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The Internet is bursting with opinions on artificial intelligence (AI) and machine learning (ML). An intellectual war is raging amongst academicians, ML evangelists, data scientists, and big data gurus. At the same time, much of the business community is still working out exactly what these emerging technologies are and how to exploit them for competitive advantage.
What are AI and ML?
Simply put, artificial intelligence (AI) enables smart systems to act like humans – in other words, with intelligent behaviour. In contrast, machine learning (ML) is a subset of AI that enables machines to act without being explicitly programmed; ML is the underlying capability that supports AI-enabled smart machines.
We are all already interacting with – and benefitting from – AI and ML. Crowdsourced traffic app, Waze, Uber’s ride optimisation functionality, and the facial recognition and social networking functionality of Facebook has AI/ML at it's core. Amazon uses neural networks (AI) to generate product recommendations, and the core technology powering Alexa is AI.
Even the banking industry, progressively is using AI and ML to transform service experiences and deliver greater value to customers. HSBC using AI-enabled fraud detection has been a much talked about story among many others.
Innovators now understand that the time to learn about and contemplate AI and ML is over. The pressure is on to drive real business innovation using AI and ML. But this will require shifting from a “can’t do, won’t work for us” mentality to a “let’s try and make it happen” way of thinking. At the same time, business leaders need to take time to learn about these technologies before they sign off on AI and ML initiatives.
So how can we get started with ML?
To jump-start AI and ML initiatives for your organisation, answering the following three questions will help:
Question 1: How do I ensure strategic business relevance?
ML initiatives need to have strategic business relevance. Ask yourself if your ML initiative meets the following three criteria:
Question 2: How do I prepare for an ML project (inception to technology implementation)?
Next, ensure organisational readiness by looking at it from multiple perspectives:
Question 3: How do I structure a business case for ML?
To prioritise your ML initiative within your company’s change portfolio, ML teams need to articulate measurable benefits. Executives and board members want to know how ML will deliver new revenue, higher NPS scores, greater employee satisfaction, and more. If you can demonstrate an opportunity for clear savings (for example, by reducing regulatory fines or fraud costs), it will strengthen your argument.
As you structure your business case, consider the following components:
Together, these components will drive the ROI debate and project sign off.
Organisations exploring ML will no doubt want to embrace the new possibilities it affords. As you get started, focus on two priorities first: 1) identifying potential business problems where machine learning becomes a powerful enabler and 2) developing a proof of concept that delivers clear business outcomes.
AI and ML will change the way we live and go about our business. And the first ones to embrace and exploit its potential will be the winners. Clearly the ML winds are gaining momentum. How long can you afford to wait?
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To learn more, join us for our webinar with Finextra on accelerating machine learning scheduled for September 12 @3pm GMT. Register here
This content is provided by an external author without editing by Finextra. It expresses the views and opinions of the author.
Kyrylo Reitor Chief Marketing Officer at International Fintech Business
06 November
Konstantin Rabin Head of Marketing at Kontomatik
Erica Andersen Marketing at smartR AI
04 November
Prakash Bhudia HOD – Product & Growth at Deriv
01 November
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