With all the recent hype, many may not realize artificial intelligence is nothing new. The idea of thinking machines was first introduced by Alan Turing in the 1950s, and the term “artificial intelligence” was coined nearly 80 years ago. However, the technology has gained new notoriety thanks to the introduction of generative AI. Yet, the buzz has come with hesitations around things like implementation, training, and ultimately, security.
Many business leaders are still questioning whether they should implement generative AI within their organizations. Each company has different goals and capabilities and therefore needs to look at the benefits and challenges associated with the technology within the context of their business. As such, anyone exploring AI adoption must understand the promised rewards and the potential risks of doing so, asking the right questions along the way to determine the right approach for their company.
Promised Rewards of AI
Large language models (LLMs) like ChatGPT have introduced AI to the masses and have been rapidly adopted by employees, students, and consumers alike to ease everyday tasks. ChatGPT and other generative AI-powered chatbots allow people to ask questions in plain language, meaning using it is simple and doesn’t require much technical skill.
Beyond ease of use, the technology promises:
- Increased productivity: With LLMs helping workers do everything from coding faster to digesting large quantities of research in minutes, workers are now free to focus on more strategic tasks, instead of time-intensive ones.
- Operational efficiencies: By embedding generative AI tools like chatbots into software or processes, organizations can get the information they need to execute on tasks quickly or automate processes altogether – significantly streamlining workflows.
- Cost savings: When time is saved and employees are more productive, organizations can reduce spend while also increasing profit.
Potential Risks of AI
While glamorous at first look, AI does not come without pitfalls. Understanding these pitfalls – and the risks they pose to businesses – can help prevent misuse, bias, security breaches, and more. A few risks organizations should be aware of:
- Data sharing: To get the answers relevant to a specific business or function, that organization needs to first give an LLM data. For example, if you want ChatGPT to write a summary of a meeting, you will have to share the meeting transcript. This means that if proprietary information gets shared with an LLM, it remains in the LLM’s knowledge system and can be accessed by other users. This is typically outlined in an End User License Agreement – which many users typically sign without much thought.
- Bias or inaccurate answers: Answers provided by LLMs have been known to be biased, inaccurate, or made up. Understanding where the model is getting the information from and reviewing the information with an air of caution before using it can help identify any of these risk factors before they cause harm.
- Copyright infringement: It is also important to remember that anything produced by generative AI is considered part of the public domain – so, putting your name on something produced by ChatGPT could lead to copyright troubles down the line.
Deciding the Right Approach
Deciding whether to use this technology will not be black and white. Luckily, there are steps organizations can take as they consider next steps that can help guide them:
- Define the use case: Organizations should take a step back and ask themselves why they want to adopt the technology – is it to improve specific processes? If so, which ones?
- Determine desired outcomes: Once the use case is defined, a business should put specific metrics around it to understand how they will define success.
- Select a tool and start small: There are a lot of generative AI tools available, and organizations must decide which ones to adopt that will best serve the use case and deliver the desired results. From there, the tool should be given to a small group of people as proof of concept before scaling.
- Measure, iterate, and improve: Like any new process, things may not go smoothly the first go around. But measuring against success, understanding employee (or customer) feedback, and making adjustments along the way may help ultimately decide if this is a tool worth expanding within an organization, or scrapping altogether.
If an organization is leaning toward adopting generative AI tools, implementing employee education, and establishing company policies are crucial steps to mitigating the risks of doing so. In terms of education, businesses should consider providing company-wide training, informational sessions like webinars, office hours for questions, and consistent communications around the tool’s use. Likewise, company-wide policies should be rolled out, clearly defining acceptable tools, acceptable use, data that can and cannot be used with the tool, and how violations will be handled.
The adoption of AI is dependent on a business’s needs and will look different for each company. Determining what level of risk and reward is appropriate may not be a simple task, but if done correctly, it will open up the right conversations that increase security and innovation across the organization.
About the Author
Michael Gray is the Chief Technology Officer at Thrive, a leading managed security services provider. Michael has served as a strong technology leader at Thrive over the past decade, contributing to the consulting, network engineering, managed services and product development groups while continually being promoted up the ladder. Michael’s technology career began at Dove Consulting and later at Praecis, a biotechnology startup that was acquired by a top-five pharmaceutical firm in 2007. Serving in his current role, he is now responsible for Thrive’s R&D and technology road-mapping vision, while also heading the security and application development practices. He is a member of several partner advisory councils and participates in many local and national technology events. Michael has a degree in Business Administration from Northeastern University and he also maintains multiple technical certifications including Fortinet, Sonicwall, Microsoft, ITIL, Kaseya and maintains his Certified Information Systems Security Professional (CISSP).
Michael can be reached online at [email protected] and at our company website https://thrivenextgen.com/
