Having done my Machine Learning certification in August 2019, I was fortunate to get an opportunity soon after to build and lead technology team that worked on AI / ML problems across the enterprise.
During the team build-out phase, I realized that many software engineers have completed a formal certification on machine learning to qualify themselves for a role in this emerging technology area where demand is expected to increase. There is also an unfounded assumption that all software engineers will work only on ML algorithms in future and demand for other skills will plummet. The reality is that not all software applications will be suitable machine learning candidates. Moreover, developing machine learning algorithms is only part of AI / ML technology lifecycle. There be massive software engineering needs outside of machine learning, particularly around data and SDLC automation to enable AI / ML technology. Having said that, familiarity of machine learning concepts will increase effectiveness of software engineers as all applications in near-future will interface with ML modules for certain functions.
Now, let’s address another question – is AI / ML just hype? To understand this, lets look at it through the lens of Gartner Hype Cycle. Since mid 1990s, a number of technologies fell by the wayside after inflated expectations in the beginning. However, a few like cloud computing, APIs / web services and social software went through the hype cycle but the reality after mainstream adoption was quite close to initial expectations. Looking at hypes since 2013, several technologies related to AI / ML have been at the top every year. Starting with big data and content analytics, we have seen natural language processing, autonomous vehicles, virtual assistants, deep learning and deep neural networks emerge at the top during the last seven years. And results from machine learning algorithms have already become part of our day to day life – like recommendations made by Amazon, You Tube or Netflix and chatbots available through a number of channels.
So, I believe AI / ML is real and will continue to disrupt mainstream industries. However, it will be different from other familiar technology disruptions in many ways:
- AI / ML technology will continue to evolve rapidly, driven by silicon valley innovation.
- New specialized areas of expertise will emerge every year that will require deep math understanding.
- Technology workforce will be under pressure as past work experience will be of limited value due to this fast evolution.
- Traditional enterprises will struggle to keep pace.
- Possibility of learning through data will undermine established business theories.
Finally, the overwhelmingly open source nature of this domain will lower entry barrier and promote start-ups to challenge established players. It will also give an opportunity for established organizations to adopt and manage this disruption. The choices made will determine whether an organization disappears like Blackberry, comes back with a bang like Microsoft or continue to hang-on like IBM. While this is primarily about embracing a relatively new technology domain, appropriate strategy around people and process will also be required to succeed. To summarize, organizations will have to create the right ecosystem and provide clarity on approach that encourages people to innovate.
In this blog series, I will articulate my thoughts around people, process and technology considerations while adopting AI / ML in a large enterprise:
- Technology functions that will require machine learning expertise.
- Business domains that will benefit from AI / ML.
- Challenges that enterprises should be prepared to encounter.
- Structure and governance to scale up adoption.
- ML technology platform.