AI and software pricing

When launching new AI products and features, the stakes are huge for AI disruptors and software ‘incumbents’ alike. 

 

Innovative AI application specialists are already delivering innovations to resolve customers’ complex business challenges – such as Jasper, helping firms consolidate their brand integrity through marketing automation, or Synthesia helping companies develop corporate communication and training assets without studio resources. But established software companies will need to adjust to new customer expectations, too, around AI-infused services or potentially lose out to others who do.

 

While software firms are rushing to leverage these AI features in their products, many of them are still grappling with how to make money from them. For example, PwC’s annual survey of enterprise CEOs found that a majority have yet to see a financial return from their AI investments.

 

 

A pricing-billing gap?

To meet this challenge, evidence is emerging that software firms will need to revisit their revenue models and the pricing strategies behind them. For instance, CRM vendor SAP recently moved from a subscription-based pricing to a usage model for its business AI offering. Code platform GitHub, which was quick to look at new pricing structures, has recently adopted usage-based billing for its GitHub Copilot offering.

 

The moves by these innovators raise the wider question: could software providers’ drive to incorporate AI elements be outstripping their ability to accurately charge for them? Could the sheer pace and scale of AI adoption overwhelm the technology and billing platforms needed for vendors to deliver AI-powered revenue growth and expansion?

 

To understand these emerging pricing challenges - and assess software companies’ progress in resolving them - we surveyed leaders at 350 UK software companies in conjunction with PwC UK earlier this year. The results make for sober reading for C-suites.

 

 

Revenue and brand issues

Almost half (44%) report challenges in capturing and measuring customer usage in increasingly popular pay-per-use models. Nearly two-thirds (62%) of UK software executives aren’t fully confident that their finance and business systems can capture customer usage data and invoice for it correctly. This means significant risk of revenue leakage – unrealised value in billed products – which PwC’s revenue integrity team believe typically runs at 4-7% of annual recurring revenue (ARR).

 

Almost half of survey respondents (48%) admitted there was no effective integration between their billing function and their CRM platform. This is another likely source of revenue leakage because it increases the risks that incorrect or outdated account and pricing data is used in bill calculations. 

 

Revenue leakage occurs because established CRM and ERP products were designed for subscriptions and not for AI products with primarily usage-based models. This lack of alignment could also undermine software companies’ long-term strategies for achieving predictable revenues.

 

And as enterprises strive to overcome monetising challenges around AI offerings, they are continuing to experiment with pricing strategies, which will demand greater flexibility from revenue management systems, as time goes on. Half of the companies we surveyed had changed their pricing strategy at least twice over the last twelve months.

 

 

Hybrid pricing

Our research highlights emerging risks for software firms around legacy charging and billing infrastructures when adopting AI-infused offerings.

 

Many established SaaS companies’ technology stacks that underpin their revenue models were developed for subscription-based models. On one hand, they can flex to meet an enterprise’s upscaling needs but on the other, they struggle to adapt quickly to the AI era’s nimble usage or hybrid pricing models. Without updating the charging and billing aspects of their main infrastructure, they’ll continue to be hampered by revenue leakage challenges. Vendors may experience increased invoicing disputes with customers, harming trust. And efforts to launch new products, packaging, or pricing innovations will be constrained by old models.

 

Enterprises also face data reliability and accuracy challenges as they evolve pricing strategies. Almost 9 of 10 companies (87%) in our survey reported a lack of integration between billing platforms and ERP or general ledger systems. This suggests that many firms are still assigning sizeable and valuable internal resources to the manual transfer of data outputs (such as invoice amounts) from their billing systems into their ERP and ledger when this process could be automated to ensure greater accuracy, streamline cumbersome processes and deliver savings.

 

 

Closing the gap

For incumbent software companies, their path to success will lie in upgrading their monetisation stacks so they can accommodate variable, usage-based pricing options, which more closely connect price to the perceived value in customers’ minds, with the more predictable subscription elements they need to plan for growth.

 

To do this, software executives will have to match their focus on AI products’ functionality with a focus on the new and robust hybrid pricing and billing capabilities needed to monetise them. They will need to ensure effective capture of user data, error-free bill calculations, and tracking of all customer entitlements.

 

The good news is that incumbents have most of what they need to do this – and they have something the AI upstarts lack: huge and long-lived datasets on their customers. What’s missing is the upgrading of aspects of their underlying infrastructures to enable the crucial elements –metering, rating, and automation of data flows – to happen. With these additions to existing systems, any software provider will be well placed to successfully implement both usage-based and hybrid pricing.

 

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Griffin Parry is Co-founder and CEO of m3ter

 

Main image courtesy of iStockPhoto.com and AndreyPopov