Artificial Intelligence (AI) has earned a reputation as a silver bullet solution to a myriad of modern business challenges across industries. From improving diagnostic care, to revolutionizing the customer experience, there are many industries and organizations that have experienced the true transformational power of AI.
However, that’s not the case for the masses. And organizations that view AI as a fix-all are missing a huge opportunity—and are also likely to encounter significant challenges. When AI is applied in a way that overemphasizes its strengths and downplays its weaknesses, that’s when we run into problems.
While we tend to hear more about innovative, breakthrough AI use cases, the real value of AI lies in its ability to vastly improve operational efficiency. Is it less exciting than AI writing and producing its own songs or creating fine art in a matter of seconds? For sure. But for most businesses, a catchy tune or pretty picture aren’t going to move the needle.
The strengths of AI in modern business
AI’s ability to automate tasks, reduce errors, and make data-driven decisions at scale are its best lauded strengths. From predictive analytics to natural language processing (NLP), AI-powered applications enable faster and more accurate decision-making. In other words, the allure of AI lies in its ability to process vast amounts of data quickly, identify patterns that might be invisible to humans, and adapt to new information in real time.
These capabilities are undeniably valuable. In sectors like finance, healthcare, and manufacturing, AI-driven solutions have already proven their worth by optimizing supply chains, improving risk management, and enhancing customer service. For instance, in manufacturing, AI can predict equipment failures before they happen, allowing for preventive maintenance that reduces downtime and costs.
The limitations of AI
On the flip side, AI-driven solutions may struggle to account for the nuanced and context-dependent nature of human behavior. For example, an AI system might flag a legitimate login attempt as suspicious simply because it does not fit the typical pattern of a user’s behavior. In such cases, human oversight is crucial to avoid disrupting business operations.
One of the areas in which AI limitations are particularly evident is identity management. Identity management involves verifying and managing user identities within an organization, ensuring that the right individuals have access to the right resources at the right times. It’s a critical function for maintaining security and compliance, especially as businesses become increasingly digital and global.
AI can certainly play a role in identity management, such as by automating identity verification processes, detecting fraudulent activities, and managing access controls dynamically. However, these applications are not without challenges. AI systems rely heavily on the quality of the data they are trained on. If the training data is incomplete, biased, or outdated—and for most organizations, it is—AI-driven decisions will reflect these flaws.
AI as an enabler of operational efficiency
While AI is not a perfect solution, it can significantly enhance operational efficiency when applied correctly. The key is to recognize AI as a tool that works best when augmenting technologies and processes, rather than a silver bullet. Let’s continue on with the example of identity management.
AI can help organizations streamline identity by automating routine tasks, such as password resets or access requests. This can free up IT staff to focus on more complex issues, improving overall productivity. AI can also assist in monitoring and analyzing user behavior patterns over time, providing insights that can help organizations fine-tune their security policies.
In addition to identity management, AI can drive operational efficiency in other areas as well. For instance, AI-powered chatbots can handle a significant portion of customer inquiries, reducing the workload on human agents. In supply chain management, AI can optimize inventory levels and predict demand more accurately, reducing waste and improving responsiveness to market changes.
However, to truly unlock AI’s potential, organizations need to invest in the right infrastructure and skills. This includes ensuring that their data is clean, accurate, and up-to-date, as well as training staff to work alongside AI systems effectively. It also means adopting a mindset of continuous improvement, where AI is regularly evaluated and updated based on feedback and new data.
So, what now?
While the demand for AI and AI-enabled software is clear, what exactly customers expect from it is another story. The hurdle of getting AI clean and verified is actually one area AI could be quite useful in helping with, automatically generating information from places like the ServiceNow Data Warehouse, Slack, and other business applications. But then what?
A very real piece of the puzzle with both AI adoption and success is that many customers don’t know what they want. Think about other transformational technology, like the iPhone. There weren’t customers involved in its development. People didn’t know they wanted or needed it until they saw it in action. In many ways, enterprise AI is the same.
This, however, is a double-edged sword. The pressure to go all-in on AI is there, but people aren’t able to figure out what is possible within their organization or if customers are seeing the value yet. Many are asking if the money is worth it and if other companies are seeing value. To keep it consistent with the identity example, leading vendors in the space are touting their AI capabilities, yet, are unclear about what these are and how customers benefit.
AI has the potential to drive significant operational efficiency across various business functions, including identity management. But it has yet to prove itself. Organizations that approach AI with a clear understanding of its limitations and strengths are more likely to succeed. In doing so, they can achieve a more realistic approach.
