Three Things Businesses Should Know Before Jumping Into AI In 2024: Matrix Integration Experts Provide Tips to Take the Leap Wisely

Major analyst firms predict AI will add trillions of dollars to the global economy by 2030. Last year, more than a third of global businesses were already using AI, and that number is continuing to grow quickly as businesses rush to employ AI to increase productivity and decrease costs.

Matrix Integration, a strategic IT infrastructure partner for more than 1,000 businesses and schools in the Midwest and beyond, is working with clients who want to employ AI into their operations, in uses ranging from cybersecurity to advanced manufacturing analytics.

“We have been leveraging AI tools in our strategic partner software suites for clients for several years. Customers turn to us for support in fine-tuning the automation capabilities within these suites to make critical decisions in their infrastructure,” said Tim Pritchett, engineer operations manager at Matrix Integration. “As time and resources continue to crunch in maintaining your IT systems and security, AI tools can be leveraged to protect your data and get the most benefit out of what you already own.”

Because AI becomes a more commonly built-in component of many managed software suites, here are the top three issues business should consider as AI becomes more universal:

1. Data quality matters. Whether businesses are using AI to generate content (such as drafting communications with customers) or analyze production efficiencies, high-quality data is necessary to train AI models. Already, biased inputs in large-language models like ChatGPT have led to biased outputs that could damage a company’s reputation on a great scale. In the case of data analysis, inaccurate or damaged data fed to an AI model will lead to unusable outputs.
2. Data security isn’t guaranteed.  Companies will need to consider how they will secure their own data, as well as data supplied by clients. This requires asking questions and developing transparency and trust with cloud services providers as well as AI vendors. For example, many businesses provide customer-facing chatbots run by AI. For example, imagine that customers type sensitive or personal data (e.g., bank account numbers) into a chatbot. Or, as another example, a business supplies internal data to AI models to generate proprietary operations solutions. Is that data safe once it gets uploaded into a cloud-based AI application? Can it be used by other customers of that AI vendor?
3. Humans are key for AI to work properly. Right now, much of AI seems to be a “black box” – most people understand the inputs and outputs but are unfamiliar with how learning algorithms work and how they handle data. For example, Microsoft 365 security tools through Defender, Sentinel, or the Purview compliance portal all do an excellent job of leveraging AI to make decisions and inform IT administrators on the best decisions to make in a scenario. However, experienced security professionals can still play a key role by fine-tuning these notifications and building automation for these tools.

Businesses looking for more information on issues to consider before incorporating AI into the business and other network and cybersecurity concerns, can visit matrixintegration.com.