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Artificial Intelligence (AI) is radically transforming businesses across the world. It is adding real value across the world of business and beyond. Executives at companies are looking towards AI to create new business value at a scale never seen before.
On one hand, leading AI companies are increasingly investing more in AI and are marching ahead of the competition. On the other hand, many companies are still contemplating their future course of action due to the paradigm shift.
Companies need to rethink their business models and ponder over how the dawn of AI will transform their business and implement strategies to deal with the fundamental shift that is being brought about by AI. It is imperative that companies immediately start looking at AI adoption and implementing new strategies or they risk becoming irrelevant.
Most companies are looking at AI for automation and increased productivity but many of the leading AI companies are able to generate value from AI through increasing revenues, by prioritising revenues and customers over costs and efficiency.
The need is to focus more on external improvements rather than internal ones because an opportunity lost in the external market far outweighs the losses that one may incur by not focusing on the internal opportunities, for improvement through AI.
Companies can now build advantage through innovative products that offer sharper value propositions like never before and have much more efficient processes. Some of the leading AI companies are following short but quicker innovation cycles, leading to stronger AI activity. Also, these companies pilot early and experiment with AI.
Additionally, such companies are increasingly using cross-functional teams to improve AI innovation. We know that “data is the new oil”.
Most companies will need to adopt an AI-first strategy in the near future. Additionally, companies need to have an operating AI strategy, today. Executives need to understand how to drive AI transformation within the company.
Additionally, a myriad of new designations is becoming available as a result of the changes that AI is bringing about. Companies first have the challenge of identifying these new designations that become necessary and then finding the right people for these new roles.
AI will affect every function within a company. Hence, companies need to re-skill employees and enable employees to understand what AI really is and the unique business opportunities it makes possible, and the threats that it poses at the same time.
The AI field is still evolving and will lead to a need for constant re-skilling, because of the changing human-machine collaboration. Therefore, the culture of the company must promote a growth mindset. For such transformations to happen, the full support of the top management is vital.
There are major hurdles that companies face while moving towards AI adoption. Lack of AI experts within the company is a huge challenge and hiring the right talent has been quite difficult.
Another challenge is to get stakeholders across the board to support adoption and the necessary transformation. There is a need to align their leadership, capabilities, behavior, and operating model with technology different from any other they have ever encountered.
Additionally, many of the leading companies are finding it challenging to implement AI at scale, leading them to shelve some AI projects and focus on high-impact ones. Companies also face the challenge of modifying their machine architecture, and org structures in order to grow AI at scale. A sound strategy needs to be formed first, without which any action to scale will be pointless.
Executives will also need to grapple with build or buy decisions with respect to AI projects. Build or buy is something executives have been doing for many years but this time around such decisions will be very complex because AI is a very different creature that comes with a different set of implications.
The stakes are very high and companies need to be proactive. If vendors are used for AI projects or even certain subtasks, special care must be taken such that valuable information that can lead to loss of competitive advantage is not given out.
Additionally, dependence on vendors may result in loss of control of critical initiatives that may affect multiple parts of the business because AI projects can easily impact multiple disciplines within a company.
Companies also need to critically assess the quality of data available with vendors to effectively implement these AI projects, before handing over projects.
Companies also need to think about the social impact of the products they build using AI. Also, they must try to ensure that they consider societal implications, every step of the way.
AI will enable some economies to move further than others. Economies that have leading AI players will have an almost unfair advantage over others and will be able to deliver immensely greater value while being much more efficient.
Also, AI will lead to a lot of localization with most goods and services being produced within a country, leading to low imports and exports. Taxation will also be re-looked at since robots will take up a lot of jobs and governments may need to tax companies for using these AI robots.
Therefore, executives at companies need to consider such evolving micro and macro factors, caused by AI, while making decisions. Since AI is still evolving, companies must build strategies with a long-term view in mind, while being flexible enough to adapt to unexpected developments in the field of AI as well as in society.
The possibilities are endless and the problems to solve for humanity are plenty. There has never been a more exciting time than today because we have powerful tools like AI to solve the world’s most challenging problems and take humanity to greater heights. A period of exponential growth has begun and we should be geared up and excited to embark on this amazing journey. We must have the agility and capability to constantly adapt our business to keep up with the world that is rapidly evolving at a pace like never before.
Also published here.