The Democratization of AI: 3 Dangers Business Leaders Must Confront

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Last year, the full power of artificial intelligence and machine learning leapt from the hands of developers and computer scientists and into the hands of consumers. In doing so, the world—including business leaders at every level—realized just how revolutionary this technology would prove. In short order, AI and Machine Learning (ML) will redefine work processes, elevate productivity, and amplify the volume of content that businesses are able to produce to serve the individualized needs of customers.

The democratization of AI, facilitated by new publicly available tools and platforms, presents a double-edged sword for companies. On one hand, it offers unprecedented opportunities for innovation, efficiency, and cost-effectiveness. It allows businesses to harness the power of advanced technologies without substantial investments in specialized expertise. However, this democratization can also bring forth myriad dangers that companies must navigate carefully. 

As AI tools become widely available and AI companies unleash deeper integrations for businesses worldwide, the risk of missteps and misuse rises significantly. Let’s examine where these dangers exist and how companies can protect against them, while still unlocking the transformative power of AI. 

Ensuring Data Security

With the democratization of AI and ML tools, legacy challenges around data security and privacy aren’t being alleviated; they’re being exacerbated. Companies are entrusted with vast amounts of sensitive information, and the democratization of AI increases the likelihood of unauthorized access or misuse of this data. The accessibility that makes AI tools attractive also amplifies the potential for cyber threats. This puts companies at risk of data breaches, intellectual property theft, and regulatory non-compliance. 

As businesses integrate AI into their operations, they must prioritize robust cybersecurity measures and ethical considerations to safeguard their assets and maintain the trust of their customers and stakeholders. AI and ML require data to learn, so it’s the responsibility of companies to ensure the data being used to teach these models remains within their own environments. They must be able to own their AI models and maintain complete control of their customer data and other information. 

Avoiding Overdependence on a Single AI Provider

Beyond data security, today’s enterprises must be cautious when it comes to developing an overdependence on a single AI tool. Given the nascent stage of many of today’s AI tools, it’s possible the companies behind such technologies, if they don’t already, could face financial instability or legal challenges. These challenges could jeopardize the continuity and reliability of the AI tool itself. If the company responsible for a given tool were to become financially unstable or hampered by legal disputes, it could result in the discontinuation of updates, maintenance, and support for the tool. This scenario could leave enterprise-level users with outdated or vulnerable technology. Ultimately, this could disrupt various sectors that have integrated the AI into their operations. 

To mitigate these risks, a diversified and collaborative approach in the development and deployment of AI tools is essential. The business community must ensure that no single entity’s failure has disproportionate consequences on the broader technological landscape. Enterprises should seek partners who approach AI, ML, and large language models (LLMs) from an agnostic standpoint. This means that they support multiple models while ensuring the ones used by a given enterprise are appropriate, sustainable, and well-supported. 

Controlling for Quality and ROI 

Finally, it’s worth noting that just because a company can automate a given task doesn’t mean it should. The return on investment (ROI) or quality of the output might not be sufficient for a business’s needs. ML models are expensive. Many organizations that experiment with these tools discover that they’re either too costly or not reliable enough to move into full production or use. 

Gauging value, reliability, and quality of AI and ML implementations can be a complicated endeavor. Enterprises need to seek out partners that can help them understand if the outputs of a given tool are sufficient for their purposes and reliable over time. Furthermore, these partners can help enterprises ensure that they’re implementing the proper workflows to solve their problems and ensure the proper checks and balances. 

In the coming years, we’re going to see an explosion in the number of customized and specialized machine learning models that are entering the world. That means companies today need to be putting an emphasis on understanding where best these tools can be applied within their organizations. They must ensure they’re delivering the security, reliability, and value required. While the democratization of AI holds immense promise, businesses must remain vigilant in addressing the associated risks to ensure the responsible and sustainable integration of these technologies into their operations.

About the Author

Simone Bohnenberger-Rich is the Chief Product Officer at Phrase, a leader in cloud-based localization technology. She joined Phrase in 2023, bringing a wealth of experience from her previous roles in AI and product leadership. Simone’s career highlights include a significant tenure at Eigen Technologies, a B2B no-code AI company empowering users to solve challenging data problems. In her time at Eigen, Simone held several pivotal roles, most notably as Senior Vice President of Product. Her main focus was on leveraging AI and machine learning for natural language processing, contributing to the company’s growth from a startup to a successful B2B entity.

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