Once upon a time, the world was divided into two calendars- BC and AD. But in the last decade itself, that calendar has changed again. It is called after-digitalization or the new AD. Now, we live in an era dotted with digital powers and of all stripes possible.
Our banks run on algorithms, our factories are controlled via IoT, bots handle our customer grievances, virtual assistants answer our queries, autonomous cars leverage deep learning, our museums use AR, drones deliver our books, our tools are made through 3D. Computers man even our electricity grids. This shows disruption is all around us impacting businesses and lives, to the deepest level possible. The question is – are we ready to survive it? and make the most of these emerging technologies?
Artificial intelligence (AI) – one of the top emerging technologies
Recall the last time you watched a movie, booked a cab, or ordered a burger.
- Was it not quick yet personal?
- Was it not less of a transaction and more of an experience?
But how do they make you feel so special? and how do they serve you so fast and precisely the way you want?
Well, the genie that makes it all happen is packed in a bottle called AI (artificial intelligence). AI is a salient emerging technology that is redefining a lot of fields. It is injecting intelligence, precision, and proactive action in a lot of business models. With AI, businesses and customers alike have hopped on to a new paradigm. It’s the landscape of intelligent machines, helping humans get better, faster, and smarter – in every way.
This growth and depth are being reflected in impressive numbers – on all kinds of calculators. Whether it is IDC that reckons worldwide revenues for the AI market to grow to 2021 to $327.5 billion and touch $554.3 billion by 2024 or it is Gartner that tells from a 2020 survey that 24 percent organizations have upped their AI investments since COVID-19 – AI is getting deep into the skin of almost every breed of business. As the post-pandemic reset takes a solid shape, 75 percent of organizations will continue or start new AI initiatives as they move into the Renew phase. Of course, Cloud and IoT are also gaining a lot of traction in enterprise usage. Still, AI is at the forefront of technologies that businesses use for automation, augmentation, and customer-centricity.
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- AI to stay high
- AI global forecast
- AI impact on jobs
- 5 roles in organizations that need AI expertise
- AI is the essential expertise for a tech professionals
- Building in-depth understanding and new capabilities in AI
AI is hot and has been nascent for a long time – that’s a beautiful contradiction that it lives. AI technology has a lot of promise for many areas – like transportation, education, health, manufacturing, retail, arts, medicine, etc. Leaders realize both the advantages and dangers of using AI as a weapon in the new landscape.
Do you know that in 2021, AI augmentation would have created $2.9 trillion of business value and 6.2 billion hours of worker productivity globally? If we look at a McKinsey Global Survey on AI, we will prove how organizations are using AI to generate value. And this value is being manifested as revenues. People from various industries attributed 20 percent or more of their organizations’ earnings before interest and taxes (EBIT) to AI. No wonder these companies plan to invest even more in AI in response to the COVID-19 pandemic. The crisis has pumped up the acceleration lever of all things digital. As a result, AI is steaming with possibility. And that’s why it should be handled smartly and carefully.
There are endless areas where AI can be put to profitable use. From demand forecasting in retail to real-time behavioral and intelligence in customer service to many services powered by hyper-personalization and customer experience improvement, so many use scenarios where AI alone can deliver results. AI has gained this muscle because of the convergence of a lot of forces. From increased processing power that helps handle complex tasks to progress in cloud computing and the outsourcing of data storage – which allows it to chew data at speed – AI has leveraged many tools and advancements very well. And it has transformed them into plug-and-play applications.
As we get closer and closer to a sophisticated understanding of how the human brain works, we enhance our ability to embed brain-like elements into computers. That’s why we are getting so much better at applications around voice and pattern recognition, natural language learning, and machine learning. Within the next three to five years, an exponential increase in the number of commercial AI-based applications is expected. A Deloitte study titled ‘Cognitive technologies: The real opportunities for business’ outlines that AI applications fall into three broad categories: Product, Process, and Insight Applications. Yes, we can embed AI in a product or service to provide end-customer benefits. Or we could inject AI into an organization’s workflow to either automate processes or start augmenting worker effectiveness. Then we can harness advanced analytical capabilities such as machine learning to uncover insights. These can be applied for operational and strategic decisions across an organization. That’s what makes AI so amenable for vehicle automation, marketing personalization, and robotics for a wide range of applications.
By now, you must have realized that AI is not a smooth tap on the shoulder. It is seismic. It disrupts the industry it touches. In McKinsey’s latest survey findings, we can see that a minority of companies recognize many of the risks of AI use, and fewer are working to reduce the risks. Cybersecurity and risks related to AI explainability are still on top of many lists. AI high performers, however, remain more likely than others to recognize and mitigate most risks. Those at high performers are 2.6 times more likely than others to say their organizations are managing equity and fairness risks such as unwanted bias in AI-driven decisions.
The task is challenging, but it has to be done. We have to balance the positive side of AI’s disruption with the flip side. We have to leverage AI’s gains, improvise and manage internal processes efficiently, and make sure that we minimize the risks associated with it. People and organizations who rise to this disruption will gain. Those failing to cope with these areas will find AI overwhelming and disturbing, reminds McKinsey.
AI has a lot of advantages when it is used for its positive potential. But when used the wrong way, it can also bring in many risks, data issues, privacy concerns, bias, discrimination, inaccuracy, and misapplication of information. Unfortunately, the world has seen many such fiascos already. Whether AI ends up as turbulence or a much-needed disruption depends on the people, skills, and approaches used to apply it.
While some industries are retrenching—such as energy, oil, gas, and mining—a few markets, like healthcare, are speeding up AI adoption- according to Omdia’s latest diagnosis. As we speak, convolutional neural networks and other deep learning technologies are presently being used primarily for image, voice, and unstructured text processing. However, they are slated to evolve to be applied in a wide variety of applications. They are turning into a standard approach for processing some incredibly large, and complex data streams. As to the Covid-effect, despite the economic challenges that pandemic-mitigation measures have caused for many companies, the ones who are witnessing the most value from AI are doubling down on the technology. The respondents at high performers said their organizations had increased investment in AI in each primary business function in response to the pandemic. It was noticed that automotive and assembly and in healthcare services and pharmaceuticals and medical products are the most likely to say their companies have increased investment.
The future is still being written. As AI moves into the cerebral workspace, it will augment and empower many tasks by humans so far. The pandemic has stressed its role. With digital transformation turning into a mainstream priority for many enterprises, AI will stay to play its vital role here.
AI has only been growing to new heights since it hit the business application space. AI market is pegged to rise by $76.44 billion, progressing at a CAGR of 21 percent between 2021 and 2025, as per Technavio. In addition, Gartner, Inc. has predicted that by 2025, 70 percent of organizations will shift their focus from big to small and wide data, providing more context for AI and making it less data-hungry.
As per Omdia, the global market for AI software will grow to $98.8 billion by 2025. However, what is also happening is that disruptions such as the COVID-19 pandemic are pushing historical data that reflects past conditions into obsolescence. This is breaking many production AI and machine learning models.
The global AI market size has been slated to touch $169,411.8 million in 2025, as per Allied Market Research. AI, and has been slotted as one of the fastest-growing technologies in recent years. What makes it even more powerful is that AI is positioned at the core of the next-gen software technologies. AI would be the primary architect for a lot of emerging technologies and tools ahead. A business that has not invested in AI will fall short on both AI value and many upcoming and competitive technologies.
So what happens when AI makes the jobs of humans faster, easier, and more intelligent? Will it hurt their jobs too?
- Data from the World Economic Forum (WEF) has hinted that automation will displace 75 million jobs but generate 133 million new ones worldwide by 2022.
- Gartner echoes that AI-related job creation will reach two million net-new jobs in 2025.
- And according to the McKinsey Global Institute, with sufficient economic growth, innovation, and investment, there can be enough new job creation to offset the impact of automation.
In some advanced economies, additional investments would be required to reduce the risk of job shortages. A recent Forrester report, ‘Future of Work,’ showed that automation is not a singular trend. We cannot say clearly how future scenarios are influenced. There will be many trends like the gig economy, the destruction of industry boundaries, and the increasing desire for privacy and transparency. Automation can open the doors to new, previously unthinkable business opportunities. Forrester recommends that as much as companies must become learning institutions, so must employees become learners. It is vital now that people learn core skills, adapt to new working models, and understand what it means to be ready for the future. This is about maximizing their Robotics Quotient. Forrester predicted job losses of 29 percent by 2030 with 13 percent job creation to compensate.
As per ‘The Future of Jobs Report 2020’ by World Economic Forum, ‘We find ourselves at a defining moment: the decisions and choices we make today will determine the course of entire generations’ lives and livelihoods. We have the tools at our disposal. The bounty of technological innovation which defines our current era can be leveraged to unleash human potential. We have the means to reskill and upskill individuals in unprecedented numbers, to deploy precision safety nets that protect displaced workers from destitution, and to create bespoke maps which orient displaced workers towards the jobs of tomorrow where they will thrive.’
It has been estimated that around 40 percent of workers will require reskilling for six months or fewer, and 94 percent of business leaders expect employees to pick up new skills on the job. Almost 84 percent of employers will digitalize working processes rapidly. Employers expect that by 2025, increasingly redundant roles will decline from 15.4 percent of the workforce to nine percent while emerging professions will grow from 7.8 percent to 13.5 percent. So by 2025, 85 million jobs may be displaced by a shift in the division of labor between humans and machines, but 97 million new roles may emerge. These would be aligned to the new division of labor between humans, machines, and algorithms.
The WEF report underlines that apart from the current disruption from the pandemic-induced lockdowns and economic contraction, technological adoption by companies will transform tasks, jobs, and skills by 2025. Just notice that 43 percent of businesses indicate they will reduce their workforce because of technology integration, 41 percent plan to expand their use of contractors for task-specialized work, and 34 percent plan to expand their workforce because of technology integration. By 2025, the time spent on current tasks at work by humans and machines will be equal, as the WEF report said. So think of the top skills and skill groups that employers see as rising in prominence in the lead-up to 2025 – these include critical thinking and analysis, problem-solving, and skills in self-management such as active learning, resilience, stress tolerance, and flexibility.
AI will not make humans less relevant. It will make them more relevant because someone has to use all the time and data that AI is churning out. Someone has to make sure that ethical issues and data integrity imperatives are not thrown out of the window while wielding the power of AI. So should we not start getting ready for these new roles and skills?
AI has percolated into almost every function, requiring respective professionals to prepare themselves adequately and adapt to a radical change of work functions. The roles spawned by AI go beyond tech experts. Professionals across different layers of an organization, irrespective of their job roles, need to know AI and how they can integrate it within their domain. That means almost everyone – 1. CXOs, 2. Project Managers, 3. Product Leaders, 4. Research Analysts and 5. Developers- everyone will have to adapt and get reborn. Yes, unless AI is understood well by a business leader, unless the data aspect is handled well by analysts, unless project managers create the right models and applications – the entire exercise for AI can go pointless. AI’s role and relevance, hence, straddles across many functions and roles in an enterprise.
As the world adapts and rises to this AI era, it is time for tech professionals to enrich their knowledge, skills, and the core mindset of AI. We need expertise across many frontiers, like:
- AI Researchers — who can invent new AI algorithms and systems.
- Software Developers — who can architect and code AI systems.
- Data Scientists — who can analyze and extract meaningful insights from data.
- Project Managers — who can execute AI projects according to the plan.
AI expertise is becoming the clincher for many tech professionals as they aspire to reach smart technology. Everything else can be automated or converted into an algorithm. But the human-in-the-loop aspect of AI and the Black Box conundrum help make a professional eye even more vital for the proper usage of AI. From data integrity, quality, modeling approach, application-angle, and governance, AI’s expertise has to cover a wide range of areas where only a human brain would stay dominant.
It all begins with understanding the 5Ws of building AI expertise. Before you get into the course or skill training, find out where, what, why, when and who – everything about AI’s imperatives. Then, educate yourself well. With multiple online and offline options available, it is now easy for you to build a promising career in AI. The trick is to stay ahead of the game. You need to upgrade capabilities to remain competitive in the industry constantly. This can be done by exploring platforms that not only bring expertise and tools to the table; but are also well-tuned into the realities and challenges of AI. The training should make you well-equipped to enable others in the proper application of AI. It should make you agile and open enough to a vast range of possibilities. It should also make you aware and capable of the risks, constraints, and dead-ends of AI in the business world. Finally, the expertise you gain should have a long shelf life too. Do not focus on learning a short-term skill, but invest your time towards a long-sight approach and capability for AI.
The best way to do so is to find a learning avenue where deep academic collaborations form the foundation of the course. This is important because AI is unlike a programming language or a gaming tool. Much knowledge is still lurking in deep research projects and documents that academic teams are privy to. A lot of exploration, experimentation, and research in the domain of AI is still happening in universities and academic labs. Also, these technologies, by their innate nature, are radical. They need a fresh approach to learning. AI is also wrestling with a lot of ethical, pragmatic, and governance issues. These are some prominent reasons you should choose programs from top academic institutions.
Start early. Start right. And fix your date with the AD calendar soon.