AI- Fad and Fiction or Reliable Tech?

Artificial intelligence is a buzzword that’s repeatedly costing enterprises because of the hype it creates.

In his expert Opinion piece in ComputerWorld, Bradley de Souza, a cross-industry professional in digital, change and transformation, opines that while technology has to become simpler and smarter, Artificial Intelligence as an enterprise technology is an increasing percentage of financial spend and yet costly mistakes occur frequently.

Most professionals even at the leadership level work inside traditional organizations and struggle to remain competitive in an unpredictable environment. Technologies like AI have been sold as the panacea for all issues that face CIOs; all may not be truthful about this claim. The biggest issue is- it hasn’t fully arrived yet! “Despite numerous references to the contrary, we are still no closer to true artificial intelligence. This fact hasn’t stopped the marketing machine from rebranding and realigning old solutions under the label of AI,” he points out.

Citing a real-life experience he has had, de Souza elucidated his problems. He was working in a team that was bogged down with mundane tracking and documentation. Believing that AI could be the solution, he was disappointed. “The scheduling AI varied from good to awful. The best solution used email to convince a hotel manager that it was a real person… None of the scheduling services on offer could access private corporate diary systems, only public, permission-based ones. The transcription services using AI were mythical and despite reviews to the contrary, none could be found.”

He adds that innovations and technology-enabled growth is a sign of a successful organization, and this forward movement does not depend on AI.

Also Read: The Compelling Myths of Low-Code/No-Code

Artificial intelligence is defined as intelligence demonstrated by machines and the term is applied when a machine mimics human functions, mostly repetitive tasks based on learning and problem-solving. In real life, the cinematic and media representation of AI brands does not really exist. There is no lifelike human-like bots and robots that are difficult to tell from humans. But the truth is that none of the tech companies have come even close to emulating the on-screen AIs.

There are organizations that claim they use AI, he said, for market gains. “The marketers have re-purposed phrases like automation and robotics to sell modern versions of old solutions. Everything from natural language search, big data analysis, collaborative filtering and the like, are being rebranded and sold as AI. There are even distinctions made between single and multi-purpose AI solutions. The latter, as yet does not exist and the former is likely to be a collection of well-known algorithms bundled together,” he states in the article.

One of the reasons for this dismal reality is because one loss makes stakeholder buying very difficult for the next round.  Issues such as past failures, cost overruns, overselling, lack of awareness- make the leadership and decision makers wary of taking a chance again.

“Ironically, many companies that lag behind the technology curve try to develop and deliver innovation themselves. These in-house efforts often fail due to a lack of preparation and experience. This perpetuates a vicious cycle which prevents such companies from progressing beyond the less developed stage. To check if real AI is involved in any new initiative, one needs to ask questions about components such as the size of the neural net and machine learning properties,”… he cites the fallacy of chatbots being considered as AI. “These are often natural language search engines presented in such a way as to simulate the now familiar text chatting experience. Previously the customer had to use a search page and then select from the results. Now, something similar occurs inside a chat window and is dubbed AI. In 2017, chatbots were heralded as the next big thing and would revolutionize the way customers and companies interact. What followed were widely reported chatbot failures from the likes of Microsoft, Facebook, and even Google. Some have suggested that chatbots are dead and that a lack of AI killed them,” he says.

So what should be the key study points before an enterprise adopts AI? De Souza elaborates:

Ascertain why an AI solution is being proposed. Ask for real-world success scenarios.

Also Read: Effective Data Warehouse Management Practices for Enterprises

Ask questions about the AI components e.g. size of the neural net, machine learning properties. Get advice from trusted independent technical experts.

AI solutions are being sold with inflated costs because they are touted as being superior. By logic, as they learn, and become more efficient, their costs should fall. Is that happening?

Perfect voice recognition is considered to be a key turning point that signals the birth of artificial intelligence. Currently, most AI solutions rely on processing data from text input but critics argue that true AI should be able to deal with inputs from any source.

And all enterprises need to be realistic about the fact that not all will become technology led organizations. Successful organizations already innovate and develop using technology, which may or may not be about AI.

Check Out The New Enterprisetalk Podcast. For more such updates follow us on Google News Enterprisetalk News.