospect while they brain dump their pain points so you can find solutions. Today, that is usually still part of the process, but data insights harvested from sandbox trials and even primary research projects or focus groups, can provide grist for a marketing mill that generates reliable pipeline.

Content for top- and mid-funnel needs to be genuine, coming from an identifiable expert in the marketplace, rather than faceless corporate messaging. Building up internal experts and supporting them in content development is one tactic—aligning with external experts who can create content that is trusted by prospects is another.

In later stages of a sales cycle, the latter will work better than the former as an objective analyst’s insights on a product or a value proposition will be more credible than the vendor themselves.

Let us know how we can help.