In December I was fortunate enough to participate in The Global AI conference in Boston, which as with all AI lead events combined innovation, intelligence, and controversy. My key take home was the reinforcement of a problem I have experienced numerous times when directly engaging with, and building teams for my clients – what is your Artificial Intelligence goal?
Strategy, or lack of strategy, around the use of Artificial Intelligence is something which is frequently discussed at thought leadership events. Successful technology projects have defined goals and well thought out roadmaps (most of the time). However, due to the uncertainty of AI’s use, defined strategies don’t always appear to be in use. In fact, with the stakes being so high, normal roadblocks can quite quickly be magnified.
As a specialist Artificial Intelligence recruiter, I am lucky enough to partner with some of the most dynamic and smart AI innovators in the world, from both a client and candidate perspective. Nevertheless, immense human intelligence in the field doesn’t offer immunity from the competition, speed, and media attention driving the growth in Artificial Intelligence.
I have seen three notable routes when building an Artificial intelligence project/team in-house.
Larger corporations are renowned for hiring top academic talent within Artificial Intelligence; perhaps someone who has been responsible for groundbreaking developments in how we teach machines to think on their own. However, without mass data and real-time problems, the ability to scale some of these excellent theories can fall down. Senior AI heads are brought in from a bird’s eye view without a defined goal or problem to provide a solution for, which leads to many hours wasted on unattainable outcomes.
The Guinea pigs
Smaller businesses often want to hire one exceptional data scientist/machine learning engineer to test the waters. This often leads to the candidate in question looking for a needle in a haystack. There is a lack of support, encouragement, or understanding from the business to make an impact. Unfortunately, their stints within the business can see their work become experimental.
The third and most successful AI teams I’ve helped build have taken small business problems or a defined area and encompassed a structured roadmap in order to tick off small milestones, leading to a large effect on the greater good, with visible rewards and repeatable processes. Ultimately this leads to stakeholder and Board confidence.
Many organizations know Artificial Intelligence is a necessity in order to stay ahead of the competition in an eat or be eaten digital society. The cost of AI projects VS return on investment in the initial stages occasionally means that they never reach their true potential.
First, have a problem or area you wish to optimize. Secondly, look at how machine intelligence can solve this problem. Thirdly, work(ing) backward, identify an area of the business which needs innovation, and the use of machine intelligence to impact things for the better.
If you would like advice or assistance in building an Artificial Intelligence team, drop me an email to set up conversation firstname.lastname@example.org