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There is no doubt that artificial intelligence (AI) and machine learning (ML) is becoming a hot topic within the fintech industry. At almost every seminar and conference, we are hearing about the rise of these emerging technologies and the potential they have to disrupt businesses.
It’s clear that AI and ML is a blueprint within which the fintech industry is operating. However, what is apparent is that no matter how much fintechs bang the drum of the impact of AI on enterprises, it still remains underutilized by many companies due to their inability to visualize, integrate and adopt these new technologies.
Recently, there has been a great deal of conversation across multiple industries around the potential of these technologies, but according to research by Accenture, 87 percent of business leaders in the UK are struggling with how best to adopt it.
That’s not to say that there isn’t an understanding of its importance for enabling strategic priorities. Indeed, three out of four C-Suite executives believe that if they don’t scale AI in the next five years, they risk going out of business completely.
Nonetheless, there remains a gap between ‘hype’ and ‘practical implementation.’ Less than 5 percent of companies have successfully
industrialized AI, while 80 percent to 85 percent are pursuing discrete proof of concept products – where the power of AI and machine learning is disconnected from business outcomes or strategic imperatives. Many companies do not sufficiently tap into the full potential of emerging technologies, consequently limiting their business impact.
With its expansive historical and structured data, fintech is a fertile ground for artificial intelligence and machine learning technologies to generate bespoke products and solutions, to help businesses increase profitability and save costs. So, why are companies generally slow to adopt, implement, and scale emerging technologies in their short, mid-term and long-term strategies?
Many companies are slow to adopt AI and machine learning due to a lack of technical know how from both an integration point of view, and a limited understanding of its value to their business.
It is essential that companies work with the right people to commission AI and ML products and solutions that have tangible business benefits and impact at the customer level.
As a former Silicon Valley technologist and research engineer for a major technology company, I have found that these technologies can play a vital role in operations across a business. Companies can identify opportunities where cost savings can be made, while simultaneously increasing efficiencies, making it easier for the CFO to embrace their role as a key contributor to the growth of the company.
By using a combination of AI and machine learning technologies, businesses can identify opportunities that companies are missing to accelerate their day-to-day activities and processes. These technologies enable customers to make smarter decisions and operate more effectively. Meanwhile, emerging technologies will increase growth opportunities to aid business development across the globe, helping companies to thrive in an international environment.
According to recent research, executives weren’t struggling to scale AI because of budgetary constraints, but rather the operational challenges of integrating these technologies into their current business processes. The inability to set up a supportive organizational structure, the absence of foundational data capabilities, and the lack of employee adoption are barriers to harnessing AI and machine learning within an organization.
It is precisely these aspects that differentiate companies that have successfully scaled AI and ML, versus their counterparts pursuing siloed proof of concepts. Not only should company bosses move towards adopting AI and ML as part of their go-to-market business strategies, but they should also actively work towards integrating and encouraging the adoption of these technologies into their day-to-day operations.
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