The following content was originally published by Business Reporter’s hub on Reuters.
Artificial intelligence (AI) can be found almost everywhere in modern life. Whether you’re receiving financial advice via a banking app, shopping online or troubleshooting computer problems with tech support, AI is likely to play a part in your daily activities. Indeed, 50 per cent of respondents to McKinsey’s State of AI 2020 survey reported that their companies have adopted the technology in at least one business function.
With the size of the global AI market valued at more than $60 billion in 2020, it’s clear that an incredible amount of money is being poured into this technology. However, companies should be wary of the misconception that AI in and of itself will deliver a return on investment (ROI). With such widespread adoption, key lessons and best practices are emerging to help companies avoid common AI pitfalls and achieve ROI from their AI systems.
Most common AI mistakes
AI is not a switch companies can simply “flip on”, and there’s no one-size-fits-all AI plug-in. Despite big investments and seemingly expert advice from knowledgeable vendors, many companies still make mistakes along the way. These mistakes have both tangible and intangible consequences, including loss of sales, unnecessary costs and, perhaps most importantly, a loss of end-user trust.
Check out the following video for a perspective about AI best practices from EastBanc Technologies Chairman Wolf Ruzicka.
Insufficient penetration: AI is more complicated to implement correctly than many companies realize at the outset. Designed to be part of a holistic business system, it will offer little benefit if only installed at a surface level. For example, many companies use AI in a chatbot function on their frontend. As these bots typically don’t have access to a company’s core systems, they are no help beyond the most basic of functions and are easily identified as non-human by customers. Without access to the right datasets on the backend, this use of AI will fail to make a meaningful impact on a company’s bottom line.
Incompatibility between different AI systems: Even those businesses that have incorporated AI into their core systems still aren’t guaranteed meaningful ROI. A company could be running multiple AI engines at once to support multiple business functions. Problems occur when these engines don’t communicate with each other effectively, or give conflicting results and advice.
Inability to go big: Small-scale AI will only offer small-scale returns. The inability to roll out the technology on a large enough scale holds many companies back from reaping the rewards of their investment. Interestingly, it’s often big organizations with unwieldy backends that struggle with this the most.
Vendor bias
Vendor bias is another reason why many organizations fail to get their money’s worth after investing in AI. Companies traditionally outsource the entire job to a single vendor that delivers an end-to-end solution. However, such huge and abrupt system overhauls are costly, slow and very risky. Most pertinently, this approach also leaves the company with no control or autonomy over the systems they come to rely on everyday. Vendors also naturally prioritize their own technologies, meaning that the vast majority of products on the market are excluded, even if they would provide the best solution for clients.
In contrast, thanks to a robust AI ecosystem, companies can select best-in-class products that can be implemented in a seamless and modular fashion to meet their unique needs. You can’t just set it and forget it when it comes to AI. Systems should be flexible and adaptable to incorporate the best that today’s rapidly changing market has to offer.
“Ultimately, what you really need to understand is that the core of this problem lies in the core of your business, not the technology vendor’s business,” says Wolf Ruzicka, Chairman of EastBanc Technologies, which helps companies customise and better leverage their existing AI systems. “Instead of having this technology bias, you must own up to the fact that you need to own your own technology destiny."
The solution
Only the company itself can drive a modular custom approach that perfectly complements its unique goals, value proposition and customer needs. But most companies don’t have this skillset within their existing talent pools. That’s where EastBanc Technologies steps in.
With more than 20 years of experience, the Washington DC-based team of software engineers puts its clients in the driver’s seat by enabling them to design, build and own their AI systems. Supporting and empowering every step of the way, the EastBanc Technologies team helps companies build modular custom software that quickly unblocks problems and delivers impactful returns.
The EastBanc Technologies team starts by identifying a “killer feature” – the unique selling point at the core of the business model that draws the end-user in and evokes emotion. Once the killer feature is identified, an AI module is integrated to enhance that feature. When this first feature is working as it should, other business applications and functions are brought online around the killer feature, progressively cleaning up and connecting data streams throughout the business to the AI systems. Unlike the traditional model, this incremental approach prioritizes organic permeation of AI. It is a fast, flexible and low-risk approach that’s laser-focused on ROI.
“All that companies really have to do is commit to not outsourcing this fundamental addition to their business,” says Ruzicka. “[They can add] components gradually on a very granular level to become AI leaders in their respective spaces.”