Artificial intelligence has a hype problem. The technology earns attention for its rapid developments and unique applications, but there is still a major gap when it comes to real-world implementations. In fact, Gartner data shows that while nearly half of CIOs plan to use AI, only 4 percent have actually started to implement the technology.
So why the lag? AI faces big barriers when it comes to enterprise adoption due to return-on-investment (ROI) concerns and workforce fears that companies design the technology to replace their jobs. These challenges are a result of poor planning strategies when it comes to AI implementation and a misguided understanding of what is required to truly leverage the technology for the betterment of the whole organization.
When implementing any type of technology, one of the biggest hurdles businesses face is clearly articulating both long-term and short-term expected ROI. For AI, this challenge can sometimes be exacerbated due to a limited understanding of its capabilities and, more importantly, how insights from the technology translate into existing goals and the bottom line. AI requires initial investments in time and financial resources, and without a firm ROI, it can be challenging to convince the executive team that the implementation will affect the business positively. This is what can lead to stalls or limited implementations of AI.
Instead of just implementing AI because it is supposed to improve the efficiency and accuracy of business practices, businesses need to take the time to better understand why they need the technology in the first place. For example, will implementing an AI solution improve the productivity and success of financial industry traders, or help compliance teams more effectively monitor for criminal activity? Once you’ve established the overarching objective, set up key metrics for meeting them. This means you will need to identify every part of the AI process in the business — from data collection to model application — and measure each part using the right benchmarks or key performance indicators (KPIs) that map the process back to ROI.
Among the workforce, there is sometimes an assumption that companies design the technology to surpass employees, and eventually replace them. This creates an unnecessary competition between man and machine, with subject matter experts (SMEs) not interested in working with or better understanding a technology that they think is intended to make their role in the company obsolete. The truth is that most companies design AI to enable, assist, and augment workers, not replace them. By completing menial tasks that take up people’s valuable time, the technology creates more opportunities for the workforce to innovate, excel, and evolve.
To effectively display the benefits of AI, business leaders should conduct small proofs of value (PoVs) for a given organizational problem, with a focus on a specific goal and its ROI. Rather than a slow “behind closed doors” roll-out, businesses should allow workers and SMEs to be involved right from the start. This will help them understand the ROI and its impact on the business as well as their everyday lives, and could turn AI skeptics into evangelists.
Ensure data interpretations are accurate, relatable, and evolving
Outside of hesitations regarding ROI or the future of the workforce, one challenge those looking to implement AI face is ensuring analysts are correctly interpreting the data to meet the needs of the business. While it is easy to complete a data input and run an algorithm to produce results, it can be much more difficult to then take those results and ensure they actually mean something to the business. This is why it’s important to understand different models of AI to ensure the types of results the machine produces add new value to existing internal processes — while also making certain that personnel is accurately digesting and applying the new insights.
The beauty of an AI algorithm is that companies can design it to learn in a continuous mode. Therefore, it is incredibly important that AI processes not only translate into valuable insight but are also agile in nature and have a system of checks and balances to ensure the data produced best reflects the needs of the business. This includes understanding how false positives factor into the use of AI and which performance metrics make the biggest impact (either false positives or false negatives) on business efficiency and innovation.
While an AI solution may seem to be a one-size-fits-all product, it’s important to remember that the strategy behind the technology needs to be as unique as the business itself. Understanding the true value and impact of AI on both the company’s workforce culture and its bottom line will begin to close the gap between implementations of the technology and ensure optimized use.
Read the original of this article on the VentureBeat website.