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Hardly an industry today can escape disruption. New technologies and business models compel CTOs and IT leaders to seek innovative ways to stay ahead of the digital transformation curve. But they face challenges understanding the role and impact of cognitive technologies such as artificial intelligence (AI) and machine learning when integrating them into their existing infrastructure.
An increasing number of those infrastructures include mainframe systems, and enterprises are looking to leverage the compute power and secure data protection mainframes offer for running cognitive applications. Cognitive technologies applied to transactional data are pivotal in capturing keen insights, building client relevancy, implementing cost-effective business models and more.
How does the smart money view AI and machine learning in these environments? For one perspective, we spoke with Brian Colwell, a well-known influencer researching alternative and technology investments. Colwell writes about disruptions and innovations in a world of emerging technologies, and he spoke with us about advanced ways in which cognitive approaches are being integrated with mainframe and other environments.
Q: As an investor, do you see AI at work in financial markets today?
A: Sure. For example, hedge funds are using machine-learning algorithms to measure social sentiment and gauge a stock’s value before earnings reports come out. That way, they can get ahead of the market. Others use machine-learning algorithms to chart multiple indexes and trading sites — simultaneously and by the minute — to facilitate high-frequency trading. For example, a financial services firm can look for a company trading at a specific rate on one exchange and trading at another rate on another exchange. This process enables the firm to engage in arbitrage — buying and selling at the same time to attain risk-free opportunities to make money based on global price differences.
Q: Many transactions in today’s global economy are driven by mainframe systems. Do cognitive technologies have a place in these environments?
A: Yes, today’s mainframe is a powerful, open and connected tool that can drive huge transaction volumes that are hard to achieve otherwise. Almost any mainframe application can benefit from AI. I think we’ll see more companies applying machine learning to the core data that lives on their mainframe systems. Quite often, that data is valuable operational data. When you apply machine learning to this data in place, the intelligence can derive more accurate information that enhances business decisions and can be shared with clients and partners. Applying machine learning in this manner builds intelligence into almost everything that the business does, which can even include AI-driven communications systems that engage clients and enhance the customer experience. And when the data remains in place, the reduction in security risk and latency helps minimize cost.
Q: Where do you see cognitive having significant impact, and what does it mean to organizations from an investment standpoint?
A: I think healthcare is an area where AI-based applications are going to have a profound effect—both in terms of patient benefits and as an industry disruptor with the ability to help generate revenue. AI is already being used to digest vast amounts of medical information and then combine that knowledge with real-time data from medical devices to come up with appropriate responses.
AI is also very interesting in the area of cognitive cybersecurity. We just can’t compete against hackers right now. They are too smart, too advanced and they have too much innovation on their side. Cognitive technology enables the active management that everybody’s looking for in cybersecurity and the ability to respond immediately to threats. In many ways the approach emulates an immune system. When it encounters a virus, it learns, it gets stronger and it can better resist that threat in the future.
Cognitive also empowers organizations to take unconventional approaches and come up with really creative strategies and ways to deploy technologies. For example, IBM is a smart investment because of what it’s doing with advanced, real-time analytics. IBM thinks outside the box about AI, and instead of going directly to the consumer with it, its approach is to go to businesses. Then those businesses take it to the consumer in a multitude of ways that multiply the value of those cognitive analytics approaches. These technologies are very open to creativity going forward.
Q: What other technology developments are you tracking?
A: I think blockchain is a fascinating area. Organizations, including IBM, are beginning to use blockchain in very interesting and innovative ways. It facilitates secure, trusted transactional communications and interactions — and not just within the financial sector. For example, there’s a blockchain for the music industry, medical records in the healthcare industry, legal records, mortgages and more. A worldwide shipping conglomerate is employing IBM blockchains to track its shipping containers, which is a huge deal when you’re talking about global shipping and trade and putting all of that on a blockchain. I consider blockchain technology to be akin to internet 2.0, where we essentially don’t even have to understand blockchain. It’s just going to be a standard internet infrastructure in which we’ll all participate.
When you think about infrastructure for these disruptive technologies, cognitive applications on mainframes not only accelerates the capture of accurate information and insight, it also enables leveraging existing investment on infrastructure and applications to speed secure delivery of services. The possibilities for cognitive innovation in business transactions are almost endless. Companies may soon be applying cognitive technology in ways we haven’t even imagined yet.
See what AI and machine learning on mainframe systems can do for your organization.