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Artificial Intelligence is Revolutionizing Business Decision Making

Artificial Intelligence is revolutionizing Business Decision Making says technology leaders Amazon and Google. So far the emergence of this disruptive technology has been breathtakingly flawless to experience and provides evidence as to what we can expect in the future.

Artificial Intelligence is Revolutionizing Business Decision Making

The technological advancement in today’s digital age has fast-paced every aspect of life, so how can enterprise decision making be left alone? Yes, over the last few years, we have rapidly moved from heuristics to analytics-driven decision making. Now with emerging technologies like artificial intelligence around the corner, business enterprises are witnessing revolutionary impact on how they make decisions today.


In the VUCA (term used to describe or to reflect on the volatility, uncertainty, complexity and ambiguity of general conditions and situations, drawing on the leadership theories of Warren Bennis and Burt Nanus) phase, businesses globally are now pivoting to an AI-led, algorithm augmented style of decision-making. With massive growth in computing power, backed by a steady rise in data storage capacity and analytics proficiency, we are undergoing a probable paradigm shift. Of course, it’s an interesting scenario wherein Artificial Intelligence will take over a powerful role in enhancing and extending human intelligence as well as enabling decision-making with complete self-governance. It will not only minimize human biases and blunders that are common with heuristic decisions, but also reduce the time consumed in making these critical decisions.


Through this post, it’s my endeavour to highlight how we migrated from simpler data-driven decisions to AI-powered decision-making. So far the emergence of this technology has been breathtakingly flawless to experience and provides evidence as to what we can expect in the future. Furthermore, in this article, I will cover a few critical facets that need to be ingrained by organizations on the transformation journey of AI, and provide a few profound cues that will make this shift more exciting and beneficial.

Let us take a look at how we got to AI from Analytics based data-driven decision support. Some truly groundbreaking events happened along the way while we were putting in our best effort to make more accurate business decisions, compelling us to envisage how decisions will be made in the enterprise.

With data inundation and digital explosion, combined with the appreciation of the fact that robust analytical capabilities result in more informed decisions, organizations already working on AI are rapidly maturing into compute houses. Data science, the interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge as well as meaningful insights out of data, is in vogue. Why? For the simple reason that we now understand data is inherently dumb when seen in isolation. It is the capability to process this data and identify patterns and inconsistencies, using hi-tech sophisticated algorithms and en masse techniques, which makes all the difference. The real value is in these self-intuitive algorithms as they define the intelligence necessary to uncover insights and make smart recommendations.


Organizations today are progressive and emerging into algorithm factories. Those working on exponential technologies, believe that by making continuous advancement in mathematical algorithms possible, they can deliver consistent decisions based on formulated as well as evolving business rules.


Indeed, it is now an established fact that companies with robust mathematical capabilities possess a massive cutting-edge advantage over those that don’t. It's this math-house prowess that makes technology leaders like Amazon and Google stand out from the rest, with their unique ability to understand their customers better, identify deviations and recognize key patterns.


If you recollect, we have seen a similar evolution in the age of analytics, wherein the science and value skewed from descriptive analytics, providing diagnostics of past events to prescriptive analytics, helping visualize and shape the future. We are observing a similar evolution in how Artificial Intelligence gets leveraged in the enterprise and where its maximum value lies.

In the initial days of AI implementations, it was considered as simply a tool for predicting and forecasting future situations, while detailing out the volatility of the external ecosystem.

Today, AI-powered decision-making is more prescriptive, with the technology providing enterprises not just having a sneak peek into the future, but also key diagnostics and recommendations on potential decision options and their payoffs. Such advanced applications of AI can help organizations make decisions that can potentially explore more business opportunities, while unravelling potential risks, planning on contingencies and avoiding threats much earlier.


The way AI is advancing, the day is not far when smart algorithms would be introduced in every walk of life and business. At the same time, algorithms will undergo more improvement leading to what will be the most catapulting business change since the industrial revolution. Organizations which already are not, will soon start developing a suite of algorithmic IP’s that will de-bias most enterprise decisions. If algorithms are going to drive most enterprise decisions of tomorrow, we need to create some checks and policies to ensure that it does not go haywire. It is more critical at present than ever before that the algorithmic suite built by organizations has a strong grounding in ethics and can handle situations appropriately for which explicit training may not have been put in place.

As we engage more into an AI-based process of decision-making, we will require organizational transformation from business, cultural, practice and technical standpoints. The following aspects will be the enablers of this change:

Setting Appropriate Attitude and Mindset - Implementing AI in the organization requires a combination of data scientists and computer scientists. As AI matures in the organization, the users, use cases and data will increase exponentially. To deliver impactful AI applications, scale, robustness and extensibility are critically important. This is where having an appropriate attitude and mindset to build something for the good is necessary. Inculcating best engineering practices will help standardize the use of these applications while ensuring that they are scalable, robust and extensible.

Developing Learning Atmosphere - The other important facet of a culture where AI can thrive is creating an atmosphere supporting continuous unlearning and relearning. AI can succeed if the resources developing and utilizing it are rewarded for continuous, research, experimentation and exploration. Also just like AI, resources should be encouraged to incorporate feedback loops and learn continuously. As technology matures, it's pertinent that the existing workforce is mandated to keep up, which will require leadership involvement. It's critical that the knowledge of algorithm theory, applied mathematics alongside training on AI library and developer tools, is imparted to the workforce, and the organizational knowledge base is continuously updated to reflect new breakthroughs in this status quo.

Embedding Human-Centric Design-Thinking - Finally, given the nature of artificial intelligence applications, it's critical that they are consumed avidly. User input very often activates the learning cycles of AI applications. To ensure high usage of these applications, it's very much necessary that we put the user at the center while designing these applications. This is where the application of behavioral sciences and human-centered design will deliver impact and more purposeful results. By imparting empathy in these applications for the user, we will be able to design better and more useful AI applications.

As we empower decision-making with algorithmic, AI-centric applications and systems, one of the biggest outcomes expected is that they will bring unfathomed efficiencies in terms of timelines and cost, alongside improvement in the speed and quality with which decisions get made. Now we need to rethink and deliver on enterprise decision-making that is increasingly shaped and augmented through artificial intelligence. For an AI transformation, it is vital to keep track of how the applications are progressing and how to explore the potential of AI for maximum benefits.


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