The emphasis and focus in technology on artificial intelligence has arrived rapidly, making it difficult to determine whether AI will establish significant contributions that reshape the world, or whether it will leave as rapidly as it arrived, never justifying the intense level of investment and attention that exists today. Let’s take a closer look at some of the fundamentals that may help form a considered view on the technology’s potential.
The Good
Generative AI attracts promises of being a tectonic transformation that will reshape our world. It has much to offer all industries, and attracts new applications and use cases every day. From personal productivity to enterprise optimization, AI is already helping companies:
- Fill employee skills gaps,
- Increase employee productivity,
- Receive help with recruitment,
- Enhance customer experiences,
- Increase sales numbers,
- Strengthen cybersecurity, and
- Automate tedious processes.
One report found that 70% of security professionals say generative AI positively impacts employee productivity and collaboration. Sixty-three percent attributed a rise in morale to generative AI as well. Executives everywhere are reporting that those who have adapted to the use of AI are getting work done at scale, more rapidly, and expanding their skill sets.
Applying Generative AI
Even with such strong attention, the broad applicability of generative AI remains greatly misunderstood. Large language models can demonstrate emergent capabilities, meaning we will need to discover just how broadly they can be applied over time. Not all of their applications are known and simply won’t be until there is a greater exploration of how GenAI applications can make use of the vast sources of internal and external information that is accessible and available to business users. It is up to us to determine what that means for individual and company privacy and data usage.
Democratizing knowledge and skills will have a far-reaching impact. Still, we will not understand the full capabilities and value of generative AI without it utilizing more of the data that is available to businesses and individuals. However, this has not kept businesses from exploring how AI can bolster their business. As referenced above, well-employed AI can provide customers with improved experiences, as well as boosting productivity and innovation for organizations. But with its full capabilities unknown, it is imperative to draw from industry best practices when implementing generative AI in your own enterprise. Best practices include:
- One use case at a time. There are many ways companies can use generative AI to enhance workflow or customer experience. Focus, and isolate one area to begin training your tool to ensure you can measure and account for its impact.
- Implement data privacy guidelines. Generative AI systems need oversight to prevent data breaches, leaks, and unauthorized access to company information. Keep humans in the loop by involving your staff in training, testing, case review, and ongoing maintenance to mitigate potential problems before they begin. Data security is increasingly important, and new technologies can expose additional risk.
- Involve outside support. In most cases, companies will require more expertise than they have in-house. Hire legal and intellectual property experts to help your team navigate potential risks and keep up with legal and governance frameworks.
- Test and adjust. AI is constantly evolving. Test your use case on controlled test groups ranging in background and industry knowledge. This is a great opportunity to involve your team and help them familiarize themselves with the new tool.
- Prepare your company. To properly integrate generative AI into your operations, clearly communicate the importance of using it responsibly and train your employees on procedures such as keeping data current, and using continuous testing and feedback. This is also the time to train your team on best practices when using AI, such as never inputting personal information and data.
- Dynamic Excellence. As with best practices for most new endeavors, the constant need to adapt or adjust internal processes is no different for generative AI. If your organization truly wants to leverage the tool, you will need the right people on your team. Consider investing in a team to focus on discovering, integrating, and monitoring AI use in your organization.
The Bad
Unfortunately, like everything new, there’ll be downfalls. Generative AI has opened new attack surfaces to bad actors and players, also contributing to a rise in deep fakes that can drive general distrust of information and its sources. As it stands, 75% of security professionals have witnessed increased attacks over the past 12 months, with 85% attributing this rise to bad actors using generative AI. While thorough encryption and monitored access controls help mitigate the problem, over time, we will see an increased need for governance, regulators, and government support. Forbes found that of their sample, 75% of consumers are concerned about misinformation from AI. This discourse appears across all industries, fueling the underlying need for policies to keep individual and company privacy safe, as well as ensuring AI is used for ethical purposes.
Gearing Up for the Future
Since the first open-source generative AI models became accessible, people have been experimenting with and deploying their own versions, pushing technology in an attempt to learn what it is capable of, and increasing the pace of its development. MarketsandMarkets projects the AI market to reach an unbelievable $407 billion by 2027, substantially growing from its estimated $86.9 billion revenue in 2022.
Data organization, availability, scalability, and governance will become vital for organizations to survive the increased expectations of technology resulting from AI. Gartner stated, “If your data is not ready for AI, then you’re not ready for AI.” AI will transform every organization due to its unique value when powered by your own company’s data.
Chief executives must pay attention to these shifts in focus on their data because, ultimately, it affects the customer’s experience. AI-assisted queries and generation from proprietary data, built on foundational models, may revolutionize our approach to information technology. Technology leaders in various industries have predicted that companies will continue to lean toward intelligent personalization, with customer experience remaining central to business strategies, propelled by increased internal productivity and innovation.
While we have seen many technologies with substantial attention come and go over the years, the lifecycle of their continued introduction, development and emergence or demise is what underpins the technology industry’s pattern for identifying and reinventing the platforms, systems and experiences that have become today’s necessities. The key to harnessing the explosion of AI is recognizing the good, bad, and future, letting those influence how and where we securely utilize it. Time invested now in doing this proactively will benefit you and your organization tomorrow.
About the Author
Paul Scott-Murphy is chief technology officer at Cirata, the company that enables data leaders to continuously move petabyte-scale data to the cloud of their choice, fast and with no business disruption. He is responsible for the company’s product and technology strategy, including industry engagement, technical innovation, new market and product initiation and creation. This includes direct interaction with the majority of Cirata’s significant customers, partners and prospects. Previously vice president of product management for Cirata, and regional chief technology officer for TIBCO Software in Asia Pacific and Japan, Scott-Murphy has a Bachelor of Science with first class honours and a Bachelor of Engineering with first class honours from the University of Western Australia.
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