The ripple accelerated as Microsoft, the parent tech giant behind Edge invested a whopping 10 billion US dollars for integrating ChatGPT to their browser. And suddenly news started flooding in that Edge is providing better search results than Google. The hype was so severe that Google promptly launched its “unpolished” BARD AI to win back the market and thus commenced the AI War of the 21st Century.
Now let us think for a second, why on earth a visionary tech giant like Microsoft would invest a fortune behind an AI Chatbot and why would another leading giant like Google would follow its footsteps desperately ? And we are not only talking about chatbots – from the AI-powered delivery robots by Amazon to the self-driving cars by Elon Musk’s Tesla, every major or at least active, tech firm is pushing towards AI-enabled innovations.
According to a recent McKinsey Global AI Survey1, 47% of respondents claim their organizations have at least one AI capability incorporated in their business processes, while another 30% indicate they are piloting AI. This represents a huge increase from 2017, when just 20% of respondents stated their organizations used AI in a fundamental aspect of their company or on a large scale. According to the report, AI use differs by sector and function. High-tech and telecommunications firms, for example, report the highest rates of AI use (68%), followed by automotive and assembly (61%) and financial services (53%).
The answer to these questions can be given in three C’s-
In this blog we will walk you through the three C’s of why and how tech firms are shaping the future by investing in AI and machine learning.
Let us assume that you are a finance executive of a reputed tech firm and you have been tasked with collecting the annual finance report of every tech company of the country in the last decade, assess what worked for them and what did not, sort out the best ones and make a report that’s visually appealing to your not so math-understanding colleagues.
You start doing it but soon realize it is not a one-man job. You reach out to the authority and they are kind enough to hire some 10 more people and now you have a team and the task is 10 fold less for each but up to 10 fold more expensive for the firm. Your team worked hard and you guys did it in a month. But then the firm buys or develops software that does it all within minutes with Machine learning and now you exclaim,” What a capable system!“
We humans might be the most intelligent and sophisticated beings on the planet but we are susceptible to physical and mental fatigue. AI and machine learning on the other hand, are not. It is inhumane to expect one tech employee to work 24-hour shifts and complete a complex task within a month, but it is very convenient for a machine learning algorithm that can do it in seconds or minutes. This is the mighty capability that tech companies are utilizing to:
- Handle big data: In the modern world, company data, especially in the tech industry, is too immense and dynamic for manual processing. However, for AI and Machine learning models that are tailored to learn these kinds of data, it is not only fast but more efficient.
- Improve decision making: As AI and ML models can be easily exposed to an inhumanely large amount of diverse data, they can help make better decisions based on mathematical reasoning, allowing the tech firms to plan ahead, make early assumptions, hire the right employees and all that impressive stuff.
- Automate tasks: AI has revolutionized task automation by automating almost every kind of task a tech company may wish for, ranging from cleaning office floors with AI-powered robots to automating tech manufacturing as seen in Tesla’s 5.4 million square feet Gigafactory in Nevada.
- Improve security: With the help of AI and ML, tech firms are strengthening their security with superior biometric verification, visual and motion-based verification, and software security mechanisms that can learn from the systems and upgrade themselves, adapting to the malware and other vulnerabilities. For example, the tech firm Indatalabs utilized an advanced AI-based face recognition system that enhanced security by 89% by reducing spoofing2.
- Increase revenue: AI and ML-powered applications are helping tech firms generate new income streams by developing new products and services, improving customer experience, customizing offers, expanding into new markets and segments, and so on. Hidden Brains, a software development company that provides AI solutions to over 30 countries worldwide, has helped its clients increase their revenue by up to 40%.
- Reduce cost: AI and ML-driven methods are greatly reducing company costs by providing the optimal course of action, predicting outcomes for each action, detecting and predicting anomalies from company data and even reducing the need for paid manpower as AI-powered automation is proving to be more efficient.
Undeniably, the tech industry is fiercely competitive and the margin for error is very low. Amongst the money, the promises and all other reasons to adopt AI, competition is the simulation that forces tech firms to always stay updated with the latest technology not only to have an edge against their competitors but mostly, to survive.
Like any consumer-based industry, the tech firms are always in a race to gather more audience and in terms gather long-term customers. And for that the most effective strategy is providing tailored services that suit every consumer group. But with varying people with more varying traits, it is not humanly possible to learn every person type – to auto complete every query instantaneously with 100 relevant suggestions. And that’s where AI and ML are dominating as they allow for-
- Assessment of market position: With the data processing potential of AI and ML models tech firms can verify their position in the market, acceptance among target consumers against their competitors allowing them to set adequate courses of action.
- Investigate consumer trends: With adequate data, tech firms can easily detect the current market trends among the consumers and modify their product line-up accordingly. It not only helps them to learn which of their products or services are working well against which competitors but also what are the chances of new products and services working well in their target market greatly reducing risks.
- Create accurate consumer profiles: As questionable as the act of collecting user data might be, it allows tech firms to develop accurate consumer profiles for each type of customer groups allowing them to make better decisions which are also well-accepted by the majority of their target consumers.
But what are the flip sides?
Despite the unprecedented potential of AI and ML that is revolutionizing the tech industry there are some challenges that must be addressed. Among all the blessings that humans get to explore as hard work is eased by AI-powered robots and software tools, some burning questions are on a steep rise.
- Lack of skill: As tech firms focus more and more on AI, the skills required are polarized towards developing AI and ML-driven services but while the demand for such services grow exponentially, the skill set has a steep learning curve leaving a noticeable gap for workforce with expertise in the field.
- AI and ML are replacing Jobs: Between all the Terminator movies and the wide-spread notion of “AI taking over the world” we grew up with now there are noticeable similarities to science fiction. As AI-based hyper automation cuts cost and time consumptions by 80-100%, tech firms are replacing manpower with AI. An estimate by BBC3 claims that 300 million jobs could be replaced by AI in the coming years, which is not a pleasant sight as lifestyle is getting more and more expensive where the job pool is shrinking.
- Lack of data: Data is the eyes and ears of any AI or ML model. It is how they learn. Although data, in the present world, is vast and ever varying, access to data, on the other hand, is often not easy and may cost companies a fortune to get hands on.
- Legal issues: With generative AI on the loose, there have been new debates regarding personal data and intellectual property that AI and ML models train on. Often data is collected without the consent of the user. AI creating crafts and arts are also threatening intellectual property. The academic domain has also been distorted with the new rise of AI-powered plagiarism. Such idiosyncrasies are causing unprecedented legal issues for tech firms using these technologies and the judicial system in general.