AI forces bigger software players to adapt pricing to compete

AI is changing everything about business. And companies that use it to rethink their core offerings and value propositions will be rewarded. Those who don’t may pay a high price.

The business world tends to be transfixed by the new entrants and disruptors. Companies like Anthropic or OpenAI provide the AI economy’s infrastructure - the raw material that other specialists use to innovate. New AI-native application players are solving specific problems, like Sierra remaking contact center workflows and Harvey streamlining legal work.

But what about the software incumbents? What about those established SaaS players who previously redefined our ideas of business platforms? They and their channel partners need to respond to the seismic challenge posed by AI. As McKinsey noted in a recent report, “With AI-native upstarts redefining speed and scale, software incumbents hoping to remain competitive must fully embrace the new technology to reinvent their value proposition and internal operations.”

They can do this by embracing AI to improve their existing products. They can also leverage their advantages, including global customer bases, huge data sets, brand equity, and understanding of customer use cases and potential for new ones.

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Adapt or lose

In many cases, incumbents are already building AI capabilities on top of existing platforms. Take the code hosting platform, GitHub. It was quick to enhance its product with the Copilot code assistant. GitHub also made an early decision to look at new pricing structures, and just recently committed GitHub Copilot to usage-based billing.

But incumbents have structural disadvantages, too. And they’ll need to adapt to compete - or run the risk of being left behind.

A key disadvantage concerns their existing monetization infrastructure, which is generally entrenched in the tech stack and, by definition, is hard to change to meet the demands of customers’ changing needs and their perception of value from software.

The issue is that the incumbents’ monetization stacks were designed for subscriptions rather than usage-based billing. Many CRM, ERP, and other quote-to-cash vendors can support the scale requirements of enterprise businesses, but they are not well-adapted to switching quickly to the usage or hybrid pricing models common in AI features/services.

The benefits of usage-based billing

In today’s fast-changing markets, the value propositions of AI-native firms tend to be oriented around actions, tasks, and outcomes, with usage-based pricing helping them manage variable costs and control margins.

The optimal model of incumbent software companies and their channel partners is to develop a hybrid pricing infrastructure – one that can accommodate usage-based pricing with subscription elements – to suit customers.

With this hybrid model, companies and their partners get the predictable revenue streams they rely on to plan, forecast, adjust workforces, etc., through the subscription elements, but still retain an element of variable pricing that connects price to value.

Revenue leakage risks

But usage-based pricing increases the risk of revenue leakage. Hybrid pricing is a form of usage-based pricing with significant variable, usage-based components.

Revenue leakage occurs where charges should be included on the invoice, but aren't, because of billing errors - it's revenue that has been earned, but never charged for.

Leakage around usage-based components in pricing can happen in three main ways – first, incomplete capture of usage data to drive charges or be delivered into billing calculations; second, simplifications or errors in the bill calculation mechanism, for example, where usage and pricing data are brought together to calculate charges but the spreadsheet can't cope with the complexity; third, out of date pricing where the source of truth for pricing (e.g. contracts or the CRM) is out of sync with the bill calculation mechanism.

What happens if there is no monetization stack upgrade? Established software providers can sometimes struggle to offer the right pricing at all. Some decide to hack the problem, with workarounds creating major operational drag on the business: difficult billing processes; revenue leakage, and poor customer experience because up-to-date usage and billing data isn’t available. There’s also a lack of flexibility when launching new products, packaging, or pricing.

Keyhole surgery

So how are winning software incumbents adapting to this fast-changing landscape?

The good news for incumbents is that the monetization stack doesn’t need major surgery, just keyhole surgery. Their current stack probably has 80% of what is needed to support the pricing models to compete effectively with native AI players and usage-based billing pioneers.

What they need is infrastructure that can fill the gaps (specifically metering, rating, and the automation of data flows around the stack), and solutions are emerging that provide exactly this.

Delivered by expert integrators and channel partners, these offerings allow incumbents to ‘upgrade’ their stacks for new pricing/business models - without changing the tools their teams already use.

Additional ‘by-product’ benefits will arise from this behind-the-scenes upgrade. One is that usage and billing data becomes available across the business. This deepened insight helps multiple business functions, including marketing, customer service, and IT, do their jobs better.

Turning obstacles into opportunities

The result? Providers and channel partners can turn a challengeーthe tricky task of upgrading legacy monetization systems or risk being upstaged by AI-native upstartsーinto an opportunity to better price all products and increase the customer-centricity of the entire business.

In doing so, software vendors will be responding to the structural changes AI has ushered in that have exposed structural weaknesses in how vendors operate and respond to AI-hungry customers. More than six in ten software leaders said they believe AI will fundamentally change their business model in the next three to five years, McKinsey reports.

As McKinsey asserts: “For incumbent software companies, the imperative is clear: becoming AI-centric is no longer optionalーit is essential to remain competitive.”