A recent emerging jobs report by Indeed revealed the top 20 emerging jobs in the United States and bolstered the case for the next big tech revolution on the horizon: Big Data and Artificial Intelligence (AI) (ref. LinkedIn Research, 7 December 2017). Demand for tech jobs across the board had risen the most among all jobs; and at the top of the tech tree, machine learning engineers, data scientists, and big data developers experienced the highest growths in talent demand.

It is interesting to consider just how much impact these developments will have on businesses and industries in Malaysia, given the technology adoption gap between the U.S. and Malaysia. Granted, Malaysians have been using the terms “AI” and “Big Data” as catch-all phrases for all things related to tech development and progress (in-line with the growing trend), but given that a large proportion of Malaysian SMEs today still do not maintain a website or even transact online (ref. Fig. 1), could we be getting ahead of ourselves?

While technology hardware has penetrated more than 75% of SMEs, usage of basic online services is still lagging significantly behind. In particular, nearly 7 out of 10 SMEs do not maintain a website; will SMEs be left behind in the next tech revolution?

Fig. 1: ICT Adoption of SMEs in Malaysia, 2016/2017.

AI is quietly changing the way digital information is being processed and accessed right now, and the potential implications to business is intriguing.

Many may readily associate the word “AI” with “robots”, but AI is, in fact, more accurately associated with machine learning than its popular physical form.

While the disruption of robots to the economy are more easily visualized, the disruptions of machine learning are relatively more subtle. Thus it may come as a surprise that, from a machine learning perspective, the AI revolution has already begun with some intriguing implications for businesses, and it has to do with search engines.

In 2017, Google and Bing implemented significant shifts in the application of AI and machine learning in structured querying in digital advertising. The following are three shifts that we believe to be most significant, based on their potential future impacts to structured querying as a whole.

Shift 1. Keyword matching in structured querying has progressed from exact, to fuzzy, to near human-like matches.

Google announced that its ad engine Adwords is now capable of matching keywords, irrespective of variations in word order or function words in exact matching; previously, exact keyword matching could only handle close variants, such as plurals, typo errors, abbreviations, and adverbs. The following examples describe the engine’s new capabilities.

Previously: “pancake mix” matches to “pancake mixer”, which are obviously not the same thing.

Currently: Google is now capable of differentiating the two terms through contextual learning.

Previously: “running shoes women” does not match to “running shoes for women”.

Currently: Google is now capable of adding functions words—like the word “for”—such that the queries match.

Previously: “engineering mechanical” does not match to “mechanical engineering”.

Currently: Google is now capable of swapping word orders, provided that the meaning is preserved.

Shift 2. Digital ad engines can now construct targeted audience entirely on their own.

Adwords is now capable of inferring a target market’s characteristics automatically, based on the contents of a marketing campaign. This means that advertisers do not need to manually specify the characteristics of their target markets by allowing the ad engine to automate the process, if they so desire.

Shift 3. Digital ad engines can now generate ads based on their interpretation of content on a website.

Bing has rolled out dynamic search ads in the U.S. and U.K., earlier this year. Bing’s ad engine is capable of generating ads (automatically) based on the content of a website that it has crawled, including ad headlines, which it then matches to user queries.

“Keywords” is still the ticket to being discovered today, but “Digital Presence” may be more important in an AI future.

Granted, the shifts in search engines are nascent developments in the field of AI; but consider the potential implications when this technology fully matures:

1. Search Engine Optimization may no longer be a significant barrier.

Today, businesses need to be highly accurate with keywords in order to be found and ranked highly on search engines. But with the maturation of machine learning and search engines’ capabilities of mastering language flexibility, the need for keyword selection and optimization in search decreases.

2. Social platforms will likely become critical resources.

In order for machine learning to mature, it will necessarily require data sources—Big Data sources—to learn from. Platforms that are dense with time sensitive and personalized information, such as social platforms, will likely become important building blocks towards an AI future.

3. People will trust and depend on the recommendations of search engines, even more than they do now.

With contextual searching possible, results from search engines will become more relevant, reliable, personalized, and precise. Search engines will not only be an intermediary tool for information fact-finding, but a recommendation tool that internalizes your personal and unique requirements to solve problems, and their solutions will likely surpass the capabilities of human solutions. Reliance on machines for decision making will not be a “try”, but a “must”.

The Key Takeaway: To anticipate the business of the future, SMEs will do good by investing in their digital presence today.

The next tech revolution will bring technology to a point where it can legitimately compete with (and likely surpass) the precision and performance of humans, and one intriguing aspect is how AI and Big Data may potentially level the playing field for businesses. This may be particularly significant to SMEs, where many do not have the know-how, experience, and resources employed by larger companies to derive significant enough returns from venturing into the digital space—provided that SMEs have, at the very least, established some form of credible digital presence. Yet, as seen earlier in this article, the large majority of SMEs in Malaysia are still lagging well behind in this department.

Illustrative Comparison of SME ICT Adoption to Population, 2017

Fig. 2: Comparison of SME ICT Adoption to Malaysian Population, 2017 (for illustrative purposes only).

In anticipation of a new world order driven by AI and Big Data, the decision to delay venturing into digital space may result in net-negative impacts to SMEs. The cost of inaction may well turn out to be the deciding factor of whether SMEs can continue to compete and defend their respective markets. Nevertheless, regardless of impact, we believe that SMEs will have no choice but to jump on the digital bandwagon when the next tech revolution arrives; the question is whether SMEs will still be catching-up from the back of the pack or riding high at the head of the race.

Written by Eigis Consulting Group

Eigis Consulting Group is a business and management consulting firm headquartered in Kuala Lumpur, Malaysia. We specialize in strategy consulting, market/industry research and analysis, and project implementation services for businesses and organizations in the public and private sectors.

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