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How to become a top 250 tech leader

With growing hype around Artificial Intelligence and the rising demands from businesses and consumers, there is no surprise that more people are turning to job roles with AI at the heart of what they do or help to deliver.

The right steps can take you to the top of a company’s IT and innovation department. But how do you get there? Simply put, there is no right way. Unlike the old-fashioned approach of forging the ‘right’ career path, modern day career progression can come in all shapes and forms, and is more than often never straightforward.

As many aspire to climb the ladder quickly, we spoke to industry expert James Bell, Head of AI and Machine Learning for Dow Jones, to hear about his career journey within the AI space.  

Forging your career path 

My career path has been somewhat unusual. I started in IT in the late 80s when working my summer and Easter holidays with my father- who was the Head of IT for a Bristol-based multinational.

I went to university to study Philosophy in the mid-90s after falling in love with the subject during my A-Levels. Frankly, that turned out to be an excellent decision as Philosophy teaches a lot of very relevant skills for the modern working environment and leadership roles. For example, it teaches you high levels of communication skills in writing and public speaking, which I take advantage of regularly; but, it also trains the ability to analyse a problem intensely, to think logically and rationally and to learn at a very high rate – all keys traits of a good leader.

 If you can pick up new ideas quickly and learn them in-depth, then you will always be valuable.

A few years later, I started in banking IT and rose to my first leadership position as the IT Manager of a subsidiary of an Israeli bank. I saw them through all sorts of change and challenges, including SOX 404 audit and the 7/7 terrorist attacks, which happened nearby and caused a total building evacuation and shutdown.

After leaving the bank, and returning from a year travelling, I went to work in solution design in SWIFT networks and there I volunteered to be trained in anti-money laundering. That turned out to be a smart decision as my next move was to become a full AML consultant and then to rise to Senior Manager of Professional Services fairly quickly.

Over the next five years, I led a team delivering data-centric projects all over the world. This involved my learning all about data quality and, of course, Artificial Intelligence and its early precursors. Multiple successful projects for large clients caught the eye of KPMG who offered me a Senior Manager role productising a machine learning technology into a product offering. From there, I was headhunted to Dow Jones as Head of AI and Machine Learning. It was a very natural fit as I had been working with Dow Jones' products and services for the last 20 years and had both a customer's and partner's POV of their business. Marrying that with the insider POV has proved very beneficial.

Key learnings and challenges

The most significant learning has been the discovery that one’s limits are simply the same as one’s abilities to learn new domains. My family motto is, “What one man can do, another can do”. If you can pick up new ideas quickly and learn them in-depth, then you will always be valuable.

The biggest obstacle is siloed thinking; that needs to circle the wagons and be overly protective. AI is a very disruptive technology, and it is vital to reassure stakeholders that AI is not coming for their jobs.

One method I used to do this is by placing a strong emphasis on AI Ethics. Robot proof your job by broadening your skill sets without becoming too specialist and adapting to the proliferation of category disruptions – don’t get left behind.

The biggest changes in AI over the last 5 years

The biggest change in AI in the last five years is two-fold; firstly, Deep Learning has revolutionised the AI domain. I often talk about how quickly AI develops through generations, and that mastery of an older technology can become increasingly obsolete. What a lot of people don't realise is that this applies to AI-technology just as aggressively. Look at Natural Language Processing: the impact of deep learning followed by the development of enormous transformers means the gap between generations is getting small enough to squash those dedicated to an older approach. 

Secondly, various factors, one being the increasing technical prowess of the cloud providers, means that AI-technology is now at the point of having a societal impact. Using NLP as our example again, the latest advances run the risk of enabling bad actors to auto-write false news, commit fraud with ease and be dangerous. So, finally, people are starting to take AI Ethics seriously. No longer can it be called "navel-gazing" as the potential impact is on masses of lives.

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