As generative artificial intelligence and blockchain technologies merge in the enterprise, expect the mundane at first

As generative artificial intelligence and blockchain technologies merge in the enterprise, expect the mundane at first

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

Let me start with all the reasons why generative AI is going to take a while to really scale in your enterprise business processes and have a measurable impact on productivity. First and foremost, companies 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 shift people’s effort from individual transactions or activities to managing end-to-end processes.

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

Take something as simple as stocking up on groceries at a grocery store. Enterprise systems and POS systems have been carefully integrated over the years to automatically reorder out-of-stock items and, much more importantly, systematically anticipate and plan to avoid out-of-stock items.

Generative AI systems, on the other hand, are not good at performing the same task over and over with high precision strictly and consistently. Ask a generative AI system similar but not identical questions and you may get very different answers. This type of variance breaks business processes built on input consistency.

Generative AI systems are amazing at coming up with new ideas, and doing so at tremendous speed, but business transformation is largely about change management – ​​both people and systems. Business ecosystems tend to transform at roughly the same rate as the slowest components of the ecosystem, not the fastest.

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A good example of this comes from the early era of online shopping. It was quickly possible to build online storefronts and accept credit card payments. However, shipping and packaging were built and optimized for the world of pallet-sized deliveries to stores. To the extent that companies even had digital catalogs, they did not have images of products. No grocery store manager needs to know what a can of soup looks like. They already know. They are 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 progress will be driven by careful design and integration, not rapid, wholesale adoption. While consumers are often able to broadly adopt new technologies within a decade, it typically takes business around 25 years, and we should probably expect the same with generative AI and its integration with blockchain technology.

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 that may come sooner rather than later.

Enterprise business processes 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 the integration of blockchains into business processes is largely a matter of both process and software integration, the likely impact will be significant and will be felt soonest.

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Blockchains do a fantastic job of improving data quality. When you think about products, services and systems that move between companies, data quality is one of the biggest detriments to working between companies. In a world of silos, data is re-entered into every business 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, you can expect generative AI systems to perform even better analyses.

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

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

News agency ANSA in Italy already notarizes nearly 1 million articles a year using EY’s OpsChain system. This was meant to combat fake news, but in the future, tools like this could be crucial to authenticating the sources of AI training data.

In the same way that generative AI systems are good at writing code, they are also good at interpreting error messages, problems and suggesting solutions. Blockchain usage is still too complex, and conversational interfaces capable of accepting error messages, searching for and formatting proposals, and acting as a “co-pilot” in a process are likely to be enormously useful to users.

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In the early days when new technologies develop and interact, the results tend to be both boring and predictable, much like I’ve described above. We saw this with GPS and online shopping and mobile phones. At first, we had an e-commerce experience that was little more than a paper catalog on a screen. In the end, 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 when blockchain and AI begin to evolve and converge together. We’re in the boring phase, but just wait until things get weird and wildly unpredictable. Because they want to.

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