Why Is Pricing Such a Hot Topic for Software Companies?

AWS’ Heather Wright offers insights on why pricing is such a hot topic for software companies. This article originally appeared on Solutions Review’s Insight Jam, an enterprise IT community enabling the human conversation on AI.

The software industry has had its fair share of ups and downs. Even before the explosion of artificial intelligence (AI), there was a shift in investments and return expectations. The glut of buildings in 2020 was followed by an industry-wide belt-tightening in 2022. Then the Rule of 40 dominated tech conversations—the principle stating that a software company’s growth rate combined with its profit margin should equal 40 percent or more.

And now? The generative AI boom is offering a wealth of opportunities. From boosting efficiency across the software lifecycle to bringing powerful new capabilities to market, many software companies are racing towards implementation in a bid to take a greater slice of market share.

But there’s a catch. It’s simply too early to know the true size and timing of generative AI’s rewards. There’s no guarantee that investments will result in business outcomes like revenue, but there is a strong possibility that generative AI could improve other metrics, such as customer retention and churn. Therefore, if you choose not to invest, you risk getting outpaced by competitors who are creating more compelling user experiences.

At the same time, pricing strategies have come to the forefront. And it’s not just pricing professionals who are being kept busy by this heightened focus. Product leaders are taking a key role in pricing strategies and approaches as they carefully consider their roadmap investments. But why is pricing on the radar right now, and what can software companies do to support more stable revenue in the long term?

Entering New Pricing Territory

With slowing growth across the high-technology industry, many organizations aren’t achieving their projected profitability. In fact, 84 percent of publicly listed software companies saw a drop in valuation from 2022 to 2023. But things rarely slow down in software, and thanks to generative AI we’re once again in the middle of a serious shake-up with the potential to change their prospects.

Software companies are investing in building capabilities that both support AI and drive software innovation. Examples of success stories include the likes of CrowdStrike, which is offering new generative AI functionality to help service providers sell AI-enhanced custom security solutions. Meanwhile, NICE has created a process automation platform that takes repetitive admin tasks off businesses’ hands, so they can focus on driving innovation projects. While generative AI applications like these are proving valuable, the fact remains that they’re inherently different to software as a service (SaaS) applications. As such, they need to be treated differently when it comes to pricing.

Firstly, the marginal costs of generative AI can be higher than SaaS. There is even less known about the revenue potential of software companies who are venturing into the space, which puts into question any longer-term assumptions on returns. Are they innovating on an enduring platform? Or is it just another point solution at risk of being bundled with other software?

The difficulty is that there’s no standard approach to pricing a generative AI service. Experts are starting to weigh in on the subject, however. One of the earliest papers in the market was published by Boston Consulting Group, noting the gap in strategic pricing approaches for generative AI in favor of speed. Others have joined the conversation, emphasizing the complexity of pricing AI services, especially when there’s no one-size-fits-all. While it’s true that we’re still exploring new ground, there are key considerations software companies can take to support sustainable monetization. 

The Multi-Faceted Art of Pricing 

Something I can’t stress enough is that pricing can’t be done in a vacuum. Strategies need to be agile and account for three main levers: technology, marketing, and pricing. Take your technology strategy, for starters. The associated costs of your AI models will vary significantly, especially when there’s such a vast breadth of models to choose from and combine in almost infinite ways. What’s more, costs keep changing as the technology progresses at a rapid pace. So, how do you pinpoint the value of your product when the market is constantly evolving and there are multiple factors at play?

The first step is to work backward from your end customer and determine the value of your innovation from their perspective. Ask yourself: What challenges are you looking to solve for them, and how? What makes your product different? When you can answer these questions, you’re far more likely to formulate a winning pricing strategy. Skilled technology assessments like these are where cloud service providers (CSPs) can offer huge value. Not only can they map out your technology, but CSPs can also help you optimize costs and reduce risks as you innovate—taking the weight of potential future cost burdens off your shoulders.

Identifying greater marketing opportunities is another area that should feed into your strategy. Software companies are at the top of their game when it comes to building, yet may have room for growth on their marketing strategies. Strong alliances with CSPs here can also give you the means to dive deeper into market segmentation as well as product differentiation, positioning, and development—in turn empowering you to drive higher value services for end users.

Once you’ve got a clear idea of the value your product delivers, it’s time to think about pricing models. While I’ve seen some advanced pricing strategies in the market, it’s not uncommon to find outdated and underserved approaches. In the generative AI space, the two main pricing models are consumption and subscription-based models. Hybrid models are popular, providing software companies with the flexibility to charge for a subscription as well as extra, usage-based fees. There’s also another option in the form of outcome-based pricing—a newcomer that charges based on the value provided. Of course, each pricing model has its own pros and cons that need to be carefully weighed up.

Playing the Long Game

Regardless of the pricing model you choose, experimenting and taking the time to work out the right pricing strategy for your business is key—especially when investor scrutiny now leaves less room for error. After all, a poorly considered pricing strategy is a self-imposed ceiling on your product’s potential. Worse yet, it leaves the door open for competitors to stake a claim.

Even though we can’t see the exact size and timelines of generative AI’s returns, its long-term potential is likely more than we know. It’s understandable that software companies may still face uncertainty based on the underwhelming results of their investments over the last year. But rather than trying to draw long-term conclusions from short-term results, the best chance to capitalize on AI’s prize may lie in collaborating across pricing, product, and marketing, as well as creating deep CSP alliances.