Expect mundane outcomes initially as AI and Blockchain merge.
Everyone is curious about how blockchain and generative-AI technologies will come together, so let me speculate.
Over the last six months, I have been playing with generative-AI tools and considering how they will transform everything, from family gatherings to weekend afternoons. I am more and more convinced that they will have an impact, but it will not be as huge or as quick as some may think, particularly in the enterprise.
Let me begin with all the reasons why generative AI will take some time to achieve scale in enterprise business processes and have a measurable effect on productivity. First and foremost, enterprises achieve scale by implementing process controls and then automating systems. 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, from inventory management to hiring.
Paul Brody is EY’s global blockchain leader and a CoinDesk columnist.
Consider something as basic as stocking a grocery store with food. Over the years, enterprise systems and retail point-of-sale (POS) systems have been integrated carefully to automatically reorder out-of-stock items and, more importantly, forecast and plan systematically to avoid running out of stock.
Generative-AI systems, on the other hand, are not good at rigorously and consistently executing the same task over and over again with high precision. If you ask a generative-AI system similar but not identical questions, you may get significantly different answers. This type of variance disrupts business processes built on input consistency.
Generative-AI systems are excellent at generating new ideas, and doing so at a tremendous speed. However, business transformation mainly involves 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. However, progress will be driven by careful design and integration, not rapid, wholesale adoption. While consumers can often adopt new technologies broadly in about a decade, it typically takes enterprises about 25 years. 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 have identified four areas 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 uses EY’s OpsChain system to notarize nearly 1 million articles per year. The purpose of this is to combat fake news. In the future, tools like this may be critical for authenticating the sources of AI-training data.
User interfaces
Generative-AI systems are not only great at writing code, but also at interpreting error messages, identifying problems, and suggesting solutions. However, blockchain usage is still too complex. Conversational interfaces that are able to accept error messages, search for, format suggestions, and work as a “co-pilot” in a process are likely to be enormously helpful to users.
As new technologies evolve and interact, the early days tend to be both boring and predictable. This was true with GPS, web commerce, and mobile phones. At first, e-commerce was little more than a paper catalog on a screen. Eventually, we ended up with push-ads in ride-sharing vehicles proposing to have food delivered to us at our destination.
Similarly, as blockchain and AI start to evolve and converge, we are in the boring phase, but just wait until things get weird and wildly unpredictable. Because they will.
Edited by Jeanhee Kim.
