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When AI and Blockchain Merge, Expect the Mundane at First

When AI and Blockchain Merge, Expect the Mundane at First

When AI and Blockchain Merge, Expect the Mundane at First

As the transformative technologies of generative artificial intelligence and blockchain find their way in business, inevitably they will interact. The pairing has the potential to achieve wild, weird and presently unimaginable results, but expect the first experiments to be boring and predictable, says EY's Paul Brody.

As the transformative technologies of generative artificial intelligence and blockchain find their way in business, inevitably they will interact. The pairing has the potential to achieve wild, weird and presently unimaginable results, but expect the first experiments to be boring and predictable, says EY's Paul Brody.

As the transformative technologies of generative artificial intelligence and blockchain find their way in business, inevitably they will interact. The pairing has the potential to achieve wild, weird and presently unimaginable results, but expect the first experiments to be boring and predictable, says EY's Paul Brody.

AccessTimeIconJun 8, 2023, 3:19 PM
Updated Jun 12, 2023, 3:01 PM

Everybody wants to know how blockchain and generative-AI technologies will come together, so allow me to speculate.

From family dinners to weekend afternoons, I’ve spent a lot of time in the last six months playing around with generative-AI tools and thinking about how they will change “everything.” I’m more and more certain that they will have an impact, but it won’t be as enormous or as fast as some might think, particularly in the enterprise.

Let me start with all the reasons why generative AI is going to take a while to really achieve scale in enterprise business processes and have a measurable impact on productivity.. First and foremost, enterprises achieve scale by implementing process controls and then automating systems. From inventory management to hiring, the key to scaling enterprise systems is the ability to move people’s work efforts from individual transactions or activities to management of end-to-end processes.

Paul Brody is EY's global blockchain leader and a CoinDesk columnist.

Take something simple like stocking a grocery store with food. Enterprise systems and retail point-of-sale (POS) systems have been carefully integrated over the years to automatically reorder out-of-stock items and, much more importantly, forecast and plan systematically to avoid ever being out of stock.

Generative-AI systems, by contrast, are not good at rigorously and consistently executing the same task over and over again with high precision. Ask a generative-AI system similar but not identical questions and you may get wildly different answers. This kind of variance breaks business processes built on input consistency.

Generative-AI systems are terrific at coming up with new ideas, and doing so at enormous speed, but business transformation is largely about change management – both people and systems. Enterprise ecosystems tend to transform at about the same rate as the slowest components in the ecosystem, not the fastest ones.

A great example of this comes from the early era of web commerce. It was quickly possible to build web-based store fronts and accept credit-card payments. However, shipping and packaging was built and optimized for a world of pallet-sized deliveries to shops. To the extent that companies even had digital catalogs, they didn’t have pictures of products. No supervisor of a grocery store needs to know what a can of soup looks like. They already know. They’re in the store every day. As a result, e-commerce took off much more slowly than analysts expected, held back not by the web, but by warehouses and logistics systems.

Like e-commerce, generative-AI systems will infiltrate enterprise systems alongside blockchain technology and they will eventually work very well together, but the progress will be driven by careful design and integration, not rapid, wholesale adoption. While consumers are often capable of adopting new technologies broadly in about a decade, it typically takes enterprise about 25 years and we should probably expect the same with generative AI and its integration with blockchain technology.

A look on the bright side

Having gotten the bad news out of the way, let me focus on the areas where we will see the most dramatic impact of how these two technologies will work together. I’ve identified four that might come sooner rather than later.

Software development

Enterprise business processes are run on software, and generative AI systems are exceptionally good at software development. It is one of the few areas where we have strong, documented evidence that generative AI systems significantly improve productivity. Since integrating blockchains into enterprise processes is very much a matter of both process and software integration, the likely impact will be significant and felt soonest.

Analytics

Blockchains do an amazing job of improving data quality. When you think about products, services, and systems that move between enterprises, one of the biggest casualties of inter-company work is data quality. In a world of silos, data is re-entered in each enterprise ecosystem. On a blockchain, tokens and hashes represent assets and data and can maintain their integrity as they move through an ecosystem. With better-quality data, expect generative-AI systems to do even better analysis.

It will also work the other way around: generative-AI systems are terrific at matching and interpreting patterns. They will become foundational to the business of blockchain analytics in a very short order, helping identify trends and classifying individual transactions.

Generative AI-training data

One of the biggest emerging problems for AI systems is how to find trustworthy source data. We’re in the early stages of an exa-flood of AI-generated content. Much of it will be banal, generic and mediocre. How will we know what is an authoritative, expert view on a topic or a machine-generated pattern based on other machine-generated patterns? By verifying authenticity and origin of source data using blockchain hashes.

The ANSA news agency in Italy already notarizes nearly 1 million articles a year using EY’s OpsChain system. This was intended to combat fake news, but in the future, tools like this may be critical for authenticating the sources of AI-training data.

User interfaces

In the same way that generative-AI systems are great at writing code, they are also good at interpreting error messages, problems and suggesting solutions. Blockchain usage is still too complex and conversational interfaces that are able to accept error messages, search for, and format suggestions and work as a “co-pilot” in a process are likely to be enormously helpful to users.

In the early days as new technologies evolve and interact, the results tend to be both boring and predictable, much as I have described above. We saw this with GPS and Web commerce and mobile phones. At first, we had an e-commerce experience that was little more than a paper catalog on a screen. Eventually, we ended up with push-ads coming to us in a ride-sharing vehicle proposing to have food delivered to us at our destination.

And so it will be here, as blockchain and AI start to evolve and converge together. We’re in the boring phase, but just wait until things get weird and wildly unpredictable. Because they will.

Edited by Jeanhee Kim.

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Paul Brody is Global Blockchain Leader for EY (Ernst & Young). Under his leadership, EY is established a global presence in the blockchain space with a particular focus on public blockchains, assurance, and business application development in the Ethereum ecosystem.