AI has become mainstream in today’s workplace in a short time.
AI has moved beyond innovation labs and is now globally acknowledged for its ability to transform processes and businesses. From redesigning operations and boosting efficiency to enhancing productivity and improving customer satisfaction, a variety of use cases help drive revenue. In fact, 26% of IT leaders predict that AI will be the primary driver of IT investment in the coming years, according to the CIO’s State of the CIO 2023 report.
On the other hand, many real-life examples also illustrate how some AI systems fail to consider human needs and values, serving as cautionary tales. As more businesses adopt AI, they must be aware of the obvious traps that come with AI implementation.
Bidish Sarkar
Traps Associated with AI Implementation
Incorrect and biased outcomes, increased risk of noncompliance, and budgetary challenges due to an increase in the total cost of ownership are the top three traps organizations must avoid when implementing AI.
AI systems producing incorrect and biased outcomes often occur due to “AI hallucinations,” instances where the training data and algorithms result in biased outcomes. The example of iTutorGroup shows how businesses can avoid the negative consequences of bias and, thereby, the hefty penalties that follow.
The risks of noncompliance are further compounded with hackers and cybercriminals increasingly using AI tools and platforms. For example, a finance employee recently fell prey to a deep fake video scam using AI, resulting in a huge financial loss to the company. This incident stands as one of the largest AI-driven heists worldwide, underscoring the need to further sensitize employees and intensify compliance measures against such emerging AI-driven threats.
Lastly, accounting for the increase in total cost of ownership that comes with implementing AI solutions can lead to budgetary challenges. Incremental growth of AI requires changes in data center designs for higher computational demands, necessitating denser setups. Take Meta, for example, which announced a pause in construction on two data center projects to redesign them for deploying AI infrastructure.
Avoid AI Traps
Avoiding these AI traps will require businesses to ensure responsible AI deployment.
Organizations must conduct thorough impact assessments to detect biases and control costs. Governance and oversight structures will prove crucial for building consumer trust. Leveraging Explainable AI will further help them and key stakeholders by improving transparency in the decision-making process of AI models.
Small and midsized companies (SMBs) can collaborate with MSPs to streamline resources, enhance security, improve efficiency, and comply with industry standards.
In AI systems, security and compliance rely on the foundational architecture and the implementation of guardrails — policies and procedures — to prevent risks. These ensure AI systems operate within defined parameters, reducing the risk of data breaches and other security incidents. Investing in compliance management and robust cybersecurity initiatives will be the key to avoiding the traps associated with regulations in the long run.
As the total cost of ownership increases with AI implementation, managing infrastructure demands a dedicated team. MSPs offer crucial expertise in this area, aiding in infrastructure management, updates, and resource optimization. Engaging various stakeholders — like ethicists, community representatives, and end-users in AI development — to consider diverse viewpoints and concerns will further help organizations solidify their AI investments in the long run.
Human-centric AI is the Key to Successful Implementation
AI is advancing at an unprecedented rate, presenting both opportunities and challenges for businesses. A human-centric AI approach will help businesses craft a robust AI strategy, establish governance and security measures, and reduce overall costs.
Strengthening data science capabilities will enable them to further embrace human-centric AI. The next few years will be critical for businesses to make the most of this technology.
Bidish Sarkar is senior vice president – data & analytics for Persistent Systems.
Image: iStock
