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In today’s fast paced & volatile VUCA world, leaders face unprecedented challenges. They need to navigate through volatility while staying focused on strategy, business performance and culture.  Artificial Intelligence is fast becoming a game changing catalyst and a strategic differentiator and almost a panacea to solve large, complex and unresolved problems.  To be an AI powered organization, leaders not only need to have a broad understanding of AI strategy, they need to know how and where to use it.  AIQRATE advisory services and consulting offerings are designed to enable leaders and decision makers from Enterprises, GCCs, Cloud Providers, Technology players, Startups, SMBs, VC/PE firms, Public Institutions and Academic Institutions to become AI ready and reduce the risk associated with curating, deploying AI strategy and ensuing interventions and increase the predictability of a durable leader’s success.

In the age of the bionic enterprises, AI continues to dominate the technology & business landscape.  Under the aegis of transformation, disruption and innovation, AI has several applications and impact areas which usher a new change in how we make decisions in the enterprise and personal spheres.  Traditionally, human decisions are to a large extent based on intuition, gut and historical data.  In the age of AI, several of our decisions will be taken by algorithms.  Leveraging AI, the ability to mimic the human brain and the ensuing ability to sense, comprehend and act will significantly go up and will result in emergence of augmented intelligence in decision making. Enterprises, GCCs, SMBs, Startups and Government Institutions are attempting to harness the power of AI to change the way they do business.  All these industry segments are looking at AI becoming the secret sauce behind making them gain a competitive advantage.  If you have not started yet, you are already behind the competition, however large or pedigreed you might be.

So, where are you placed on your AI journey?  At AIQRATE, we can guide you on your journey of understanding what AI can do for you, embedding it within your business strategy, functional areas and augmenting the decision-making process.

At AIQRATE, we are here to help you with the art of the possible with AI.  Through our bespoke AI strategy frameworks, methodologies, toolkits, playbooks and assessments, we will bring seamless Transformation, Innovation and Disruption to your businesses.  Leveraging our proven repository of consulting templates and artifacts, we will curate your AI strategic approach roadmap. Our advisory offerings and consulting engagements are designed in alignment with your strategic growth, vision and competitive scenarios.

We are at an inflection point where AI will revolutionize the way we do business. The paradigms of customer, products, offerings, services and competition will change dramatically; and being AI-ready will become a true differentiator.  AIQRATE will be your strategic partner to help you to prepare for what’s next in order to stay relevant.

Enclosing here our journey walk through of 2020.

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.

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.

For the many potential application areas where artificial intelligence can deliver breakthrough transformation, unlock efficiencies and augment human life, one of the most impactful areas – at least in the Indian context – will be the value it can bring to the field of agriculture.

According to Indian Brand Equity Foundation, nearly 58% of India’s population relies on agriculture as their primary source of livelihood. The total export of agricultural commodities for India is expected to hit $38.1 billion in FY18, making the country one of the top 15 exporters globally.

However, agriculture in India is riddled with systemic problems, which AI is well placed to address. First, the traditionally unorganised agriculture sector continues to be difficult to organise due to our vast geography combined with our cultural and linguistic diversity. Second, farmers tend to be severely cash-strapped owing to smaller landholding, leading to lower farm productivity. Due to this poor cash position, capital expenditure associated with mechanisation and technology adoption continues to remain slow – thus precluding the possibility of improving farm productivity. Third, farmers are unable to tap a wider base of knowledge and tend to rely on community-based sources of knowledge. This handicap keeps them insulated from the progress of their peers.

Almost all the other large and small topical problems faced by farmers in India flow from one of these three macro-level problems – a lack of organisation, weaker capital muscle to introduce modernisation initiatives and poorer access to a larger, refined body of knowledge. On the bright side, with improved data collection techniques and easier access to cutting-edge research, enabled with better access to telecommunications infrastructure and improving technology literacy, these endemic problems might finally see an amenable solution; combined with the burgeoning interest of the private sector and thrust by the government for modernising farming.

So, it is time for artificial intelligence to step in and deliver a smarter, leaner and productive agrarian sector. Here are the three novel ideas for improving and augmenting farm productivity output using AI:

Communication in Local Languages
AI-powered technology provides a unique opportunity for agriculturists to break down traditional linguistic barriers. Through the infusion of natural language processing (NLP) and machine learning in agriculture, farmers would be able to communicate and learn from their peers and with civic bodies in their local language.

One such example is the development of ‘Kisan Helplines’. Farmers typically need to reach out to civic bodies for information and guidance but this may get hampered due to the poor availability and limited scalability offered by human-centric contact centres. Step in, chatbot. Chatbots can offer high availability, high scalability and reduce the wait time for farmers in need of advice or warnings. Through multi-lingual chatbots powered by NLP, farmers could get easier access to information that may be extremely critical for their activities.

Further, using an NLP-powered communication exchange, they would be able to connect with peers across States for information. Such a communication platform would help crowdsource information on weather progression and potential pest attacks. It would also help seek reviews and feedback on a multitude of farm procurement – seeds, fertilizers, pesticides and mechanical equipment – as well as trade information on successes and practices. By reducing this communication gap and enabling farmers to organise, they would be empowered to make better decisions from sowing to selling and everything in between.

Improving Farming Productivity
While increasing landholding might be challenging, AI can help make the most out of the land available by improving the yield through farm productivity interventions.

By using AI techniques such as machine vision and deep learning, farmers will be able to make many small tweaks to their farming activity that will ultimately result in sizeable gains. For instance, in the pre-sowing and sowing period, machine vision will help farmers monitor soil health and defects – helping them take corrective action early. During the sowing period, farmers can use AI to predict the near-term weather conditions, which can inform the optimal time to sow their seeds. Prior to the harvest, they could harness the power of machine vision to mitigate the impact of pest attacks. Finally, at the time of harvest, machine vision can help farmers grade their produce and demand the right market price.

One such brilliant example of this in action is Microsoft’s initiative with the International Crop Research Institute for the Semi-Arid Tropics (Icrisat), Hyderabad. Their AI sowing app uses machine learning and Microsoft’s PowerBI platform to predict and inform farmers the optimal date to sow. A feature phone capable of receiving text messages is all they need to receive this information. Early results from the use of this technology suggest a 30% improvement in yield per hectare achieved through 175 farmers, who were part of this programme.

Another example is the work being done by Bengaluru-based startup CropIn. Their suite of solutions captures data from multiple sources to provide real-time actionable insights – around weather information, scheduling and monitoring farm activity, monitoring crop health and estimating size of harvest. AI-enabled solutions such as these have great potential to reduce inefficiencies and uncertainties.

Pricing and Supply Chain
While pricing strategies and supply chain optimisation have reached a high level of maturity in the corporate sphere, there is a huge scope for them to impact the agricultural space in India. Using AI, farmers would be able to sense market demand for their goods and keep a pulse of customer choices and seasonality. Market sensing around consumer demand would help them aggressively price their goods and yield a better return.

AI-powered supply chains, on the other hand, can help improve their bottom line by reducing the costs incurred in managing distributed logistics and a multitude of middlemen. Through technology-powered communication networks and smarter routing, smaller farmers too will be able to organise their route-to-market more efficiently and gain benefits of scale. They would also be able to get their perishable goods to market quicker without the intervention of middlemen – thus reducing wastage and losses. An efficient, synchronous farm-to-table supply chain would help bring down their costs and improve their financial position.

At present, AI is only making its first inroads into agriculture. A multitude of use cases exists beyond these as well – from accurately forecasting commodities in the futures market to speeding up loan approval requests through faster credit scoring. AI for agriculture is not only a huge opportunity for startups and large organisations. It also offers the chance to make a huge impact in a sector crying for outside help to improve its operations and productivity.

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.

Agriculture Sector
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

Smart Cities
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

Education System
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

Healthcare
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

Legal Challenges
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.

We know the quakes are coming. We just don’t know how to tell enough people early enough to avoid the catastrophe ahead. Around the world more than 13,000 people are killed each year by earthquakes, and almost 5 million have their lives affected by injury or loss of property. Add to that $12 billion a year in economic losses to the global economy (the average annual toll between 1980 and 2008). Understandably for some time scientists have been asking if earthquakes can be predicted more accurately.

Unfortunately, the conventional answer has often been “no”. For many years earthquake prediction relied almost entirely on monitoring the frequency of quakes or natural events in the surroundings and using this to establish when they were likely to reoccur. A case in point is the Haicheng earthquake that occurred in eastern China on February 4, 1975. Just prior to this earthquake, the temperatures were high and the pressure was abnormal. Many snakes and rodents also emerged from the ground as a warning sign. With this information, the State Seismological Bureau (SSB) was able to predict an earthquake that helped to save many lives. However, this prediction was issued on the day when the earthquake occurred, so it did cause heavy loss of property. Had this earthquake been predicted a few days earlier, it could have been possible to completely evacuate the affected cities, and this is exactly where big data fits in.

Nature is always giving cues about the occurrence of events, and it is simply up to us to tune in to these cues so that we can act accordingly. Since these cues are widespread, it is best to use big data to collectively bring in this data to a central location so that analysis and the resulting predictions are more accurate. Some common information that can be tracked by big data is the movement of animals and the atmospheric conditions preceding earthquakes.

Scientists today predict where major earthquakes are likely to occur, based on the movement of the plates in the Earth and the location of fault zones. They calculate quake probabilities by looking at the history of earthquakes in the region and detecting where pressure is building along fault lines. These can go wrong as a strain released along a section of the fault line can transfer strain to another section. This is also what happened in the recent quake, say French scientists, noting that the 1934 quake on the eastern segment had moved a part of the strain to the eastern section where the latest quake was triggered.

Academics often put forward arguments that accurate earthquake prediction is inherently impossible, as conditions for potential seismic disturbance exist along all tectonic fault lines, and a build-up of small-scale seismic activity can effectively trigger larger, more devastating quakes at any point. However all this is changing. Big Data analysis has opened up the game to a new breed of earthquake forecasters using satellite and atmospheric data combined with statistical analysis. And their striking results seem to be proving the naysayers wrong.

One of these innovators is Jersey-based Terra Seismic, which uses satellite data to predict major earthquakes anywhere in the world with 90% accuracy. It uses unparalleled satellite Big Data technology, in many cases they could forecast major (magnitude 6+) quakes from one to 30 days before they occur in all key seismic prone countries. It uses open source software written in Python and running on Apache web servers to process large volumes of satellite data, taken each day from regions where seismic activity is ongoing or seems imminent. Custom algorithms analyze the satellite images and sensor data to extrapolate risk, based on historical facts of which combinations of circumstances have previously led to dangerous quakes.

Of course plenty of other organizations have monitored these signs – but it is big data analytics which is now providing the leap in levels of accuracy. Monitored in isolation these particular metrics might be meaningless – due to the huge number of factors involved in determining where a quake will hit, and how severe it will be. But with the ability to monitor all potential quake areas, and correlate any data point on one quake, with any other – predictions can become far more precise, and far more accurate models of likely quake activity can be constructed, based on statistical likelihood.

So once again we see Big Data being put to use to make the impossible possible – and hopefully cut down on the human misery and waste of life caused by natural disasters across the globe.