“The Web as I envisaged it, we have not seen it yet. The future is still so much bigger than the past.” – Tim Berners-Lee 

Over the last decade, Artificial Intelligence (AI) has evolved at a staggering pace, which has made it a critical tool across sectors. Consumers now expect better user experiences, higher levels of personalization, faster service, and smarter options. With AI being dubbed as one of the most important technological breakthroughs in the history of humankind, experts opine that it has the potential to change the world similar to how electricity did during the second industrial revolution. 

Several leading publications have speculated that 2022 will be the year of AI – when AI-related technologies will transition from an experimental phase to one of practical application. In this context, here are a few areas where I believe AI will make a significant impact in the future.  

Logistics and Supply Chain 

In the logistics and supply chain industry, AI is proving to be a game-changer. The industry is facing increasing challenges in the aftermath of the pandemic. A highly uncertain world with geopolitical shifts driven by conflicts of an economic and military nature is also adding to the complexities, disrupting sectors from crude oil to computer chips. When implemented correctly, AI has the potential to help companies make smarter and more agile decisions. AI is helping create a “new normal” that’s more automated, intelligent, and efficient in the global supply chain and logistics industry. 

Workforce Re-alignments 

Workforce re-alignment is another megatrend that the pandemic has accelerated. The nature of work and the workforce of today is changing rapidly, driven by advancements in connectivity and cognitive technologies. The evolution of the industry goes beyond human factors such as demographics, the rise of the gig economy, digital automation, and more. Although there were efforts to establish AI-driven process automation during the pre-pandemic era, due to the remote nature of work, it has now become more of a necessity than before. However, organizations need to be careful while implementing AI-based solutions and avoid an approach of automating everything. Rather, they should adopt a measured, calibrated, hybrid approach to make AI effective in achieving organizational success. 

Surveillance and Cybersecurity 

Over the last few years, the world has become increasingly more digital. Although this shift has made life easier in many ways, it has also made the world more vulnerable to cyber threats. From individual to national assets, cyber threats have been growing in scale and reach. The introduction of complex assets such as cryptocurrencies and non-fungible tokens (NFTs) has made the situation even more complex.    

In such a scenario, AI can and will play an increasingly important role in the prediction and identification of cyber threats, not to mention corrective action that can be taken, enabling organizations to mitigate such risks proactively. AI will play an equally important role in the security and surveillance space. AI-driven video and analytics are becoming commonplace in several countries. 

With this astronomical rise in the use of AI across spheres, fundamental questions on important matters such as individual privacy and rights vis-à-vis those of the state come to the fore. This is where ethical AI becomes very important. 

Ethical AI 

Ethics is a complex, intricate, and perplexing topic. Ethics can be defined as the moral standards that guide an individual's or a group's behaviour or actions. To put it another way, ethics is a set of principles, norms, or guidelines for determining what is good or right. Every technological breakthrough has a good and bad side, and AI is no exception. AI models and related technologies intrinsically depend on data, and hence it is on data that our discussion must centre. 

Data Privacy 

AI as a technology is essentially data-hungry. As a result, the eagerness and enthusiasm to improve and sharpen algorithms have often tended to disregard aspects of data sensitivity in many situations. However, the introduction of regulations such as the General Data Protection Regulation (GDPR) as well as the rise in general awareness of the misuse of data will hopefully lead to even more robust compliance standards in the future.  

Several recent instances of backlash against some of the major technology companies have underlined the importance of measures that enterprises need to take when dealing with data. Going forward, it will also be the responsibility of lawmakers as well as the state to be more proactive in ensuring data privacy.  

Bias and Fairness  

In the past, we have seen organizations as well as intended beneficiaries suffering huge liabilities owing to the bias inherent in AI solutions. Therefore, we need concerted efforts to design AI solutions that operate on principles of fairness. We also need to create awareness among our workforces on the danger of biases creeping into the solutions that we develop. It is important to institutionalize governance mechanisms for AI operations to oversee the development and production of AI at every step.  

Transparency and Explainability  

AI is often labelled as a black box. This means that we often struggle to explain the process behind how AI-based solutions result in their intended outcomes. This ambiguity often leads to distrust in AI-based solutions because, as human beings, we prefer to understand how things work. Hence, a lot of research will be required over the next few years to make AI more explainable. Explainability is a critical factor in making AI-based solutions both reliable as well as ethical. 

Accountability 

Last but not least, the AI development process needs to include accountability for its predictions and actions. This would involve defining the boundaries as well as the extreme conditions for failure.  

In conclusion, AI will make strides in several directions in the future. What we discussed in this article is just the tip of the iceberg. The balance between the sheer necessity of AI-based solutions and their support systems on one side, and the ethical development of such solutions on the other, will be critical in transitioning AI from an experimentation phase to one of helping solve real-world, complex problems. The sweet spot at which the ability to harness the power of AI meets the limits of the human imagination will be a defining moment in this journey.