Last year saw the power of artificial intelligence and machine learning suddenly move from the hands of developers and computer scientists into the hands of consumers. In the process, the world, including business leaders at all levels, realized how revolutionary this technology could be. In the short term, AI and machine learning (ML) will redefine work processes, improve productivity, and increase the amount of content businesses can create to meet the individual needs of their customers.
The democratization of AI, facilitated by newly released tools and platforms, is a double-edged sword for businesses. On the one hand, it offers unprecedented opportunities for increased innovation, efficiency, and cost-effectiveness, allowing companies to harness the power of advanced technology without having to invest heavily in expertise. However, this democratization can also bring about a myriad of dangers that businesses must navigate carefully.
As AI tools become more widely used and AI companies bring deeper integration into businesses around the world, the risks of failure and misuse increase significantly. Let's consider where these dangers lie and how businesses can protect themselves against them while unleashing the transformative power of AI.
Ensuring Data Security
The democratization of AI and ML tools has exacerbated, not alleviated, traditional challenges around data security and privacy. Businesses hold vast amounts of sensitive information, and the democratization of AI increases the likelihood that this data will be accessed and misused. The accessibility that makes AI tools so appealing also increases the likelihood of cyber threats, putting businesses at risk of data breaches, intellectual property theft, and regulatory non-compliance.
As companies integrate AI into their operations, they must prioritize robust cybersecurity measures and ethical considerations to protect their assets and maintain the trust of their customers and stakeholders. Because AI and ML require data to learn, it is the responsibility of companies to ensure that the data used to train these models stays within their own environments. Companies must own their AI models and have full control over customer data and other information.
Avoid over-reliance on a single AI provider
Beyond data security, businesses today should be cautious about relying too heavily on a single AI tool. Many of today's AI tools are in their early stages, and the companies behind such technologies may face financial instability and legal challenges, if they have not already. These challenges could jeopardize the continuity and reliability of the AI tool itself. If the company responsible for a particular tool becomes financially unstable or is hampered by legal disputes, updates, maintenance, and support for the tool may cease. This scenario leaves enterprise-level users saddled with outdated or vulnerable technology. Ultimately, it could lead to disruption in various sectors that are integrating AI into their operations.
To mitigate these risks, a diverse and collaborative approach in the development and deployment of AI tools is essential. The business community needs to ensure that the failure of a single organization does not disproportionately impact a broad area of the technology. Companies should seek partners that approach AI, ML, and large language models (LLMs) from an agnostic perspective, which means supporting multiple models while ensuring that the models used by a given company are appropriate, sustainable, and well-supported.
Quality and ROI Management
Finally, it is important to note that just because a company can automate a certain task does not necessarily mean they should. The return on investment (ROI) or quality of the output may not be sufficient for the needs of the business. ML models are expensive. Many organizations that trial these tools find them too costly or not reliable enough to move into full production or use.
Evaluating the value, reliability, and quality of AI and ML implementations can be a complex task. Companies need to look for partners who can help them determine whether the output of a particular tool is sufficient for their purposes and can be trusted over time. Additionally, these partners can help companies implement the right workflows to solve problems and ensure the right checks and balances are in place.
Over the next few years, we expect to see an explosion in the number of customized and specialized machine learning models, which means businesses today must be focused on understanding where these tools can be most effectively applied within their organizations. They must ensure they deliver the security, reliability, and value required. The democratization of AI holds great promise, but businesses must remain vigilant to the associated risks to ensure they are integrating these technologies into their operations responsibly and sustainably.
About the Author
Dr. Simone Bohnenberger-Rich is Chief Product Officer at Phrase, the global leader in AI-driven translation technology. She joined Phrase after five years at Eigen Technologies, a B2B no-code AI company helping users solve their toughest data problems, ultimately serving as SVP of Product. Prior to Eigen, she spent many years in strategy consulting at Monitor Deloitte, advising clients on growth strategies at the intersection of data and technology.
Sign up for the free insideBIGDATA newsletter.
Join us on Twitter: https://twitter.com/InsideBigData1
Join us on LinkedIn: https://www.linkedin.com/company/insidebigdata/
Join us on Facebook: https://www.facebook.com/insideBIGDATANOW
