The emergence of generative artificial intelligence (AI) has taken the world by storm. In the wake of this demand, the supply chain has exploded with significant activity across each layer trying to build access to commercial opportunities in the enterprise segment. However, due to the market generally being a freemium-based model, ABI Research, a global technology intelligence firm, suggests a change is needed to improve investor confidence.
As a result of the popularity of AI, the market is expected to grow at a CAGR of 162 per cent and be worth $60billion by 2030 for supply chain stakeholders, according to ABI Research.
According to Reece Hayden, senior analyst at ABI Research, the platform costs at least $500,000 per day to operate and as its popularity continues to increase, so will the cost. However, it is currently funded through venture capital investment or internal subsidies and this is not a sustainable growth plan, given customer acquisition cost.
“Running large language models like Claude, LLaMa, Titan, or GPT-3.5 has a sizeable cost burden that will be challenging to reduce,” explains Hayden. “These models are also mostly unfit for purpose in the enterprise market.”
Tackling high consumer acquisition costs
There is a solution to this problem according to ABI Research: monetisation. However, it is vital that if organisations wish to break the freemium revenue models they are currently stuck in, they must carefully align their capabilities with an advertising model which is fit for purpose.
Search engines use such models and they have proven successful in adjacent areas like cloud marketplaces. Notably, the most successful revenue generation strategies over the foreseeable future will look to support enterprise adoption directly. Most enterprises lack machine learning (ML) skills/tools, operational expertise, and strategic legal/governance frameworks to support generative AI development and implementation effectively.
For this reason, Hayden recommends: “Supply chain stakeholders should look to provide consultancy services or build low/no-code platforms that support development, deployment, fine-tuning, optimisation, operational change management, and day two operations.”
Implementing advertising models
This is already establishing itself as a proven model. For example, OpenAI has enabled the integration of Bain, McKinsey, and BCG with incumbents. Additionally, the enterprise service part of the supply chain is only set to grow as it is estimated it will be worth more than $15billion by 2030, so it gives organisations a perfect niche in the market to target.
Stakeholders can look to implement advertising models like those used by search engines, revenue share models which have proven successful in adjacent areas like cloud marketplaces or even look to productize open-sourced LLMs with closed-source enterprise functions. But it is vital that stakeholders carefully align their capabilities with the right revenue model.
The most successful revenue generation strategies over the foreseeable future will look to support enterprise adoption directly. Most enterprises lack Machine Learning (ML) skills/tools, operational expertise, and strategic legal/governance frameworks to support generative AI development and implementation effectively.
What is increasing interest in data service providers?
Other factors are at play in this market, most notably the increasing focus on data privacy. This will trigger increasing interest in data service providers. Enforcement of copyright regulation for training data and enterprise demand for fine-tuning will create sustained interest in companies able to curate enterprise datasets or generate synthetic databases.
“Recent fundraising rounds indicate more significant interest in ML data companies. For example, Mostly AI, a synthetic data generator, has just raised $25million, while Snorkel AI recently raised $85million at a valuation of $1billion”, Hayden says.
“The supply chain has plenty of opportunities to offset their cost burden with revenue models, and some are already looking to do so,” concludes Hayden. “Beyond simply identifying new revenue models, stakeholders should look to build strong partnerships across the supply chain, build products/services that target B2B deployment and scale, and develop a leading position in responsible AI development.”