For the past few years, Artificial Intelligence has initiated unlocking value gains through the automation and augmentation of routinized operational activity. But are we underestimating the potential of machine intelligence? Does it make sense to relegate a powerful technology to perform tactical tasks? Or can AI move further upstream and help corporate boards make more accurate, strategic decisions?

The possibility of AI to enable better decision-making has been heavily discounted thus far. However, with Artificial Intelligence capably enabling more informed decisions in the realm of healthcare and investment banking – two of the most complex arenas where AI has been deployed – the possibility of having machine cognition in the boardroom no longer sounds too far-fetched. At the end of the day, corporate boards make complex decisions, that have huge ramifications for the future of their organizations. It is important that these decisions are based in fact, rather than judgement. AI can help corporate boards make faster, more accurate and unbiased decisions. AI can help inform strategy by giving executives a better understanding of their internal and external environments. Let us look at some key areas where senior executives in organizations can look at making better decisions using Artificial Intelligence.

AI for Executive Decision-Making

Corporate boards and top executives are charged with maintaining the health and competitiveness of an organization. They are responsible for the long-term sustainability and success of their organizations. This, in turn, requires them to stay ahead of the curve and understand their business landscape and intelligently deploy capital across inorganic and organic growth channels. Executives also own the key metrics for their organizations – and ensure that the overall return for the shareholder capital employed continuously beats industry expectations. Let us look at how AI can help transform the activity of executives in these areas.

Executive leaders often rely on numerous reports around key organizational metrics to make decisions that can have massive implications for their businesses. Is a particular segment of the business growing rapidly? Are some cost centers underperforming on their efficiency metrics? Are there laggards in the product portfolio of the enterprise that are dragging performance down? All these numbers have to figuratively be at the tip of an executive’s tongue – so that in key meetings decisions that affect the future of the business can be made more accurately and quickly.

AI-powered smart assistants would be extremely critical to help push the needle on making executive decisions with accuracy and speed. With intelligent bots, executives can be provided updates on the most critical metrics that they care for at the right time when they need them. With AI, it is possible to personalize the insights that are sent to executives – so that they are able to drill down and understand the basis for each metric.

Unbiased Capital Allocation on R&D and M&A Activities

Corporate boards and executives also need to take the long term view of how their companies evolve to thrive in the future. This requires intelligent bets to be taken on budgetary spending – for both organic and inorganic activities. How much money needs to be realistically spent on Research and Development activity and how it can it help corporations maintain larger moats against their competition? Can corporations look at inorganic acquisitions to accelerate the growth of synergistic capabilities that can form much more compelling value propositions?

AI will soon be able to provide comprehensive answers to such questions. By leveraging data from multiple sources combined with intelligent algorithms, AI will be able to weigh these multiple options and identify which one is best suited for each unique situations. In this way again, AI can help executives forecast which decisions can have maximum impact on financial metrics and model the long-term health of the organization.

As corporate boardrooms take serious cognizance of having robotic counterparts augmenting the decision-making process, it is important to consider certain caveats. For AI to work to its full potential, it is important to ensure that it is provided high quality data and continuously refined algorithms. We have seen the fallouts of algorithms going awry before. Biased algorithms working off bad data sets create issues that could potentially disrupt the fabric of the organization. It is therefore important that organizations ensure the implementation of explainable AI that can provide the rationale and take accountability of the decisions that it powers. Finally, it is important that executive leaders also create the right culture within their organizations for AI to thrive. A combination of human intelligence and artificial intelligence is the future and hence it is critical that companies relook at their culture to ensure that both can amicably survive together and put the organization on the right path.

According to research by McKinsey, it is estimated that 16 percent of board of directors did not fully understand how the dynamics of their industries were changing and how new technologies could impact their businesses. This gives AI a huge window of opportunity to permeate through global boardrooms and power better decisions. Decisions that can keep their organizations financially healthy, focused on the long-term and competitively differentiated against their competitors.

AI and blockchain are two of the prime drivers in the technology space that catalyze the pace of innovation and demonstrating radical shifts across every industry. Each of this technical venture comes with a degree of technical complexity and business implications. Fusion of the two will be able to redesign the entire technical landscape along with a human effect from scratch.

Blockchain has its own limitations, it is a mix of  technology-related and culture influence from the financial services sector, but most of them can be conceited by AI in a way or another.

The illustrated points below will be able to give a gist of the potentials that can be realized at the intersection of AI and Blockchain:

Energy consumption in mining: Mining has already proven that it requires tons of energy and is heavy in the economic perspective. AI has mastered in optimizing energy consumption across multiple sectors, similar results can be expected for the blockchain as well. AI can dramatically reduce the costs of maintaining servers and validate potential savings to lower investments in mining hardware.

Federated Learning: Blockchain is growing at a steady pace of 1MB every 10 minutes. Blockchain pruning  is a possible solution through AI. A new decentralized learning system such as federated learning, for example, or new data sharing techniques to make the system more efficient.

Security: Concerns still exist on the security system of built-in layers and applications  for Blockchain  (e.g., the DAO, Bitfinex, etc.). The mileage created by machine learning in the last two years makes AI a solid candidate for the blockchain to guarantee secure applications deployment, especially given the fixed structure of the system.

Blockchain-AI Data gates: Blockchain has proven its ability for record keeping, authentication, and execution while AI drives decisions by assessing/understanding patterns and datasets, ultimately engendering autonomous interaction. The combo (AI and blockchain) will be become a data gate with these several characteristics that will ensure a seamless interaction in the nearest future.

Auditing of AI through blockchain: AI is seen as a black box ( complex set of calculations and algorithms) to distinguish patterns or trends. This makes it a difficult task for the humans to govern the choices taken by the artificial intelligence in yielding results. Accountability of the AI black box is seen as biggest challenge, considering  concerns across the community for tampering or the altering happening to the calculations for the given input which eventually reflects in the output generated. This challenge can be easily comprehended by the blockchain innovation. Implementing robust auditing of these calculations utilizing the blockchain is seen as the biggest driver for enhancing the credibility of the business organizations and reinstating trust in the reliability of the information.

Leverage on Artificial Trust: Future roadmap of this fusion can successfully lead into creation of virtual agents that will create new ledger by themselves. Machine to machine interaction will be the new norm reinstating trust in a secure way to share data and coordinate decisions, as well as a robust mechanism to reach a quorum.

Machine performance monitoring and changes: Blockchain miners (companies and individuals)  pour an incredible amount of money into specialized hardware components. AI can complement such as machine/equipment monitoring to deploy more efficient systems and do away with the unproductive heavy ones.

Blockchain for better information management: AI has a proven mechanism that runs of an incorporated or centralized database. In such a case, there are always chances for information occurrence of a mishap, i.e. gets lost, altered, or undermined. 

Blockchain and artificial intelligence fusion can eliminate the above concern. Under the umbrella of blockchain the data is decentralized and stored within different nodes or systems. This reinstates trust on  that your information is safe and unaltered. Most importantly the information is time-stamped and is in the sequence making recuperation less demanding and exact. 

Some key challenges on the block: The fusion throws open technical and ethical implications arising from the interaction between these two technologies, such as the need to edit data on a blockchain and most importantly the duo pushing to become data hoarder. Experimentations alone will be able to provide a detailed answer on these lines.

In conclusion blockchain and AI are the two sides of the technology spectrum. One efficiently fosters centralized intelligence while the other promotes decentralized applications in an open-data environment. The fusion of the two will be an intelligent way to amplify positive externalities and advance mankind, most importantly reap the maximum potential for business needs.

A robust ecosystem for small and medium enterprises is one of the key indicators of economic vibrancy and entrepreneurial energy in a nation. India’s entrepreneurial spirit was given a massive boost after liberalisation in the early 90s. These norms ended the draconian ‘Licence Raj’ that kept the lid on the business aspirations of the average Indian. With improving access to capital, heightened ease of doing business and a galvanised ecosystem that provides mentorship and guidance to fledgling startup founders, the small and medium enterprise (SME) sector has been riding high over the past couple of decades. Now with Artificial Intelligence (AI) in the mix, SMEs will be given another boost – through reduced cost and improved efficiencies in how they run, operate and succeed.

There is no doubt that AI will be an important gamechanger for the SME sector. Startups today are much more data-rich than before and understand the value that can be unlocked through intelligent deployment of advanced analytics. They understand that a data-driven understanding of their business landscape will far outweigh heuristic methods in the dynamic environment in which their enterprises operate. Further, with lowering cost of adoption, increased focus on the SME industry by incumbent analytics/AI vendors and partners, we have a perfect storm of sorts for the sector to derive the exponential benefits of this technology. Let us look at the areas where AI can deliver a strong, demonstrable impact on the sector and how such businesses can get started on their AI journey.

Galvanising SME Operations

When run on the right data set, AI can work its magic in providing untold operational benefits to SMEs. The case for AI in the startup sector is much stronger than it is for their larger corporate counterparts. The reasons for that are two-fold. First, startups typically operate on smaller budgets – which means that they need to automate as much as they can to reduce costs associated with a higher headcount.

Secondly, startups by their very nature are extremely nimble, allowing them to experiment rapidly with new, innovative technologies. This twofold advantage means that AI vendors as SMEs need to have a robust strategy in place to work together and uncover the latent advantages offered by this technology. Here are a few areas where AI can specifically help startups galvanise their operations.

• Predictive maintenance: SMEs, especially in the manufacturing segment, can unlock huge benefits in the production process using AI. With sophisticated algorithms monitoring machine health, AI can help reduce the downtime in production schedules by accurately modelling when a critical machine is likely to go down, allowing businesses to better plan demand fulfilment.

• Supply chain and logistics: A major drain on the revenue of nascent businesses is the cost associated with procurement of raw materials and delivery of finished products. By using AI and third-party location data, SMEs can plug this drain by powering faster and leaner delivery schedules. Similarly, demand planning and order fulfilment will get a big boost as SMEs learn how to forecast accurately through machine learning models, thus reducing the waste that entails unused, unsold and unutilised inventory.

• Marketing and sales: Multiple SMEs tend to go under because they take on much large corporations with massive sales and marketing budgets. AI can help these startups level the playing field. By using data from each prospect interactions as well as leveraging emerging breakthroughs in the field of programmatic advertising, AI can help fine-tune the marketing programs of startups and help deliver better ROI on their spend. Similarly, through an improved understanding of their territory, AI can provide laser-guided focus to sales people on which prospects to focus on and what approaches can deliver the best results.

• Customer service: Where large enterprises can afford to outsource customer service operations or even bring them in-house, SMEs do not have these advantages. In today’s environment, customers are shown to be more loyal when provided with a superior customer experience. AI can bridge the gap between customer expectations and the constrained budgets available to provide those. With intelligent assistants, SMEs can navigate common questions and complaints put forth by customers and provide a superior customer service at much lowered costs of delivery.

• Talent acquisition: SMEs often have vastly varying needs for talent. For instance, those that are on a strong growth trajectory need to staff their companies rapidly before the competitive advantage they offer slips. Those that are on a slower curve also need to make sure that they hire candidates with the right mix of experience and attitudinal attributes to ensure the smooth functioning of their business.

AI can help reduce the time taken to identify the right candidates by rapidly screening resumes to identify the best fit for the needs of the business. Further, with the right data and training, AI can also administer relevant tests to candidates and grade their performance, thus reducing the requirement of human intervention and time taken to screen good candidates.

Getting Started

Let us look at some of the key factors that business leaders need to keep in mind as they get started on their AI journey.

• AI starts with data: The first consideration before planning an AI intervention is to understand whether high quality data is available for AI to work its magic. Without the right data sets, even the best algorithms can go awry. It is essential that business leaders ensure that their data repositories are sufficiently rich to get started on the AI journey.

• Identify the right problems: SMEs tend to be inundated with multiple issues of burning importance. It can be very enticing for business leaders to assume that AI is the panacea for all problems. That is not the case. Business leaders need to identify the right problem statements where AI can make a demonstrable impact and prioritise use cases that can be solved through AI. Scan the market for best practices and learn from peers to better understand what AI can do and what measurable benefit you can derive from AI-led interventions

• Set success benchmarks: For AI leaders, it is important to set a marker for the right expectations with business leaders. Hence, for the business to see continuous improvement in the results delivered by AI, it is critical to identify the right set of business metrics and expected performance against each of those.

Artificial Intelligence today has gone well beyond experimentation to now becoming a real game-changer in how businesses operate. AI can bring significant benefits to startups with improved efficiencies and faster operations. SME leaders looking for strong competitive advantages with respect to their peers would do well to harness the power of this technology and infuse it into their key business process to accelerate outcomes and grow their businesses.

Can artificial intelligence be used in conjunction with mental health practitioners to better discover and address cases of depression and other mental illnesses? We have seen impactful examples of AI being applied to a range of health and wellness use cases, with the potential to help improve the lives of millions of people. Recent advances in this breakthrough technology have also shown the great promise that AI holds in helping individuals and doctors better manage cases and mitigate adverse health impacts of depression and other illnesses that plague the human mind.

Depression is a key problem faced by humans across the world — from milder forms of repetitive depressive mood states to more deeper-seated clinical depression. While we have seen significant improvements in our theoretical understanding of clinical depression, its causes and how to effectively mitigate it, the problem continues to affect millions of people. Psychiatric conditions such as this do not have vaccines or proven preventive medication. So much of the success in helping those with clinical depression hinges on early detection, continuous monitoring and scientific counselling and – in certain cases with a physician’s prescription – medication.

Health Data

At the macro-level, the downside of not managing clinical depression, especially for big populations, can be devastating. According to a study in 2015, it is estimated that approximately 216 million individuals across the globe (3% of total global population) have been affected with clinical depression – with a usual onset in the 20s and 30s. A lack of concerted focus on overall mental health and well-being can lead to outcomes as diverse as high suicide and crime rates to a reduction in national productivity and high levels of substance abuse and addiction.
Today we have a huge repository of health data. By applying machine learning and deep learning algorithms, AI can help individuals and physicians detect early indications of the onset of depression, monitor the progress of patients using medical and other therapeutic options and provide continuous support.

Early Detection

By using AI to understand and highlight speech patterns, use of specific words in text communication and even facial expression, we could possibly improve the speed at which such cases are uncovered. For achieving this, it is important that doctors and machine learning experts work together to identify patterns that have a strong correlation with clinical depression.
A research led by Dr Fei-Fei Li of Stanford University found that using facial and speech recognition data and algorithmic models, it might be possible to detect cases where there is an onset of clinical depression. By leveraging a combination of facial expressions, voice intonation and spoken words, their research was able to diagnose if an individual was suffering from depression with 80% accuracy.

Facebook too has begun piloting its own machine learning algorithm to go through posts to identify linguistic red flags that are indicative of depression. In early testing, the algorithm was said to perform just as well as existing questionnaires that are used to identify depression.

Therapist Interaction

Can AI inform therapists and help them do their job better? With the high and growing number of mental illnesses, the limited number of therapists need AI interventions to help make their job easier while helping improve health outcomes. AI could change the game in the therapy arena by augmenting therapists in identifying subtle signs showcased by patients – such as intonations in voice and facial expressions – which therapists might sometimes miss. Further, AI can also provide guided suggestions to them in providing effective treatment options.

An interesting innovation in this area comes from Ginger. This technology company combines a strong clinician network with machine learning to provide patients with the emotional support that they need at the right time – offering 24/7 availability for cognitive behaviour therapy (CBT), alongside mindfulness and resilience training.

On the back-end, AI is using each patient’s progress to inform its capabilities and make it smarter and more scalable. AI also helps Ginger match its users with a team of three emotional support coaches that it determines to be best suited for the individual.

Continuous Support

The stigma associated with mental illnesses often acts as a bottleneck in uncovering which individuals are suffering from it. Having a non-human companion that can provide human-like compassion can be a possible game changer in helping counsel patients suffering from depression and help them in the rehabilitation process. There are also advantages associated with AI being that counsellor – as it offers higher availability at a much lower cost.

Woebot is an excellent example and a bellwether for what AI could accomplish in the future for this use case. Created by Dr Alison Darcy, Woebot is a computer program that can be integrated with Facebook and is aimed at replicating the typical conversation a patient would have with a therapist. The tool asks about a patient’s mood and thoughts and offers evidence-based CBT. Just like a therapist would in a real-life situation, Woebot ‘listens’ actively in a conversation and learns about the patient to offer interactions more tailored to an individual’s unique situation.

Last year, researchers from the Massachusetts Institute of Technology presented a paper detailing a neural-network model that can be run over raw text and audio data to discover speech patterns that could be indicative of depression, without the need for a pre-set questionnaire. This immense breakthrough will hopefully ripple into more similar research, which could help aid those who suffer from cognitive conditions such as dementia.

While the potential for AI in the field of mental health is massive, it is also imperative that we consider the aspect of protecting the privacy and identity of those included in studies pertaining to this domain. Maintaining data security norms is an extremely crucial part of any AI exercise and in this case, it is even more paramount. To protect the sanctity of AI interventions and participating patients providing the data, it is extremely important to address concerns around data and information security first, before addressing any future use cases in this technology.