According to a recent PwC study, the potential contribution of AI to the world’s economy could reach $15.7 trillion by 2030. At a global level, companies are overwhelmed with strategies on how to implement the best AI-based practices to get ahead of the competition. Increasingly more C-level executives from top enterprises seek to get ahead of the curve through continuous investment, talent acquisition, employee retention, and risk management.
As interest in AI among tech-savvy millennial increases, leaders fear that if they don’t adapt their current strategies, they’ll get left behind. Although competition abounds, the path to attaining AI excellence is not the same for all companies as some have different priorities than others. Why do companies pursue an AI advantage? The following reasons might apply
To fuel innovation with already available data
Companies worldwide have declared their ambition to implement AI-based technologies into their business models to fuel innovation. In 2018, there were over 1,300 declared AI startups in the US, followed by China and Israel with 350+. One of the main reasons AI is winning ground is because enterprises already have access to data that feeds information and fuels innovation. Amazon is an excellent example of a company with a massive database on user purchases and behaviors. Coupled with machine learning algorithms, Amazon never stops learning and that makes its predictions closer and closer to meeting customer demand with every year that goes by.
To empower employees and increase engagement
Building a strong, passionate workforce cannot be done by robots alone. Automating tasks does contribute to a better, more streamlined way of work. However, enterprises must acknowledge that human interaction is equally important. Companies with engaged employees have the ability to trump the competition by 147% and according to McKinsey Global Institute, organizations with connected employees can improve productivity by 20%.
For years, the United States has been a leader in public and private AI research. In 2012, venture capitalists funded $282 million in AI initiatives, and that number skyrocketed to US$5 billion by 2017. In 2018, AI investments by VCs topped US$8 billion. Fashion retailer H&M implemented Convo’s Retail Social Collaboration Platform to enables workers in the US to communicate more efficiently and receive updates in real-time on in-store issues via a news feed interface. With Convo, H&M will continue its mission to maintain a strong brand affinity among employees and also boost customer experience.
To reach AI maturity and get ahead of the competition
Both experience and enthusiasm vary among companies flirting with AI. In many cases, the objective is to improve processes whereas a long-term goal would be to transform the organization inside out. However, bridging the skill gap challenge in AI comes in the way of adoption. Companies should focus on building an agile-oriented strategy before deploying any AI-based technology. Insights from Deloitte’s most recent survey on the state of AI reveals that 68% of today’s companies are in dire need of data scientists, researchers, and software developers.
The key to getting ahead of the competition, in this case, might be to hire external talent. However, Microsoft thinks differently. Recently, the company launched a program focused on training 15,000 workers to close the AI skill gap rather than hire new people. President of Global Sales, Marketing and Operations at Microsoft, Jean-Philippe Courtois, mentioned: “As a technology company committed to driving innovation, we have a responsibility to help workers access the AI training they need to ensure they thrive in the workplace of today and tomorrow.”
Whether they’re early adopters or experienced giants, companies across all industries pursue an AI advantage to get ahead of the competition. In one way or another – whether it’s training employees, hiring new talent or partnering with the next, most hyped AI startup out there – it is a never-ending journey paved with risks and challenging decisions on whether to automate or adjust. With adjacent technologies like cloud computing and data analytics, it has never been easier to tap into the potential of AI and use it to explore, learn, and fail just to do it all over again and, with a bit of luck, succeed.