Last year saw the full power of artificial intelligence and machine learning leap from the hands of programmers and IT specialists into the hands of consumers. This is how the world – including business leaders at every level – realized how revolutionary this technology would prove to be. In miniature, artificial intelligence and machine learning (ML) will redefine work processes, augment productivity and augment the amount of content that companies are able to produce to meet individualized customer needs.
The democratization of AI, powered by modern, publicly available tools and platforms, is a double-edged sword for companies. On the one hand, it offers unprecedented opportunities for innovation, efficiency and profitability. It enables enterprises to harness the power of advanced technologies without significant investment in specialized knowledge. However, this democratization can also come with a myriad of risks that companies must navigate carefully.
As AI tools become widely available and AI companies enable deeper integration for enterprises around the world, the risk of errors and misuse increases significantly. Let’s take a look at where these threats occur and how companies can protect against them while unlocking the transformative power of AI.
Ensuring data security
With the democratization of AI and machine learning tools, existing data security and privacy challenges are not being alleviated; are sharpened. Companies are entrusted with expansive amounts of sensitive information, and the democratization of artificial intelligence increases the likelihood of unauthorized access or misuse of this data. The accessibility that makes AI tools attractive also increases the potential for cyber threats. This puts companies at risk of data breaches, intellectual property theft and regulatory non-compliance.
As companies incorporate 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. Artificial intelligence and machine learning require data to train, so it is the responsibility of companies to ensure that the data used to train these models remains in their own environments. They must be able to own their AI models and maintain full control over customer data and other information.
Avoiding excessive dependence on a single AI vendor
In addition to data security, today’s enterprises must be cautious about the risk of becoming overly dependent on a single AI tool. Given the nascent stages of many current AI tools, it is possible that the companies behind such technologies, if they have not already done so, could face financial instability or legal challenges. These challenges can threaten the continuity and reliability of the AI tool itself. If the company responsible for a given tool found itself in a situation of financial instability or difficulties in the form of legal disputes, this could result in the cessation of updates, maintenance and support for the tool. In this scenario, enterprise-level users may be using antiquated or vulnerable technology. Ultimately, this could disrupt various sectors that have incorporated AI into their operations.
To mitigate this risk, a differentiated, collaborative approach to developing and deploying AI tools is imperative. The business community must ensure that the failure of any single entity does not have disproportionate consequences on the broader technology landscape. Enterprises should look for partners that approach AI, machine learning and gigantic language models (LLM) from an agnostic point of view. This means they support multiple models while ensuring that the ones an enterprise uses are relevant, sustainable and well-supported.
Controlling quality and return on investment
Finally, it is worth noting that just because a company can automate a given task, it does not mean that it should. The return on investment (ROI) or quality of results may not be sufficient for the company’s needs. ML models are pricey. Many organizations experimenting with these tools find that they are either too pricey or not reliable enough to move into full production or exploit.
Assessing the value, reliability, and quality of AI and ML implementations can be a sophisticated endeavor. Enterprises need to look for partners who can assist them understand whether a tool’s results are sufficient for their goals and reliable over time. Moreover, these partners can assist enterprises ensure that they are implementing the right workflows to solve their problems and ensure appropriate checks and balances.
In the coming years, we will see an explosion in the number of custom and specialized machine learning models emerging around the world. This means that today’s companies must place an emphasis on understanding where these tools can best be applied in their organizations. They need to be confident that they are providing the required security, reliability and value. While the democratization of AI holds great promise, enterprises must remain vigilant in addressing its associated risks to ensure responsible and sustainable integration of these technologies into their operations.
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