What constitutes ‘fair use’ of data is increasingly coming under scrutiny by regulators across the world. With the digital detonation that has been unleashed in the past few years, leading to a deluge of data – organisations globally have jumped at the prospect of achieving competitive advantage through more refined data mining methods. In the race for mining every bit of data possible and using it to inform and improve algorithmic models, we have lost sight of what data we should be collecting and processing. There also seems to be a deficit of attention to what constitutes a breach and how offending parties should be identified and prosecuted for unfair use.

There’s growing rhetoric that all these questions be astutely addressed through a regulation of some form. With examples of detrimental use of data surfacing regularly, businesses, individuals and society at large are demanding an answer for exactly what data can be collected – and how it should be aggregated, stored, managed and processed.
If data is indeed the new oil, we need to have a strong understanding of what constitutes the fair use of this invaluable resource. This article attempts to highlight India’s stance on triggering regulatory measures to govern the use of data.Importance of Data Governance

Importance of Data Governance

Before we try to get into what data governance should mean in the Indian context, let us first look at the definition of data governance and why it is an important field of study to wrap our head around.

In simple terms, data governance is the framework that lays down the strategy of how data is used and managed within an organisation. Data governance leaders must stay abreast of the legal and regulatory frameworks specific to the geographies that they operate in and ensure that their organisations are compliant with the rules and regulations. A lot of their effort at present is aimed at maintaining the sanctity of organisational data and ensuring that it does not fall in the wrong hands. As such, the amount of time and effort expended on ensuring that these norms are adequately adhered to is contingent upon the risk associated with a potential breach or loss of data.

In effect, a framework of data governance is intended to ensure that a certain set of rules is applied and enforced to ensure that data is used in the right perspective within an organisation.

Data Governance in Indian Context

India is rapidly moving towards digitisation. Internet connectivity has exploded in the last few years, leading to rapid adoption of internet-enabled applications — social media, online shopping, digital wallets etc. The result of this increasing connectivity and adoption is a fast-growing digital footprint of Indian citizens. Add to this the Aadhaar programme proliferation and adoption – and we have almost every citizen that has personal digital footprint somewhere – codified in the form of data.

With a footprint of this magnitude, there is an element of risk attached. What if this data falls in the wrong hands? What if personal data is used to manipulate citizens? What are the protection mechanisms citizens have against potential overreach by stewards of the data themselves? It is time we found answers to these very pertinent questions – and data governance regulation is the way we will find comprehensive answers to these impending conversations

Perspectives for India

The pertinent departments are mulling over on a collective stand that should be taken while formulating data governance norms. For one, Indian citizens are protected by a recent Supreme Court ruling that privacy is a fundamental right. This has led to a heightened sense of urgency around arriving at a legislative framework for addressing genuine concerns around data protection and privacy, as well as cybersecurity.

As a result of these concerns, the Central government recently set up a committee of experts, led by Justice BN Srikrishna, tasked with formulating data governance norms. This committee is expected to maintain the delicate balance between protecting the privacy of citizens and fostering the growth of the digital economy simultaneously. Their initial work – legal deliberations and benchmarking activity against similar legal frameworks such as GDPR (General Data Protection Regulation) – has resulted in the identification of seven key principles around which any data protection framework needs to be built. Three of the most crucial pointers include:

1. Informed Consent: Consent is deemed to be an expression of human autonomy. While collecting personal data, it is critical that the users be informed adequately about the implications around how this data is intended to be used before capturing their express consent to provide this data
2. Data Minimisation: Data should not be collected indiscriminately. Data collected should be minimal and necessary for purposes for which the data is sought and other compatible purposes beneficial for the data subject.
3. Structured Enforcement: Enforcement of the data protection framework must be by a high-powered statutory authority with sufficient capacity. Without statutory authority, any remedial measures sought by citizens over data privacy infringement will be meaningless.
Striking the right balance between fostering an environment in which the digital economy can grow to its full potential, whilst protecting the rights of citizens is extremely difficult.

With a multitude of malafide parties today seeking to leverage personal data of citizens for malicious purposes, it is crucial that the government and the legal system set out a framework that protects the sovereignty and interests of the people. By allaying fears of misuse of data, the digital economy will grow as people become less fearful and more enthusiastically contribute information where a meaningful end outcome can be achieved.

Artificial intelligence is fast making its way into mainstream education. I do not infer as part of the standard technical curriculum. But several schools, colleges, universities and other academic institutions are adopting AI in the process of delivering impactful education to students and their numbers are rapidly increasing.

Across the world, we are seeing AI augmentation in different facets of the education system – from automating routine tasks that teachers have to perform to crafting personalised education curriculum that is line with a student’s aptitude and areas of interest.

The education sector in India suffers from deep-rooted challenges that need wholesale solutions. The bulk of our students is compelled to go through archaic pedagogical methods that are employed to deliver static and outdated curricula.

For a while now, Bill Gates and other tech stalwarts have been excited by the idea of infusing AI into the education system. Bill Gates calls this bouquet of technology-driven, impactful delivery of coursework as ‘Artificially Intelligent Tutoring Systems’ and hopes that it leads to better internalisation of course content. This column shares some of the areas where AI can leave its mark on the education system and revolutionise the way the next generation of students learn.

Freeing up Teacher’s Time

Teachers are burdened with several menial, low-value tasks that are ripe for an AI augmentation. These tasks neither deliver better learning outcomes nor improve student experience. The time our teachers spend performing hygiene activities – from taking the attendance of the class, evaluating and grading tests and assignments and performing peer reviews – is enormously wasteful.

The time spent by teachers can be easily unlocked through AI, helping them focus on what they do best – teaching and coaching for success. Bringing in AI into the core way-of-working of schools today will help eliminate these burdensome tasks in the following ways:

• By curating tests for students automatically based on the aptitude of students in the classroom. Rather than relying on teachers to conjure up questions in the classroom, AI can help tutors assess the learning level of students and contextually bring up questions. Teachers will be able to administer tests much more easily by using a gradational question bank powered by AI

• Grading the administered tests and assignments. This is another time-consuming and often low-value task that can easily be taken up by AI administered-tests. AI can help automate the repetitive task of grading tests, thus helping teachers focus more on how they can create a better platform for learning by coaching and solving questions from students. AI-graded tests can also help bring up commonly occurring patterns of errors (ie, are students mainly making the same mistakes?), in effect providing input to teachers on which lesson plans require more impetus in the next class

• Ease out repetitive administrative tasks. Teachers also spend hours over the year submitting periodic reviews to their supervisors and coordinators, taking attendance and peer reviewing the efficacy of other teachers. This workload can also be supported by AI – by maintaining automated attendance logs, summarising the test scores of students and reporting the performance of teachers

Curricula, Content Planning

The present-day curricula delivery process is largely inefficient. The current paradigm requires a teacher to deliver pre-designed, standardised content to a classroom full of students with diverse aptitudes and interest levels. The negative impact of current pedagogical methods can still be manifested through the employability score of the current generation.

By leveraging the variegated applications powered by AI techniques, academia will not only be able to deliver more personalised curricula and lesson plans but also improve students’ understanding and retention of the coursework, leading to an improvement in educational outcomes. Here are a few examples of how we can enable those:

• AI can be instrumental in creating a culture of continuous improvement among teachers. By tracking their performance across different key metrics, the educational system will be able to uncover the areas where teachers need support and coaching more effectively. AI can also help curate the coursework for teacher improvement, thus making sure that teachers are continuously updated and can continuously refine their craft

• By infusing AI into the skills and aptitude assessment process for students, schools will be able to better judge the current level of understanding among students for a particular subject area as well as where their innate inclinations lie. Often, students are unclear or unsure about how they can make the most of their talents and how they can channel them into a trade. AI can help schools map out the data of previous students, their career achievements and tie that back to educational research. This will allow schools to accurately predict the subjects for which a student has a natural inclination and then coach her in that direction

• AI can also use data around student attention, interest combined with their aptitudes and abilities to recommend customised coursework. This will help students build a structured career path. This AI-centric approach will foster personalised training pathways and provide students with the skills needed to succeed in their future professions, rather than burdening them and staggering their confidence as the current system does

Optimising Classroom Experience

To fully unleash the creativity and expertise of teachers, the education system needs to also imbue AI-led applications in the classroom on a day-to-day basis. This will enable teachers to work at full throttle. Time spent on minding students and reorienting classroom methods to ensure better student engagement can be saved by using AI in the following ways:

• AI can help improve the tracking of students’ attention levels and help teachers intervene before a student loses interest in the classroom content. While teachers are conversant in minding students that actively disrupt the classroom, engaging students who are quietly inattentive is a comparatively difficult task. By employing attention tracking devices, teachers can much easily monitor the attentiveness of the class and mind them before they tune out

• By aggregating the attention scores of the classroom, AI can help teachers devise a more potent mix of teaching, testing and activities – to continuously ensure better class performance and engagement

AI can bring a plethora of benefits to the education system at large, providing improved educational outcomes to all stakeholders – students, teachers and parents. Through personalised curricula, improved efficiency in the time management for teachers and effective in-class monitoring and assistance, AI can shift the paradigm of how the education system works and how coursework is consumed and leveraged by the next generation of students.

The adoption and benefit realisation from cognitive technologies is gaining increasing momentum. According to a PwC report, 72% of business executives surveyed believe that artificial intelligence (AI) will be a strong business advantage and 67% believe that a combination of human and machine intelligence is a more powerful entity than each one on its own.

Another survey conducted by Deloitte reports that on an average, 83% of respondents who have actively deployed AI in the enterprise see moderate to substantial benefits through AI – a number that goes further up with the number of AI deployments.

These studies make it abundantly clear that AI is occupying a high and increasing mindshare among business executives – who have a strong appreciation of the bottom line impact delivered by cognitive systems, through improved efficiencies.

AI-first Mindset

Having said that, with AI becoming more and more mainstream in an organisational setup, piecemeal implementations will deliver a lower marginal impact to organisations’ competitive advantage. While once early adopters were able to realise transformational benefits through siloed AI deployments, now that it is fast maturing as a must-have in the enterprise and we will need a different approach.

To realise true competitive advantage, organisations need to have an AI-first mindset. It is the new normal in accelerating business decisions. It was once said that every company is a technology company – meaning that all companies were expected to have mature technology backbones to deliver business impact and customer satisfaction. That dictum is now being amended to say – every company is a cognitive company.

To deliver on this promise, companies need to weave AI into the very fabric of their strategy. To realise competitive advantage tomorrow, we need to embed AI across the organisation today, with a strong, stable and scalable foundation. Here are three building blocks that are needed to create that robust foundation.

1. Enrich Data & Algorithm Repositories

If data is indeed the new oil (which it is), organisations that hold the deepest reserves and the most advanced refinery will be the ones that win in this new landscape. Companies having the most meaningful repository of data, along with fit-for-purpose proprietary algorithms will most likely enjoy a sizeable competitive advantage.

So, companies need to improve and re-invent their data generation and collection mechanisms. Data generation will help reduce their reliance on external data providers and help them own the data for conducting meaningful, real-time analysis by continuously enriching the data set.
Alongside, corporations also need to build an ‘algorithm factory’ – to speed up the development of accurate, fit-for-purpose and meaningful algorithms. The algorithm factory would need to push out data models in an iterative process in a way that improves the speed and accuracy.

This would enable the data and analysis capabilities of companies to grow in a scalable manner. While this task would largely fall under the aegis of data science teams, business teams would be required to provide timely interventions and feedback – to validate impact delivered by these models, and suggest course-corrections where necessary.

Another key aspect of this process is to enable a transparent cross-organisation view into these repositories. This will allow employees to collaborate and innovate rapidly by learning what is already been done and will reduce needless time and effort spent in developing something that’s already there.

2. AI Education for Workforce

Operationalising AI requires a convergence of different skill sets. According to the above-cited Deloitte survey, 37% of respondents felt that their managers didn’t understand cognitive technology – which was a hindrance to their AI deployments.

We need to mix different streams of people to build a scalable AI-centric organisation. For instance, business teams need to be continuously trained on the operational aspects of AI, its various types, use cases and benefits – to appreciate how AI can impact their area of business.
Technology teams need to be re-skilled around the development and deployment of AI applications. Data processing and analyst teams need to better understand how to build scalable computational models, which can run more autonomously and improve fast.
Unlike a typical technology transformation, AI transformation is a business reengineering exercise and requires cross-functional teams to collaborate and enrich their understanding of AI and how it impacts their functions, while building a scalable AI programme.

The implicit advantage of developing topical training programmes and involving a larger set of the workforce is to mitigate the FUD that is typically associated with automation initiatives. By giving employees the opportunity to learn and contribute in a meaningful way, we can eliminate bottlenecks, change-aversion and enable a successful AI transformation.

3. Ethical and Security Measures

The 4th Industrial Revolution will require a re-assessment of ethical and security practices around data, algorithms and applications that use the former two.

By introducing renewed standards and ethical codes, enterprises can address two important concerns people typically raise – how much power can/should AI exercise and how can we stay protected in cases of overreach.

We are already witnessing teething trouble – with accidents involving self-driving cars resulting in pedestrian deaths, and the continuing Facebook-Cambridge Analytica saga.

Building a strong grounding for AI systems will go a long way in improving customer and social confidence – that personal data is in safe hands and is protected from abuse – enabling them to provide an informed consent to their data. To that end, we need to continue refining our understanding around the ethical standards of AI implementations

AI and other cyber-physical systems are key components of the next generation of business. According to a report by semiconductor manufacturer, ARM, 61% of respondents believe that AI can make the world a better place. To increase that sentiment even further, and to make AI business-as-usual, and power the cognitive enterprise, it is critical that we subject machine intelligence to the same level of governance, scrutiny and ethical standards that we would apply to any core business process.

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.

In these columns, I have earlier attempted to highlight the possibilities of multiple game-changing applications in the field of artificial intelligence (AI) that hold the potential to deliver positive benefits to humanity. AI can do much more beyond enabling a transformative business impact. Every technological advancement brings with it an opportunity to deliver positive changes to society. It is no different with AI. With massive increases in computing power and data deluge, it is possible for AI to be a harbinger of change for the society we live in.

A complex problem that we see is Autism Spectrum Disorder. As of 2015, this neurodevelopment disorder is estimated to have affected the lives of 24.8 million people globally. Worse, despite all the advances in medical sciences, there is no conclusive understanding of its causes and cures. While we do see cases where autism gets resolved, we are still unable to point concretely to any medical option that works better than others.

At best, autism can be managed – possibly through a mix of early diagnosis and continuous therapy. Thankfully, we live in an era where the awareness of autism is on the rise and the associated stigma is declining. By harnessing the power of AI to detect and manage cases of autism, we could potentially help those suffering from it lead a fulfilling life of dignity and respect.

While not conclusively proven, it is likely that an early diagnosis of autism is hugely beneficial for managing it in the future. An early detection would also help the people around these children be better attuned to their condition and enable them to set an environment that is conducive to their development.

One such application is applying AI to analyse vocal and behavioural cues exhibited by children. Stephen Sheinkopf — an autism researcher and psychologist at the Brown University Center for the Study of Children at Risk — believes anomalous crying patterns of babies might serve as an early warning sign of autism.

Neurological cues present in the acoustic features of cries — pitch, energy and resonance — might hold the key to early detection. Combining vocal, behavioural and physiological data, we might be able to piece together a pathway to early detection. That’s where AI and machine learning can add really great value, in integrating these disparate pieces of information that might otherwise be hard to make sense of.

An excellent example of this in action is Chatterbaby — conceptualised and developed by Ariana Anderson, a computational neuropsychologist at UCLA. Earlier, functionality enabled identification of potential causes for why a baby is crying by monitoring crying patterns. In addition, Chatterbaby is also attempting to identify if there are discernible differences in the crying patterns of autistic children and neurotypical ones — ultimately aiming to isolate the characteristics of each group to detect autism early.

Early ‘Testing’

Researchers in the UK and Italy have turned to AI for developing what might be the world’s first ‘test’ for autism. In their study, they leveraged AI to compare the protein levels in the blood samples of two groups of children — one group comprising 38 children diagnosed with autism, and the other with 31 children without the diagnosis. Their findings helped develop an algorithm that could test autism — with a 90% accuracy for which children have autistic spectrum disorder and an 87% accuracy for which children do not have it.

Another example is through tracking changes in brain function of six-month-old babies, which researchers from UNC Chapel Hill and Washington University’s School of Medicine believe can help early detection. They recently published a paper wherein they examined the brain scans of 59 high-risk babies to understand the connections and interactions between different regions of the brain. Post this, they analysed the brain scans of the 11 babies that were eventually diagnosed with autism.

By combining the data with AI and deep learning, they developed an algorithm able to detect the possibility of autism with an accuracy of 9 out of 11.

Chatbots and Virtual Assistants

Across studies, we see children with ASD have high levels of comfort with computers due to their predictable and logical nature. Autistic children can perceive humans to be emotional and unpredictable but computer-based systems (even those with human expression) to be rational and non-judgmental.

Systems such as chatbots and social robots can help capture and track the progress of autistic children, continuously monitor their social behaviours and make quick, informal assessments in school and at home. The big promise of these systems is allowing autistic children the opportunity to navigate social interactions, unfamiliar environments while aiding them to reach the developmental goals usually set for neurotypical children.

An example of this is an app called Companion, featuring a virtual assistant named Abby. Abby helps identify interests and needs of autistic children and provides support throughout the day. Beyond this app, Identifier identifies talents of autistic children through interactive game-play; eventual results are compiled onto a dashboard detailing the skills and shortcomings of the child.

A Strict No-No

While AI can bring these benefits, it is also important to ensure that these systems are designed with empathy for the autistic population. For instance, the use of bright, jarring colours is an absolute no-no for the design of such applications. Secondly, virtual agents should be enabled to speak in simple language, without the use of idioms, euphemisms or figures of speech to help such people understand instructions much better.

For a problem that has no apparent cause or cure, AI could be a breakthrough in improving the quality of life of those who suffer from autism and the people around them. By using AI for early diagnosis, disease management and people enablement, we would be able to help bring dignity to the lives of affected people.

Over the last few years, technology has been rapidly permeating the sporting arena — from DRS in cricket to VAR (video assistant referee) and goal-line technology in football. Moneyball – the seminal book and movie on the use of analytics for smarter player acquisition – was the tipping point in how analytics and AI could be gainfully used in managing the business of sports better.

For years, avid sports fans around the world have had the topline analytics for their favourite players and teams at their fingertips. From there, today carrying analytics on player performance is seeing a massive data detonation. Sports analytics is no longer a mere water-cooler conversation. It is increasingly a specialised science that is rapidly informing the way professional sports team pick, monitor and coach players – in the process, even transforming the way a game is played.

As sports become more competitive, the margin for error is becoming smaller than ever. Teams need to incorporate every possible scientific element that helps improve player performance for a sustained improvement in their rankings. One such science being co-opted in sports is Artificial Intelligence – which is now a key component in the way teams identify players with high potential, monitor in-game progress to provide feedback and provide coaching for long-term success to players. Let us unpack these three crucial areas and see how AI is playing a key role in managing sports teams.

1. Identifying Top Stars

With a wealth of data available right from lower rungs to youth team records, AI can be hugely decisive in scouting for players likely to be mainstays of the senior levels of sport for years to come. By ingesting performance data for each player from their very first game, AI can uncover youth players who have a strong potential or can complement existing players in the team.

Union Minister of State for Youth Affairs and Sports Rajyavardhan Rathore believes that AI will help sports pick future stars. His plan is for the Indian government to create a database of nearly 3 crore young children in the age group of 5 to 18 years, who will be further refined and trained based on their abilities in different sports.

2. Performance and Insights

The major chunk of identified AI use cases falls in the arena of monitoring key performance indicators of sportspersons and providing insights for improvement. A lot of investment has already gone into the development of various sensors and devices that can track speed, accuracy, motion during practise sessions and games.

These monitoring devices – coupled with computer vision and machine learning – will be crucial interventions in the way coaches and players refine their approaches to their sport. Whether it be identifying potential areas to work on during training, or an analysis of situations within the game, AI can combine well with coaches to uncover how to extract the best performance from their teams and players.

An example of this in action is Dutch company SciSports. The company has developed a product called BallJames. Leveraging computer vision and machine learning, the product uses 3D images to provide insights on movements and tactics for football players.

3. Coaching for Success

Augmenting coaches and long-term training programme is the third and rapidly emerging use case of AI in sports. Depending on the sport being played, elite athletes spend between 10 years and 20 years at the very senior level, and possibly 10 years before at junior levels before becoming elite pros. All of which means that these athletes require continuous mental and physical conditioning to stay at the top of their game for nearly 30 years – depending on the sport they are playing.

AI can be an important intervention in building customised training routines for sportspersons – helping them take a long-term view of their health and diet. For instance, using AI, players could identify areas of improvement on the physical side and strengthen muscles, which may be adjudged as below-par for their sport. On similar lines, AI-led apps could also help suggest dietary options based on the conditioning required and provide personalised pathways for these athletes depending on their unique physical characteristics.

Mental Health

The other equally, if not more important aspect of managing the long-term well-being of athletes, is mental health. Sports players often live away from their families, go through swings in form alongside enormous highs and lows of success and failure. Through all of this, it is critical that we also manage the long-term psychological health of athletes to ensure that they stay at the top of their game. Apps such as jolt.ai – chatbots for motivation and tracking workout adherence – could be instrumental if created specifically to manage the mentality of sports players.

While the above mostly dealt with the ‘game’ aspects of sports management, there is also a plethora of topical applications that can be utilised in the management of sports franchisees (fan loyalty and engagement, player acquisition and abilities matching and uplift from sponsorship deals) and sports administrators (spend and procurement management, asset management and tracking legal compliance and fraud). When you combine these two areas, it is evident that Artificial Intelligence could very well be the next MVP in sports!