Author(s): Dr. Murat Açar
1. “Artificial intelligence is a fad/hype/buzzword.”
First, the elephant in the room rises, and there is no room to pretend not to see it. About the organizations and people thinking about artificial intelligence (AI) as a fad/mere hype/buzzword, I have this one:
No more fresh water of basic slides, go for the deep ocean of applied science.
With that, the ones having a concise and applied AI plan will realize the actual benefits and close the opportunity gap in the digital era with solution-wise better and financial-wise effective outcomes. Others following pseudo-AI (fake it till you make it kind of folks) will enjoy the ROI (risk of ignoring) of AI.
This myth also caused instability across the titles that organizations assign to their C-suite and business lines (e.g., Chief Digital, Chief AI, Chief Transformation, Chief Data Officers).
The ultimate understanding resides here: How would you explain when life does not develop in line with your assumptions? Or, let’s say that, how would you explain others were doing things like they were challenging all assumptions?
2. “Bringing data scientists into the team will address all AI concerns.”
This is a common mistake. Organizations should not look for decades of experience in any given field of science if the entire organization is new to that field. Culture will eat those kinds of unconscious attempts.
First, we need advocates to focus on people, character, and talent, not tech per se. Transformation starts at the individual level. In response, you are right to say that “Speed is important.” but that consideration is due to the fact that you feel FOMO, organizational isomorphism, and speed hunger as a result of digital disruption. When organizations see the AI show-offs by disruptors, they impatiently consider it as an overnight success/fail.
Once you build the foundation for an appropriate digital culture, you can first elaborate on leaner-faster-better AI initiatives. Finally, among the 5W1H questions about AI, “Why” and “How” are critical instead of “What.” We should not directly rush into learning the new digital technologies (an attempt to What is it?). Rather, we should focus on “Why” and “How” those technologies popped up nowadays, not a decade ago, though they were there for decades in the literature. With an analogy, one cannot learn a new language by memorizing its vocabulary (i.e., What), whereas s/he can learn by observing “Why” and “How” the words/sentences are used.
3. “No budget for a team/innovation/transformation, no need for AI.”
Have you ever heard of virtual data scientists? Or data-science-as-a-service? Today’s world allows us to access any kind of information with almost no cost. The ever-growing mentions and needs for data scientists are due to the information asymmetry among the executives in an organization. CXO said, “We need data science.”, then HR said, “Search the keyword: data science.” With that, data scientists were considered aliens during those times, and there was a tendency to put data science in the titles. Fortunately, those days are almost over.
The keyword people are unaware of the fact that there are scientists who devoted their lives to data analytics, machine learning (ML), natural language processing (NLP), and AI. So, it is not as simple as a keyword search to reach people who are literally knowledgeable. You can, of course, come across the ones who are figuratively pretender.
4. “We need to buy AI, cannot build it.”
This is simply running-away from AI if there is not any sound basis for a buy decision. Before your monetization moves in the AI journey, you need to come up with a well-grounded decision of buying vs. building.
I associate those groundless buy decisions with fixed mindsets, whereas build itself is an example of a growth mindset. So, it sounds good and easy to lean back the high-tech vendors/providers by acquiring their products/services about AI, but then the organizations’ growth will be limited in terms of know-how, intrapreneurship, and continuous learning. Besides, the organizations will not be able to beat the uncertainty avoidance dimension in their culture and challenge the status quo if they throw the ball to others.
Yes, AI is uncertain for some of the individuals, and they may look for ways to avoid it (e.g., one who has spent decades on business intelligence, ERP, and CRM and observed the indefinite mass they built, can prefer staying in the comfort zone). At least, try to build it yourself, experiment fast, and fail fast. This is the restless reinvention in the digital era. Then, you will realize/monetize faster.
5. “Mentioning AI is an attempt to AI.”
Dear advertisers, please be informed that when you mention AI on your ads, you are not bringing any value to add, and there is not any scientific significance of doing that. If I were you, I would spend that ad budget for growing a team and trying to build a culture capable of dealing with AI. If there is a scientific effort, well, you are good to go, showcase it! But if not, please hesitate using the term AI (e.g., reading a QR code is definitely not AI).
What is more, there is a misconception about the place of AI, ML, NLP, and data analytics in the scientific literature, but AI is chosen most of the time for any attempt, thanks to its fancy impression. Okay, you choose Head of Machine Learning or Head of Artificial Intelligence. The latter sounds better, right? So, please be skeptical about whoever tells you to put AI phrases anywhere in the ads or any form of presentation.
Debunking the Myths about Artificial Intelligence was originally published in Towards AI — Multidisciplinary Science Journal on Medium, where people are continuing the conversation by highlighting and responding to this story.
Published via Towards AI