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The idea that AI will conjure up an apocalyptic, robot-ruled future, where mechanical overlords govern humans is an extremely low probability event, even in the very distant future. In fact, not only are AI-driven interventions accelerating business outcomes – AI is also helping nations around the world find new avenues for enabling positive social outcomes for their people.
For all the evolution and development of humanity and technology over the years, our world still faces pressing systemic challenges that affect humanity at a large scale. From our complex and labyrinthine legal systems to the inefficiencies in our healthcare sector, large-scale problems still abound. The need of the hour is to better connect the people with the basic facilities they require. AI may not be a panacea in and of itself, but it offers a huge potential to improve the quality of life of people across the globe. Thankfully, today multiple nations have the intellectual capital – our peers in the software engineering and AI domains – that can bring substantial dividends for the population at large.
In this article, I will attempt to touch upon how AI can be used to address large, complex and unsolved problems and contribute to improving the quality of life for humanity. In keeping with WTISD’s topic for this year – Enabling the Positive Use of AI for All – I’ll share a social perspective on how AI-powered innovations can be hugely transformational to the world:
Improve Access to Healthcare Facilities
Available statistics show that over 45% of WHO Member States report to have less than 1 physician per 1000 people. (World Health Organization recommends a ratio of 1:1000). When you add to that the inequitable spread of doctors across certain countries, we have a poorly served population. While the life expectancy at a global level is 72 years (average across both males and females), the disparity between regions can be startling. For instance, the average in the WHO’s Africa region is a low 61.2 years. By imbibing AI, we can deliver an exponential improvement in health outcomes by improving medical adherence to reduce readmissions, tracking patient medical histories, improving access to physicians, reducing the time spent in clinics and prescribing personalized treatment pathways. Using AI, we can:
- Identify high population density areas that are currently underserved by hospitals. This can provide policymakers with inputs on how they can improve the deployment and availability of doctors, medical equipment and medication
- Leverage early warning signals through alternative mediums such as social media tracking for public health studies to provide guided diagnosis and interventions
- Create a digital record of patients’ medical histories and their clinical notes and use that as a reference for prescribing evidence-based treatment options and developing tailored treatment pathways
- Improve patient medical adherence by identifying individuals without health insurance, providing coverage and incentivizing the use of appropriate medication and treatment
- Speed up routine clinical processes such as scanning and annotating X-Rays and CT-Scans using computer vision and prescribing actions to physicians.
Revamp the Education System
The education system is undeniably critical for shaping future generations. However, both quality of and access to education is incredibly disparate across the developed and developing worlds. Curricula can often be outdated, thus not providing students with the skills they need for their careers. Problems abound in the education sector – from a high level of student dropouts, quality and methodology of teaching and lack of workforce readiness among students. While policymakers mull over how education can be made more contemporary and effective, AI can help provide guided interventions in the field of education by:
- Mapping the aptitude and interest of students in schools and universities with skills that are demanded by the market. This will help provide prescriptive career guidance that will be beneficial to both the employers and the future workforce
- Tracking the demand for skills in the market and the educational infrastructure available to supply those skills, through a Skills Repository. This will help keep education concurrent with current market demands and ensure much better alignment between academia and corporates
- Automate routine, time-consuming tasks – from creating and grading test papers, developing personalized benchmarks for each student, identifying gaps in student development, tracking aptitude and attentiveness within each subject, and enabling teachers to focus on curriculum development, coaching and mentoring, and improving behavioral and personality aspects of students
- Identify potential school and university-level dropouts and their root-causes so educational institutions can take proactive steps to ensure student retention and course completion.
Address Legal and Law Enforcement Challenges
Globally, we face structural issues in areas of law enforcement and jurisprudence. Globally the average police-to-people ratio is 1 police personnel per 604 people, which is lower than the UN recommended standard of 1 per 454. Poor law enforcement eventually lends itself to a high crime rate and an overburdened legal system. AI can be a hugely pertinent gamechanger for global governance systems and help law enforcement officers improve surveillance by augmenting police efforts, automate a variety of routinized legal tasks and improve transparency in governance. By bringing the potential of AI in law enforcement, we can offer:
- Surveillance and identification of wrongdoers; areas recognized for high criminal activity can be done through computer vision
- Review and summary-creation of long drawn cases and their history can be done through natural language processing and voice recognition
- Routing Right-to-Information and governance-related citizen requests through intelligent bots, thus making it more efficient to get critical information
- Employ Anomaly Detection frameworks to surface fraudulent transactions – especially among land deals.
A global population of over 7.7 billion people, distributed across a huge landmass throws up a sizeable challenge when it comes to scalability. With many individual nations crippled by the inability to serve their populations, their public services need technology-centric solutions that are scalable and intelligent at the same time. Artificial intelligence will effectively address a number of these problems which are of socio-economic importance, and will go a long way in improving the quality of life of humanity at large. To enable this, public services need to act sooner rather than later and ramp up their data sets, identify and onboard technology, innovation and research partners for ideating and applying AI techniques that can power humanity’s next big leap.
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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.
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.
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The traditional archetype of learning and employment – where students went to university and learned a skill that served them for the entirety of the careers – is rapidly evolving and changing. Today, we are in the age of continuous learning – where there is an inherent expectation on members of the employed workforce to constantly upgrade their knowledge and soft and hard skills. Earlier, executive education used to be a reserve of a privileged few high performers at large organizations, who showed great promise and rapidly rose through the ranks. Now, continuous learning across all the segments of the workforce is increasingly the new normal – almost to the point where it is the mandate for organizations that wish to grow and succeed in the business sphere. There is an expectation now that even the rank-and-file of the organization devote time to learning, unlearning & relearning and apply newfound ideas and techniques into their area of business.
What has been the driver behind this change? Why has upskilling and reskilling become the norm for the contemporary corporate career? A few important reasons underpin this change. The nature of the business today is extremely dynamic. Business environment and competitive landscape are changing faster than ever, with technology becoming the mainstay of the modern business. Tech-enabled startups are moving in and challenging traditional incumbents across industries. These changes are made a further complex with emergent ideas and changing paradigms of organizational management and leadership. In today’s fast-moving world of business, it is a critical priority for executives to keep their organizations nimble, proactive and armed with every arrow in their quiver, to ensure the continued success of their firms
Executive education is an important medium to achieve this goal and helps bridge the skill gap that is almost certain to rise when industries and organizations face structural headwinds. However, for executive education to live up to the promise and deliver value to employees and their organizations, we need to re-look at the programs itself. We need to ensure that the coursework and curriculum are topical, contextualized and relevant, whilst being personalized to the needs of the organization and its professionals. Here are a few perspectives on how executive education can be adapted for the industry4.0 era:
Expand the Scope of Executive Education and the Courseware
As we dismantle the traditional paradigms of work and education, we also need to rewire our traditional understanding of what an executive education comprises. For years, corporations relied on top-tier management schools and universities to facilitate the essential leadership training for their workforce. In today’s world, rewiring an understanding of leadership is just not going to cut it. Executive education programs need to add more in terms of practical, on-the-job skills, that will help employees perform better and remain relevant to the needs of the business.
There is now a strong case to expand the scope of executive education beyond traditional B-schools and include even MOOC-based education – which is provided by a plethora of websites today. Coursera, Udemy, LinkedIn Learning – to name a few – provide very tactical, hands-on understanding of essential, practical skills that the workforce can put to use right away, while also facilitating the career change aspirations that employees may have. Organizations need to seek out these MOOC-based providers to augment the executive education curriculum in a way that increases its scope and reach among employees.
These programs could very well help employees refresh their skill sets. For instance, such programs could help coders become well-rounded full-stack developers. Similarly, those with data engineering skills could be moved into areas such as high-performance analytics or artificial intelligence. Team leads could be educated formally in the tools and techniques associated with product management. For mapping current employee skills with the contemporary requirements of the business, MOOCs can be a critical intervention to incrementally upskill employees in their domain of work.
Incorporating the importance of shorter, tactical courses
Whilst there is no doubt about the value provided by a long-form one-year executive education program, companies also need to consider the benefits of short-term tactical coursework. Corporations need to augment their training programs with shorter, time-boxed courseware that can deliver instant impact for the organization.
There are two reasons why this is important. Firstly, given the speed at which technology and business mature, it may not always make sense to put someone in a one-year program and wait for the delivery of associated results. In such circumstances, short form courses help deliver faster time-to-value – with employees able to deliver results in weeks, rather than months. Secondly, shorter-term courses also help reduce some of the inherent barriers people have towards learning. Shortening the learning cycle, putting it to use immediately and seeing real-life results, can help employees see instant benefits of the lifelong learning paradigm and break down mental barriers to learning.
Co-create multiple, personalized career pathways
The key word here is ‘personalized’. We need to move away from the old thinking of one-size-fits-all training to deliver more tailored, fit-for-purpose and relevant executive education to employees. To start, organizations need to develop skill maps and assessments – to identify where the workforce is today in terms of the required skill sets and where they are expected to be. Once this is performed, L&D teams can help create personalized learning journey-maps for their employees – based on the career interests and aspirations of employees. For instance, for some employees, it may make sense to provide a refresher and upskilling in their current areas of work and for others, it may make sense to reskill them in new areas of the business. Either way, developing a personalized training regimen for the executive education of the employees will deliver better results and help them excel in their field and improve the efficacy of learning programs too.
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It is critical that public services ramp up their data sets, identify partners for ideation and leverage technology
For all its growth and development since independence, India faces many systemic problems. From our complex and labyrinthine legal system to the inefficiencies in our agricultural sector, large-scale problems still abound.
We need to better connect our burgeoning population with basic facilities. While Artificial Intelligence may not be the panacea in itself, we need to harness its potential to improve living conditions. Thankfully, we have the intellectual capital – our information technology peers – that can bring substantial dividend in this arena. By combining our inherent technological prowess and the keenness of our government in promoting technology-led interventions, AI can truly be a game-changer for India. Here’s an India-specific perspective on how AI can be a force for good for our country.
Though the agricultural sector sees piecemeal improvements, numerous problems go unresolved – from low yield, low predictability of yield, poor access to institutional credit and financing to lack of transparency around pricing for produce. Using AI, agriculture can be transformed by:
• Provision of on-demand information on quality of seeds, fertilizers, pesticides and the track record of providers and opportunities for mechanisation through better equipment. This can be done through bot-enabled ‘Kisan Helplines’ that can provide guided advice for improving productivity
• Improving predictability of yield by ingesting data on soil health, equipment quality, farmer activity and weather conditions
• Improving visibility of market price trends for crops produced (domestic and international) so that they can make informed decisions on pricing, while exploring going to market without intermediary interference
• Leveraging data from productivity, yield and forecasts and potential prices to assess creditworthiness of individual farmers. This will speed up disbursement of finance and ensure farmers get better rates for crop insurance
Indian cities have grown in an extremely unplanned manner, with public infrastructure and services struggling to catch up. Consider this – the cost of traffic congestion alone in just four major cities is estimated to be $22 billion annually. With AI, urban planners can:
• Track movement of traffic and people to identify opportunities for ‘decentralising’ major hubs and develop future-ready public infrastructure to facilitate smoother movement of people, vehicles and goods
• Model population density and availability of sanitation facilities to improve access. Additionally, by applying image analytics on drone surveilled images can help determine quality of sanitation facilities and accelerating their upkeep
• Identify and improve access to current and emergent residential and commercial hubs by creating more optimal public transport networks
• Align consumption of resources – energy, water, cooking gas – to actual needs
• Crowdsource, store and take action to improve infrastructure by directly soliciting participation from citizens
• Improve planning and forecasting for infrastructure development through better coordination between public works departments, leveraging traffic data and streamlining supply chains
The education system in India is among the most outdated and unequitable when compared with the developed world. Problems abound from a prominent level of student dropouts, to quality and methodology of teaching, lack of workforce readiness among students and outdated curricula. Here’s how AI can help improve certain facets:
• Track the demand for skills in the market and the educational infrastructure available to supply those skills through a National Skills Repository. This will help keep education concurrent with current market demands
• Automate routine, time-consuming tasks – from creating and grading test papers, developing personalised benchmarks for each student, identifying gaps in student development, tracking aptitude and attentiveness within each subject – and enabling teachers to focus on curriculum development, coaching and mentoring and improving behavioural and personality aspects of students
• Identify potential dropouts and root-causes, enabling educational institutions to take proactive steps to ensure student retention and course completion
The doctor-to-patient ratio in India is quite poor – with 0.62 doctors available per 1,000 people (WHO recommends a ratio of 1:1,000). When you add to that the inadequate spread of doctors across the country, we have a poorly served population, ranking 125th in the world for life expectancy. Using AI, we can:
• Identify areas with a high population density, which are underserved by public hospitals. Further, improve the deployment and availability of doctors, medical equipment and medication to better serve the population
• Track patient histories and clinical notes to prescribe evidence-based treatment
• Speed up routine processes such as scanning X-rays and CT-scans for malignancies using image analytics
• Improve public health studies by identifying early warning signals through alternative methods such as social media tracking
• Identify individuals without health insurance and incentivise their usage to improve patient medical adherence
When adjusted for VIP protection, India claims an extremely poor police-to-people ratio with 1 police for every 663 people. There are 27 million cases pending with courts, of which six million have been pending for over five years. AI can be a crucial enabler for our crumbling governance system and can help:
• Speed up review and summary writing of long drawn cases and their
history using natural language processing and voice recognition
• Use image analytics for surveillance and identification of wrong-doers in areas recognised for high criminal activity
• Surface fraudulent deals – especially among land deals – using anomaly detection frameworks to speed up delivery of justice
• Improve public services and transparency by routing RTI requests through intelligent bots, thus making it more efficient to get critical information
With a population of over 1.3 billion people, distributed across a huge landmass, public services urgently need technology-centric solutions that are both intelligent and scalable. AI will effectively address a number of these problems. To this end, it is critical that public services act sooner than later and ramp up their data sets, identify technology partners for ideation and apply AI techniques to power the India’s next leap forward.