AI at all costs? Understanding AI pricing models for your business
Thierry Grenot
A unique opportunity for publishers
Before turning to the subject of pricing models for AI integration, let’s take a quick look back at recent events.
November 30, 2022: opening of chatGPT, the first generative AI service for the general public. The event had an extraordinary impact. With over 100 million users in just a few weeks, it was the fastest adoption of a new technology in history.
The reason is simple: the simplicity of using our everyday language. No training to undergo, no certification to pass, no documentation to pore over. You ask a question, you get the answer. It’s magic.
Use quickly spread to the business world. As is often the case, the novelty was banned, then used on the sly and finally gradually accepted by companies, willingly or not.
Rapidly evolving AI technologies
Over the past two years, technologies have been refined. Very quickly, new players and new uses have appeared: transactional AI, enabling direct action on applications, natural language querying of knowledge bases, and so on.
So it’s only natural that users of business applications should expect their professional digital tools to take advantage of these new possibilities.
Let’s take a step aside and look at the impact of technology on machine translation.
We can see that these solutions have undergone considerable change over the years. And that the recent arrival of large-scale language models (generative AI) has improved the quality of services to such an extent that their adoption has exploded, for those who have been able to take the plunge.
It’s hard for software publishers to ignore this trend. It’s even impossible not to analyze how to incorporate these innovative functions, which are so popular with users, into their solutions.
- Refine usage (“what’s it for?”),
- Find the right partners,
- Involve their own technical and sales teams.
- And above all, identify opportunities to add value to their solutions.
Dust under the carpet?
However, AI is not immune to its limitations and problems.
In its recent study “2024 State of Generative AI in Global Enterprises”, Lucidworks issues five warnings:
- The fad is fading and companies are grounding their projects in reality. As a result, project planning is more considered.
- Delays in deploying AI projects diminish the expected return on investment: half of executives say they derive little benefit from generative AI.
- High implementation costs are leading companies to re-evaluate the expenditure allocated to these projects.
- It’s the practical aspects that are driving the adoption of generative AI. In fact, governance and cost reduction are the main drivers of generative AI.
- Executives are investing in future-proof AI initiatives. Clearly, an LLM alone is not a generative AI solution capable of delivering results.
Furthermore, generative AI solutions have a number of shortcomings inherent in large language models (LLMs) that need to be seriously considered in the case of a professional implementation: hallucinations, security, trust and so on.
Before implementing AI technologies within their solutions, publishers therefore need to accurately analyze their customers’ needs, as well as their own strengths and weaknesses around this still shifting theme.
A competitive situation in upheaval
The arrival of conversational AI on the business applications market is reshuffling the deck, and the forces at play are undergoing profound transformations.
We recently devoted a full article to this subject, the conclusions of which we’ll be recapitulating directly here, with an indication of developments between now and a horizon of (at most) 3 years ahead:
↑ Le pouvoir de négociation de vos clients va augmenter.
↓ Your suppliers’ bargaining power will decrease.
↑ The threat from new entrants is set to increase.
= No alternatives in sight.
↑ Your market’s competitive intensity will intensify.
↑ Legal constraints to be applied more rigorously.
The transformations are both profound and rapid, a sign that the time is right for publishers to adopt this theme with reason and determination.
Does AI cost an arm and a leg?
As a matter of fact, it does. Even both arms & both legs. Particularly generative AI, which draws its strength from gigantism. As illustrated by Open AI’s new language model (GPT 4), which has over 1,500 billion parameters (in fact, the exact number is confidential).
To give you an idea, that’s 10 times more than the number of neurons in a human brain (although we’re not talking about the same thing).
The cost of training the underlying deep networks runs into billions of euros per year. This is bound to shake up the business models of the few companies capable of investing such sums, in the hope that “the winner takes it all” (GAFAM, NVIDIA).
For end-user companies, the costs are also considerable, depending on the uses, technologies and R&D to be invested. And in any case, they are out of reach for VSEs, SMEs and even most ETIs.
Is generative AI on the brink of collapse? We don’t believe so for a second. After all, the Internet withstood the excesses and bubble of 2001 rather well, because its usefulness had already been demonstrated!it
Beyond pricing, what are the cost models for AI?
First and foremost, AI pricing models mean cost models.
Naturally, we’ll have to come up with ways of escaping the curse of gigantism (some of which are already in place):
- The availability of smaller language models (still around 10 billion parameters) reduces the cost of training and inference;
- Open source, a topic we’ve already covered;
- The reasoned use of costly functions, obtained by mixing different technologies and getting the best out of each of them;
- The arrival of multi-tenant solutions (e.g. Agora Software), enabling technological and know-how costs to be shared between numerous customers;
- And of course, the integration of conversational AI within business applications, which brings benefits to businesses at marginal cost
2024 = 2 ➜ 2027 = 8
The arrival of the various European regulations has not finished spilling ink and creating heated polemics. But the General Data Protection Regulation (GDPR) has applied since 2018. The Digital Markets Regulation (DMA) and the Digital Services Regulation (DSA) since 2023. And the AI Act has been passed and will apply from 2025 for the most part. In short, we’re going to have to learn to live with this legislative arsenal.
AI at the service of the application value chain
The value of a solution lies in its in-depth knowledge of the business and the quality of the implementation it offers its customers.
Applications such as ERP, HRIS, ATS, etc. provide tremendous support to many professionals in the performance of their missions.
Just as a software publisher doesn’t design its servers, operating systems and databases, a software publisher doesn’t set out to become an AI provider. Rather, they use AI to support their solutions.
This will not prevent him from becoming a specialist in the use of these technologies in the context of his business. Because that’s where the value for his customers lies:
- Radical simplification of the user experience;
- Less time wasted in menus (“a sentence is worth 9 clicks, at least”);
- Less training, less documentation to read, etc. ;
- Interrogation of business knowledge bases;
- Assisted drafting of recurring documents (quotes, invoices, etc.);
- Etc.
Fad? After all, the software industry too needs fads to thrive. Provided we respond appropriately to the above warnings, user reaction to the arrival of chatGPT seems clear enough to take a stand.
Finally, “not being in” would also imply hidden costs. Indeed, an editor not integrating AI would necessarily pay the price in terms of image, unpreparedness for future transformations and technical debt.
Which pricing models for AI?
The truth is, the pricing model at which a publisher markets its conversational AI offering depends on the priorities it sets for itself:
- Embedded – The aim here is to position the solution on an innovative axis, in tune with customer needs. In this case, AI becomes a fully integrated function within the product. As a result, distribution must be global and rapid across the entire customer base. The gains will be indirect but significant:
- Global price of business solution maintained (or even increased);
- Simplified sales through integration into the basic package;
- Aggressive and determined competitive positioning.
- Premium – In this case, you’re primarily looking for additional income. To do this, you need a price that’s high enough to make it worthwhile, even if it means deploying only on a limited basis:
- Premium offer for a subset of users (or customers);
- Specific marketing and sales efforts;
- Search for immediate profitability.
When considering AI pricing models, software publishers should take into account factors such as scalability, flexibility, and the specific features included in each pricing tier.
AI pricing models: What publishers should bear in mind
- Popular with users, conversational AI is here to stay;
- There are still problems to be solved to bring these functions to a professional level;
- Business applications will seize on it to maintain their competitive position;
- Beware of costs: both a threat (profitability) and an opportunity to add value for customers;
- AI pricing models reflect the vendor’s positioning with regard to artificial intelligence: must-have or nice-to-have.
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You are a software publisher? We hope this article has given you a better understanding of the changing market forces.
Agora Software is the publisher of conversational AI solutions partner of application publishers. We rapidly deploy conversational application interfaces that enhance the user experience of software and platforms. By integrating Agora Software into your solution, your users benefit from rich, multilingual and omnichannel interactions.
Would you like to integrate conversational AI into your applications?
Let’s talk: contact@agora.software
If you enjoyed this article, you might also like Hallucinations, LLM and conversational AI.
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