In this article, the outcomes of a global AI-based pricing study are summarized: even though the majority of companies acknowledge the transformative potential of artificial intelligence (AI)-based pricing, actual adoption rates lag behind the enthusiasm. Numerous barriers impede its widespread application, yet for the companies that surmount these obstacles, the benefits are not just theoretical; they’re measurable and impactful. When properly implemented, AI-based pricing yields superior results. Danilo Zatta (zatta.danilo@gmail.com) is a management advisor and the author of several books, including The 10 Rules of Highly Effective Pricing. Connect with Dan on LinkedIn: www.linkedin.com/in/danilo-zatta/.

The Pricing Advisor, May 2024

Pricing study finds 54% of corporations don’t use AI for any business purposes

Despite corporations being enthusiastic about the potential of artificial intelligence (AI) based pricing strategies – defined as the use of AI technologies and algorithms to determine the optimal prices for goods and services – more than half have yet to harness any form of AI for business purposes and only a meagre 27% deploy AI based pricing, according to a study from Valcon.

Amid the current challenging market conditions, the use of AI is becoming critical for leveraging existing data to drive top-line profitability. While B2C companies have been early AI adopters, the B2B sector is rapidly following suit. Despite the growing attention to the topic, key questions still loom: How prevalent are AI-based pricing strategies in the business world? What are their real-world applications, and what tangible benefits can companies expect?

Respondents to the study ranged from small businesses to conglomerates: 40% were companies with over 10,000 employees, 30% were mid-sized companies with 1,000-10,000 employees, and the remaining 30% were smaller firms with 100-999 employees. Of these, 55% were primarily active within B2B, while 45% operated in the B2C sphere. Participants were distributed among a wide range of industry sectors, e.g. automotive, chemicals, consumer goods, industrial goods and high tech, financial services, retail and wholesale, transportation, media, oil and pharmaceuticals, telecom, utilities, and others.

The research study, which surveyed 1,500 European, Asian, and American companies from SMEs to large global corporates, found that 53% of companies do not believe their internal data is mature enough for AI-based pricing in terms of quality and quantity. This means companies are unable to benefit from a practice that can help maximize margins at a time when they are facing headwinds from inflation, volatile market conditions, and fluctuating customer loyalty.

Figure 1 – Risks associated with AI-based pricing (response percentage)

In terms of the risks and barriers associated with AI-based pricing, respondents – who included CEOs, CFOs, CSOs, and Chief Pricing Officers – believed the loss of control or a lack of understanding was the key risk (34%), followed by a lack of internal acceptance (23%), high maintenance costs (13%), and compliance and regulatory issues (11%).

54% of companies are not using AI-based pricing

Key statistics from the Global AI-based Pricing Study include:

• 76% of respondents consider AI-based pricing to be relevant or highly relevant for managing prices and increasing profitability.
• 54% of respondents are however not using it.
• 27% reported regular use of AI to optimize promotions. For example, 19% regularly review and optimize prices via Chat GPT and 8% use specific applications like dynamic pricing.
• 54% of corporates do not yet use AI for business purposes.
• 53% of corporations say their internal data is not robust enough for AI based pricing, whereas 37% says they believe their data is strong enough for AI pricing.
• 67% say their IT infrastructure is not mature enough for AI based pricing, whereas 27% believe that theirs is sufficiently mature.
• Regarding data used for pricing decisions, 46% use historical transactional data, 21% use internal cost data, and 11% use customer demographic data.

Figure 2 – The Paradox of AI: while 76% of respondents consider AI-based pricing to be relevant, yet only 46% are using some sort of AI-based solution

Even though most respondents recognize the transformative potential of AI based pricing, adoption rates are significantly lagging behind. More than half of respondents reported an increase in profitability, despite inflation and significant market volatility, but as economic growth continues to stagnate, the use of AI will become critical for corporates to drive top line profitability.

AI-based pricing pays off

The study underscores that companies employing AI-based pricing are realising tangible returns. Among the successful use cases in practice that companies reported, examples include: price optimisers, dynamic pricing, discount recommenders, segmentation based on behavioural pattern recognition, promotion optimisation, sales probability forecaster, white spot tracker and geo pricing, to name the most popular ones. Notably, companies leveraging the companies using AI-based pricing mechanisms report an average increase in their Return on Sales (RoS) by a significant 2-6%.

When AI-based pricing models are transparent, produce comprehensible results, and offer traceable logic, they garner greater acceptance—whether for proposed prices or discount structures. For businesses aiming to capitalize on the growing adoption of AI, the imperative is clear: act now to maintain a competitive advantage. This necessitates enhancing resources, elevating data quality standards, upskilling teams, and fortifying IT infrastructures.

Figure 3 – selected comments from study participants

The blueprint for success in AI-based pricing

Companies that are successful in applying AI-based pricing adhere to three cardinal rules, as indicated by the superior profitability of the outperformers in the study:

1. Pilot before you scale: Successful enterprises initiate by developing specific use cases, which are then tested through agile pilot projects. These could focus on a particular product range, market segment, or distribution channel. The strategy here is to start small, validating individual use cases rather than chasing a disruptive “big bang” solution that entails greater risk if deployed without thorough testing. These “speedboat projects” are typically short-term, averaging around four weeks. Once proof-of-concept is established, scaling is expedited to capitalize on the identified potential.
2. Inclusive change management: A vital element for the success of AI-based pricing initiatives lies in effective change management. This involves deeply engaging the organization and key stakeholders, particularly the sales team responsible for execution. This inclusive approach mitigates the “not invented here” syndrome and fosters greater acceptance of the new pricing models.
3. Guidance and capacity building: Leading companies offer robust guidance and support to ensure the proper application of AI-based pricing, as well as comprehension and acceptance of the results. The time and effort devoted to data collection, consolidation, and cleansing typically account for 25-30% of the entire project timeline, setting realistic expectations for all involved.

Following these rules will help companies achieve better results when introducing AI-based pricing.