Will the Real AI Please Stand Up?
- By Prasad Ramakrishnan, Freshworks
- April 01, 2024
Not all AI solutions are built the same. However, some offerings advertised as AI-driven lack true AI capabilities, such as natural language processing and machine learning. This comes as more tech providers look to capitalize on the AI hype to market their solutions. However, this practice only serves to give AI a bad name, not to mention cause organizations to mistakenly acquire tools and programs that fail to live up to the hype.
For IT teams looking to augment their operations, greater scrutiny must be exercised when choosing AI solutions.
Distinguishing fake from genuine
Across the Asia Pacific, 80% of CIOs will embrace AI to improve agility and enable insight-driven operations, according to an IDC report. However, prudence is key, or organizations risk sinking costs into tools that don't empower their digital transformation journeys.
To find the right solutions, IT practitioners should look for concrete evidence of the AI tool's functions and value before deciding. This includes gathering information on the solutions' features, their performance, and the best practices involved in the development process. These details need to come from an independent third-party source that is not engaged with the developer so that organizations can assess the solutions' capabilities more accurately. Simultaneously, IT teams must also analyze the developers' standards to ensure they can deliver clear and consistent results.
With this information, organizations can make better decisions and choose the right tools for their operations. This can empower employees to deliver impactful customer conversations and reduce their tasks, leading to greater acceptance of AI solutions. Moreover, organizations can avoid needless spending and reduce software bloat, which otherwise hampers operational performance.
A reality check on chatbots
One such example of shallow AI solutions can be seen in some chatbot offerings that rely on pre-programmed responses and basic rule-based algorithms to deliver automated replies to customers' questions. They are not equipped with true AI capabilities like natural language processing or machine learning (ML) that can create human-like engagements. This, in turn, makes it difficult for organizations to deliver exceptional and empathetic experiences that give customers the impression that the brand cares about their needs.
For those looking to augment customer interactions through AI, IT teams need to sit down and assess all the available options deliberately so they do not give customers canned responses. Achieving this requires IT teams to test each solution internally and gradually incorporate them into customer service operations while monitoring performance. These steps can lead to incisive conversations, which, in time, maximize brand loyalty and drive more customers to businesses' front doors.
AI-driven virtual assistant—or not?
Virtual assistants that can help users manage their schedules are another solution requiring greater scrutiny. This is especially the case as some solutions utilize rule-based algorithms and predefined templates to handle scheduling tasks. While these virtual assistant models can translate specific keywords and phrases into simple scheduling actions, they cannot remember preferences and adapt to changes. As a result, users are forced to do the extra work of manually adjusting their schedules.
On the other hand, a true AI-powered virtual assistant can learn from problems and situations and identify effective responses to handle them. To determine whether a virtual assistant really uses AI, IT teams should test it by having business employees communicate with the platform and see if they can complete certain tasks based on the provided instructions. Furthermore, IT teams should also assess whether the solution can adapt to new and unfamiliar events.
Putting trust in innovation
On the AI developers' end, ensuring a trusted ecosystem is critical to building practical solutions that can deliver on their potential. For this reason, developers must comply with the guidelines of the Model AI Governance Framework for Generative AI to lay the groundwork for more responsible AI development.
One of the most important steps developers need to take is to understand their responsibilities toward the end user. This includes being transparent about their development process and reporting incidents as early as possible so that users can take steps to protect themselves. By taking this factor into account, developers will be able to better align their solutions with their clients' needs.
Distinguishing between real and shallow AI solutions may be a time-consuming process. Still, it is vital to ensure employees are working with the right capabilities to meet customers' needs. Not only that, but it also boosts cost savings and prevents unwanted solutions from bloating operations. These factors combined can help organizations stay ahead of the pack throughout 2024 and beyond.
The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Image credit: iStockphoto/valiantsin suprunovich
Prasad Ramakrishnan, Freshworks
Prasad Ramakrishnan is the chief information officer and senior vice president of IT at Freshworks.