A recent McKinsey report says that AI is today packed into everything we see around us; be it the products we buy or services we use, or many of the customer problems being solved today by companies. The report says that it is the transformational technology of our digital age, and its practical application throughout the economy is growing apace.
Management consulting firm BCG says that rapid advances in machine vision and language processing are becoming organisational cornerstones. Companies are striving to strike a balance between people skill-sets and machines, thereby radically enhancing competitive advantage and resulting in a better understanding of customer pain points, that can then be addressed with more efficient processes.
Bain and company says the next phase of automation, based on machine learning, artificial intelligence and advances in robotics, will affect more than 50 per cent of today’s workforce. They further caution that the pace of such technological adoption will most likely become more rapid in coming years.
Here to stay?
So, are these mere buzzwords or management jargon which help consultants make tons of money? Is AIMLA (artificial intelligence, machine learning, analytics) here to stay, and will it become inherently ingrained in the way companies are run, or is it a passing phase, like most other jargon? As aspiring managers, do you really need to bother what AIMLA is and is this the right time for you to acquire skill-sets in these areas, in addition to your own domain expertise?
The mega-trend that is clearly visible today strongly shows that AIMLA has crossed the ‘inflexion point’; you don’t need to have an ‘intricate mind’ to figure out that AIMLA is the future; it’s already here! Of course, there are leaders and laggards in AI and ML implementation; some companies, and industries, are far ahead of others in using AI and ML to gain competitive advantage.
End of the day, AI and ML are about accuracy in prediction and many companies are fairly advanced using large-scale neural networks and algorithms that are learnt through large volumes of training data. To survive, leave along having competitive advantage, the laggards will, and definitely need to, catch up in the AI game in the coming days.
Early adopters
Why are some companies far ahead of others in AIMLA adoption? Management foresight, access to skilled workforce and ability to allocate funds to AIMLA projects are some of the reasons why some companies are ahead of others in AIMLA implementation. Many companies are fighting for their survival in competitive markets today and are unable to convince shareholders to invest for the medium to long term.
To a certain extent there are risks today in AIMLA investments; standardisation has not yet evolved, there are still questions as to whether AIMLA projects should be done internally within the company or should we outsource to the ‘so-called’ consultants.
Successful AIMLA implementation requires an intricate blend of data science knowledge, skill-sets and programming tools with business and domain expertise. Companies need to pay well to either attract such unique skill-sets within the company or outsource the same to worthy third-party consultants.
Such initiatives come with substantial costs and drain management time, away from their ‘day to day’ initiatives and, hence, they often take a back seat. Given such a scenario, there is a natural tendency on the part of senior management for procrastination and delaying such investments and resource allocations to such perceived ‘futuristic’ requirements. Such companies will soon realise that they are ‘left out’ ‘behind the curve’ and will be soon be forced to fall in line. So, as aspiring managers, you have a huge opportunity if you are armed with such skill-sets!
Make yourself future-ready
Managers at all levels take decisions. These decisions are required to solve today’s complex problems, where environments are uncertain and the business landscape is rapidly changing. Managerial decisions are made within such dynamic environments and no manager makes any decision with 100 per cent information. All of this means that the only confidence with which managers make decisions today in the midst of such uncertainties is based on the ‘comfort factor’ that machine learning and predictive analytics provide. ‘Data insight’ backed decisions will become, or are becoming, the new normal.
If one looks at this ‘mega-trend’ from an aspiring manager’s standpoint, taking a medium- to longer-term view of their careers and the type of skill-sets employers look for in them; the picture will start falling into place. Data science knowledge and skill-sets, programming tools such as Python and natural language processing clubbed with a strategic thinking mindset, with a clear understanding of business and domain specifics, are something students need to steadily acquire and equip themselves with in order be marketable!
Gaining a strong grounding in statistical techniques will help them hone their business, functional and strategic thinking skill-sets; as leadership at all levels require some amount of ‘hands-on’ computing capability. Basic techniques such as descriptive statistics and inference, moving to regression analysis and then further to such advanced techniques as clustering, dimensionality reduction and ensemble learning, slowly but steadily, builds a solid base for aspiring managers.
Hardware catches up
The whole concept of AI is not a new tsunami wave. It has been in practice since the mid-1950 and saw a revival in the mid-1980s and 1990s, when many techniques, especially in data mining and medical diagnostics, were commercialised. But the lack of hardware to process large data-sets stood as a major limitation.
The 2000s saw the emergence of cheaper hardware that can harness huge data-sets. Especially with sensors become extremely affordable in the last few years, ‘cyber-mechanical’ integration in the form of internet of things (IoT) reaching new levels has resulted in an explosion of data capture and wider availability. All of these, clubbed with ‘data science’ skill-sets capable of writing algorithms and the managerial insights to solve business problems has pushed deep learning and predictive analytics to the forefront in the last few years.
Some business schools are thinking ahead of the curve and have realised that creating the appropriate skill-sets in AIMLA technology has the potential to transform the country’s managerial landscape. Great Lakes Institute of Management, for example, has pioneered the teaching of business analytics over the last five years. Now, with a specialised major in the area of AIMLA, Great Lakes is ensuring that potential managers are equipped with the requisite skill-sets to power the AIMLA movement and advance it globally.