Use of AI and ML in the Fight against the Pandemic

Within a few months of COVID-19, the world has changed and we have been forced to look for solutions everywhere. As we grapple with the pandemic, harnessing the potential of technological advances brings us closer to the belief that we will overcome it. In this endeavor, artificial intelligence (AI) and machine learning (ML) have emerged as two key technologies which empower us to better understand and address the COVID-specific issues. Right from prediction to detection, screening, and diagnosing to inching closer towards a vaccine, these two technologies have a critical role to play.

Using ML to screen patients effectively

With every new pandemic, diagnosing individuals is a significant challenge. As tests are expensive at the beginning conducting them on a large scale is a near-impossible task. Any individual who develops symptoms of COVID-19 would be concerned that they have contracted the disease. Instead of taking medical samples from each patient and waiting for slow reports, a more affordable and faster ML-based test would always prove more useful.

Interestingly, a hospital in Florida was one of the first to use machine learning for a strong response to the virus. As patients enter the hospital an automatic face scan (which is based on ML) is conducted to detect whether they are running a fever. This piece of data plays a critical role in triaging patients. Additionally, ML-enabled bots help patients self-identify the next course of action once they are being detected with mild COVID-like symptoms. This saves doctors from spending excessive time on answering questions of worried patients instead of focusing on the treatment.

Detecting patients at high risk

Once a group of people is tested positive it becomes critical to detect patients at a higher risk of developing serious complications or in need of advanced medical care. While many develop symptoms which are mild others may develop acute respiratory distress syndrome (ARDS) or severe lung disease which needs immediate attention. In this context, significant inroads have been made in research and a forecast model has been proposed which depends on the XGBoost calculation (a machine learning algorithm). It helps detect the seriously sick and predict mortality risk. It takes into account three clinical pointers; lymphocytes, lactic dehydrogenase, and high affectability C-reactive protein, an imbalance in which leads to cell injury, compromised cell immunity, and inflammation, also indicative of pathophysiological progress of COVID-19.

Understanding the spread of the contagion

AI-based predictive modeling does not just help in identifying the infected individuals but also in gauging the severity and spread of the pandemic. Early warning systems which are AI-based mine mainstream news, information in other channels, and online content in multiple languages to detect epidemiological patterns which also complement other healthcare networks and syndromic surveillance. A majority of the countries worldwide have opted for contract tracing systems to ascertain the infection routes by using geo-location data to identify individuals who are coming in close contact with the virus carriers. Immediately, text messages are sent as alerts directing them to self-isolate.  

Towards a vaccine

AI has played a critical role in the race to discover a vaccine. By understanding viral protein structures, the technology has suggested probable components of the vaccine. It has also helped medical researchers to scour thousands of literature at an unprecedented pace and arrive at conclusions. As the scientific literature related to COVID continues to grow scientists find it challenging to shortlist papers that are relevant to their specific research. A reputed institute which conducts high-impact research created freely available data sets that are machine readable. This enabled researchers to create and apply NLP algorithms, which would accelerate the process of discovering a vaccine. By connecting the dots between studies, AI makes it possible to identify a hypothesis and subsequently suggests experiments.

Holding on to hope

While modern technologies have helped us so far in this intense battle against the pandemic, we hope that it would continue to assist us in achieving new victories and see to the end of this unfortunate situation. It would not be wrong to assume that the possibility of combining ML, AI, and other convergence points of sectors such as healthcare and life sciences presents a brilliant opportunity for new drug discovery. And, as we leverage this opportunity, we will only stand to gain.

 

Transitioning Back to Work—Strategies You cannot ignore

In the last few months, the COVID-19 crisis has caused unprecedented disruptions in the workplace. This has led organizations to make difficult decisions as they balance between the needs of the business and that of the employees. Along with it, the lockdown restrictions are gradually phasing out and most are preparing to reopen the workplaces. However, to limit the exposure and transmission of the disease, adherence to regulations is critical. It has become imperative for companies to review their existing policies and think of new policies which limit exposure and transmission of the disease.

A center of command

As organizations reflect over new workforce strategies, this will not be a typical recovery. Given the lack of available therapy and the uncertainty over the vaccine, organizations will have to plan for multiple time horizons and scenarios. They may even have to plan for multiple waves of the pandemic considering the resurgence of the virus in countries which had declared themselves virus-free. When we think of alternative ways of working, the critical question which comes up is do we need a command center? Do critical leaders need to be a part of this core group which will help in restarting operations? Will they be able to help in managing external stakeholders? Will the command center be connected to necessary sources for updated news and information which directly affect the business?

Turning to analytics

The next important decision is whether to use analytics and dashboards for informed decision making. What data do organizations need to make accurate quantitative decisions and how do they ensure that they continue to get updated information even from sources beyond the command center? These are questions which companies need to delve into. Data and technology can be used to visualize scenarios and predict what may come next. This scenario planning through analytics will provide business leaders an accurate view of how their decisions will affect customers, staff, and the operational capacity. It may make a significant difference in a strong continuity plan.

A return to work plan has two important components—workplace management and leadership intervention, which we discuss further.  

Office and site management

For a successful transition to work, the first step is to decide on the number of employees returning to the office. Higher the number of employees returning to the workplace, the greater is the risk. This requires a staggered reintroduction and smart workforce scheduling. Determining functions, work, and roles that need to return to workplace and those that can continue to work remotely can help in creating effective rosters and prevent the spread of the disease. For teams working remotely, organizations need to provide adequate support and training.

While reopening workplaces, businesses will need to observe the regulations around social distancing and hygiene practices. Reconfiguring office spaces may prove critical as the requirement for six feet impacts seating arrangements. Organizations may have to set up barriers between workstations and also provide personal protective equipment such as gloves, masks, and eye wear. Additionally, they cannot overlook the basic prevention measures such as routine environmental cleaning and disinfection.

Effective leadership for workforce management

As organizations embark on their process of recovery, they have to commit to the physical, psychological, and financial well-being of their employees. Physical well-being will include a stronger emphasis on safety, easy access to testing, and social distancing. Psychological well-being can be ensured through practices such as flexible schedules and adequate support from managers. It is critical to recognize the diversity of the workforce and help them transition to recovery. The commitment to well-being may have to be extended to the employees’ well-being at home, where they continue to take care of their children or the elderly.

While people priorities remain on top of mind for leaders they need to factor in employee morale. While a percentage of the workforce continues with remote work it brings its own set of challenges. Often they have to juggle priorities and the boundaries increasingly become unclear between work and personal time. The responsibility lies with the team leader to strengthen the team now and help them build mental resilience. During these times of uncertainty, it is up to them to provide support and demonstrate compassion.

Hence it’s a holistic approach which can help organizations tide over these difficult times. It would not be wrong to assume that COVID-19 has reinforced our conviction that human concerns cannot be overlooked, especially for organizations looking to resume operations and adapt to new ways of working. In fact, this crisis provides organizations with a valuable opportunity to build a strong connection with employees and pave the way for a resilient response to the pandemic. Do you agree?

 

Time for a Relook at Travel and Tourism

Germ and Human Mettle

Travel and tourism is a palpable element in living a worthy life. Evidently, with the pandemic, it has been jeopardized to its breadth and depth.

With an industry worth more than $ 9.25 trillion, the splinters create a plethora of whammies – stagflation, trade wars, geo-political embargoes, ecological and environmental disturbances, fear psychosis, technology failures and beyond everything else, the hierarchy of need, want, purchasing ability.

Our social genes reassure us that living a diabolical life is not a solution, but coexistence, circumvention are. Our lives are gradually adopting the new normal. Businesses are endeavoring to re-open, medical science is striving to create a vaccine, and people are trying to be back to ‘office’. And so is their intent to travel germinating. A recent find from Pinterest suggests that people are again searching for travel inspiration, even more than the same time last year.

In this article, we will look at how the travel and tourism ecosystem, as we understand, will evolve.

Holistically, there is a lot of ground to cover

While there is no clear definition of what comes under the umbrella of travel and tourism, the prime agents are:

  • Airlines
  • Airports
  • Hotels & Resorts
  • Vacation rentals
  • Restaurants
  • Cruise companies
Untitled Document
  • Car rental companies
  • Railways
  • Buses
  • MICE operators
  • Activities
  • Retail companies

Interestingly enough, their business models or operations do not run in silos today. It is a complex mesh of underlying synergies, disparities and to a good extent, that of cannibalization. Having said that, the way future unfurls for each of these industries, is a function of travel intents. Let us see how for each of the most prominent travel intents, the industry will respond in the imminent future. 

Intent 1: Country roads, take me home…..

This segment of travellers will evolve as local or near location experience buyers. With airlines newly operating to lesser travelled destinations and tier 3 cities/towns, premium hotels and resorts providing hefty discounts, we will see the intrepid traveller club being energized. With respect to local travel at the destination, it will be skewed towards either car rental services or organized taxi operators with increased reinstatement of safety and personal hygiene.

When it comes to the F&B subdomain, in-house restaurants at hotels will see some positive vibes, along with QSRs making a big come back. Otherwise, the F&B sector will continue with this slump for some time as food adventure takes a back seat now.

Local experiences and outdoor visits will gather steam, whereas activities (like theme parks, games, theatres etc.) will continue in this lull. In gist, sustainability will be the mantra, and customer confidence building the steam to drive.

In the midst of all these, retail players will still have their proliferation – whether it’s digital retail or brick and mortar stores. With local experiences being the first choice, travellers will be somewhat skeptical with products and services available there. Hence digital retail, as we visualize, will thrive.

Intent 2: Swaying to the tunes of the oceans and seas

We don’t really see the big-ticket cruise industry picking up anytime soon. When we are barely on the brink of a recession, these are extravagantly indulgent. Very clearly, this industry has been for select few – only about 8% of the US population ever go on a cruise in a lifetime. Now, when the going is tough, we are apprehensive that travellers will splurge on luxury, laid back vacations.

What is important to understand is that the cruise industry acts as a feeder to multiple other travel and tourism agents. Airlines and airports for getting back home, hotels & resorts in case of a multi-location cruise, car rental agencies, trams, trains and buses for local travel, F&B for local dining experiences, activities at destinations or hops, local retails and everyone else under the sun. Concomitantly, they will all be hurt from this channel revenue fading away.

Intent 3: Work is worship, the workplace is a shrine

Zoom, Hangout, WebEx, GoTo Meetings are ubiquitous now. And we opine this will be the future of remote working. Well, but, we only partially agree to it. Plant engineers cannot work on digital twins for operations, lawyers can’t fight every case from a couch, telemedicine has only a narrow scope, and the list continues. There is proof beyond doubt that business travel will have to resume, with ambidextrous values of work and leisure.

However, as we visualize, airlines will have fewer business class seats to sell (thanks to restrictions on corporate spend) and might re-configure the way plane seats are laid out today. Air ticket prices will drop to lure more customers, may be with a re-designed loyalty program that earns you lesser points that what you gain today. You see, effectively the entire top line and bottom line strategies will change.

What happens to the allied industries of hotels, F&B, car rentals, MICE are natural corollaries.

Our best guess is, it might take 3-4 quarters for it to return to stability and predictability and in a reduced capacity, the whole ecosystem will spring back to life.

Intent 4: Two’s company, Three’s a crowd

ITB Berlin, Japan Expo, NAB Show, Indian IPL, Facebook F8, Wimbledon are only handful that have been scrapped or postponed. There are shiploads more. The tune of the economic impact of this is not limited to the travel and tourism industry, but to the balance sheets of nations and states. And as we envisage, 2-3 years is an optimist’s approach of guessing when normalcy would return for the MICE sector. But read it twice; this will also impact airlines, airports, hotels, F&B, retail, car rental, trains and buses, tours and activities, nature parks and every other player you could think of.

What we see could happen are:

a Smaller and medium-sized local players who have been arranging local events, meetings etc. will emerge as a stronger sub-segment. Across cities, towns and suburbs, they would form own chains and snowball.

b. Webinars will increase in numbers – paid or free

c. Local and franchised hotels will offer conferencing capabilities on a wider scale

Intent 5: Union of two or more

This branch could be split into many – people travelling for personal and social reunions and commitments, students who study in universities abroad and their parents, participants in themed marriages, religious congregations, and many more. Most of these will be brushed off, leaving tiny specs alone. These again will take time to recover. There will be a need to monitor demand patterns for these segments. Disposable incomes need to increase, countries need to free up their borders, the number of students flocking universities need to increase and other social prominences have to re-emerge.

Till then, it is a game of watching the bait.

Can technology find an answer to this uplift?

There are a couple of very crucial pieces in the maze that only technology will be able to solve.

1. Designing one trip – We have said this before. This industry is holistic in nature, no salt added. With a design thinking approach, we need to create the real vision of one trip for customers that include all providers and their services. This not alone offers an advantage for customers, but is also the vehicle to understand what data does not tell. Data, at the moment, is not the fuel as it does not exist for such unprecedented times. It can only corroborate.

2. Track and trace – Track customer personas, their non-classical journeys, spending patterns, location preferences, intents and trip activities, active and passive feedback. This is the only way to understand demand and design supply at the moment. And create and train new algorithms. Period.

3. Jack up bandwidth – Tons of things are being done virtually. Obviously, the need for scaled -up systems and infrastructure is needed. It is something that states, policymakers, service providers and travel players all have to work in tandem on. Cloud infrastructure, with its scalability, will become the obvious choice for many.

4. Fire behind the wall – Cyber security will have to become rock solid. With increased online syndication happening, new payment methods being introduced, sensitivity of information being shared (that were never shared before), unscrupulous activities will also increase. And it is natural that we need to protect the ecosystem.

The last note

A huge section of society will not travel again. In recent years, the industry was experiencing a new segment – mature travellers. Some of them had saved for a lifetime for a cruise may be or for a safari in Africa. This new-found liberation will die.

Consultants who prided in living in airplanes will find a new liberation in Workation, being able to spend some time with family and friends.

Intrepid travellers will find a noble way of channelizing their energy in may be creating rich media content, sharing more experiences that would enthuse people to travel.

Sustainability is the only way to get rid of this pandemic’s fear. Till a vaccine is invented, we will have to keep on close syndication amongst ourselves to create value for travel and to ensure we are safe, to return from the lull.

About the author

Sudipto Ghosh is an Associate Director at TCG Digital Solutions Pvt. Ltd. and oversees digital projects for airline/airport clients. He comes with 14+ years of business consulting experience with key airlines and travel solution providers across the globe. As part of his role across multiple organizations and clients, he has led large digital transformation projects, pre-sales and converted multi-million dollar deals with key stress on transformation, business-IT alignment, systems rationalization, PSS, ecommerce, multi modal transportation.

Sudipto holds a Post Graduate Diploma in International Business (Gold Medalist) from Indian Institute of Foreign Trade and an engineering graduate degree in Information Technology (Gold Medalist).

Ensuring Business Continuity in Times of COVID-19

The COVID-19 crisis has brought with it extreme levels of uncertainty and has created a situation that is well beyond the experience of most of the enterprises. Companies are likely to experience a disruption in their operations as employees are forced to work remotely. They may have to deal with short-staffing and low visibility of workflows. Additionally, they require rethinking how to interact with the potential customers, address their changing demands, and prepare for recovery.

As most businesses choose not to reinvest, B2B organizations need an agile shift in engagement and communication channels. The need of the hour is business continuity and disaster recovery plans which will help wade through the challenges. In this primer, we outline a few thoughts on how businesses can improve their early response to the pandemic. From the trends, it is clear that they need to build on resilience and prepare well-in-advance to pull through the tumultuous times.

Ways to cope

Right at the onset, organizations need to accept that it is not business as usual. It is of utmost importance to prioritize what needs to be done immediately for survival in the long run. It is critical to focus on the well-being of employees, ensure their mental health during self-isolation, and give them a sense of belonging while keeping the team momentum going. It works best for businesses in these times to put the health and safety of the employees first. 

They also need to determine how the crisis will affect their budgets, and if former assumptions no longer remain relevant, they should focus on being agile to bring in necessary changes in financial plans. They will have to find out whether the existing business model is resilient enough to reduce both short-term and long-term costs and help in recovery. They may require new models, which assess the potential risks of the crisis and define effective responses.

Importantly business continuity planning (BCP) requires serious attention from senior leaders. While pandemics are inevitable, investing rightly with immediate effect may have a positive impact on the ability to recover. Any strategic business plan crafted previously for this year may not prove effective. 2020 will be a year of regrouping and survival. Not only will it come with new ways of working and fresh responsibilities, but it will also bring in a new management paradigm.

While existing business continuity plans are reviewed, this crisis is a wake-up call for those organizations which have mainly focused on daily operational needs without investing in long-term strategies. At this hour, a shift of technology capacity to digital platforms will help mitigate the adverse impact of the outbreak to an extent. By “digital platforms” we refer to more pervasive, technology-enabled business models that facilitate necessary exchanges between interdependent groups, either two or more. They bring together the end-users and producers, enabling and empowering them to transact with each other.

For seamless operations

Besides, the above organizations need to focus on operational continuity. Two key areas where CTOs/CIOs can increase access and capabilities are –

  1. Digital workplace resources and
  2. Digital technology to address changing customer behavior

The capabilities of the digital workforce can be increased by identifying their workflows, clearly identifying who can work remotely, the tasks they can accomplish, and the systems they will need access to. If required, the technology capabilities will have to be scaled which may involve empowering the workforce with messaging, collaboration and video conferencing tools, and ascertaining the security need to safeguard business data.

For the second aspect, organizations need to look at increasing the capacity for digital sales and self -service while ensuring personalized remote experiences to ensure a positive experience for customers in unfamiliar contexts. Immediately introducing smartphone apps and chatbots will help solve queries and allow the customer service reps to handle more complex issues. New opportunities that emerge in these times can be embraced to create relevant products, not forgetting that agile practices will enable seamless shifting of the necessary digital and physical resources.

Looking at strong leadership

In these unprecedented times, good leadership can act as a silver lining to a business.
Leaders can consider reducing the full-time work to eighty percent, allowing the employees to come to terms with the crisis. They can consider getting creative together and adopting a clear, empathetic, honest approach to communication until the situation evolves. They can also consider capturing success stories of team members overcoming the adversity and continuing to perform unhindered. These stories can be shared for circulation with the human resource team, which in turn will motivate rest of the workforce.

For a sustainable future

Your response today will determine your future tomorrow. A crisis-agnostic plan is what you need. It may involve exploring the different scenarios and how they may affect the business, adopting a well-thought-out agile working program, creating a dedicated team for crisis-management and training them to act on a war footing when required, crafting a strong stakeholder communication strategy, and lastly ensuring collaboration both internally and externally.

While the response window for most businesses affected will last for a few months, we can measure recovery can only in years. Hence, it’s time to take quick action and adopt a long-term perspective which will enable a company to emerge from this crisis stronger and move towards sustainability.

Why Data Science is Integral to an Insurance Business Today?

Did you realize that whether you are riding the Uber, instructing Alexa, wearing fitness trackers or using the smartphone, a vast amount of data is being generated? Interestingly, an estimate by experts reveals that by 2025, the volume of digital data will increase to 163 zettabytes. Today, this data acts as a treasure trove for most companies who consider evaluating it to generate actionable insights. This trend is sector-agnostic, and across domains, we see a similar pattern – be it in manufacturing, life science and healthcare, energy, retail, utilities or BFSI.  The banking and financial services industry is one domain where data generated and handled is significantly enormous. As the electronic records only grow in number the financial services industry is turning to big data analytics to store data, generate actionable insights, and boost scalability. The insurers are not lagging behind, where they have access to rich sources of data and have figured out the right blend of what, why, and how to use the data to thrive in a highly competitive business environment.

A quick look at the challenges which plague today’s insurance companies tells us why harnessing customer data is more than important now. Growing risks, high operational costs, a volatile market, customer renewal, and churn remain major concerns. Data analytics, a significant part of data science helps address these challenges and facilitates claims processing, pricing strategies, and fraud management. Let’s look at some of these use cases in detail:

Risk assessment

Identifying risks and taking steps to mitigate them is a critical responsibility of any insurer, which becomes even more important when underwriting policies. There are cases where policies are written for storefronts and warehouses. In such cases, fixed data sets which include political, geographic, and economic data are analyzed. Furthermore, personal data of customers often prove to be an asset which when efficiently used helps analyze both types of risk— pure and speculative. The matrix model needs mention here which supports accurate risk assessment and facilitates meaningful risk discussions. In this 3×3 model, the event consequences lie along one axis and the event frequency on the other. Each group in the matrix represents risks at some level. The model is dependent on algorithms that detect and combine the data revolving around individual risks which vary in nature, effect, and character. This helps list each risk, rate its likelihood and severity and identify the highest-priority risks which may require immediate remediation.

Claims management

Apart from risk management, claims assessment is another important area where analytics plays a critical role. It is a given that faster and better insights empower insurers to ascertain and interpret what is going on in the claims process. However, data needs to be combined with analytics expertise to not only improve the speed of identifying hidden correlations but also respond to potential challenges timely. Analytics, in particular, helps prioritize claims, settle straightforward claims, tag the complex claims for further inquiry, and reduce the cycle time. Let’s take a case in point. A particular insurer’s data revealed that the claim cycle is abnormally high only in a specific geography. When data was dissected further, it was discovered that claim time for a particular claim in that geography was almost twice, and this was the case in bodily injury claims. This led to the understanding that by addressing the root cause of the cycle time, the insurer could decrease the average cycle time. The use of analytics led the insurer to specific questions such as the number of adjusters available to address bodily injury claims, idle time among adjusters, and so on which when solved expedited the claim cycle time.

Fraud Management

While we already know by now how analytics contributes to risk assessment and claims management, it also helps detect frauds. Handling frauds manually have always proved costly for insurers, more so when high-value frauds go undetected. Three innovative analytics methods in which frauds are detected include:

  • Social Network Analysis (SNA)

In the case of SNA, both structured and unstructured data are fed into the ETL tool (Extract, Transform and Load tool). The analytics team then uses information across sources and calculates the likelihood of fraud based on several factors. Technologies that are integrated into the predictive modelling process for fraud management include text mining, content categorization, sentiment analysis along with social network analysis. 

  • Predictive analytics

The second method is predictive analytics, where text analytics and sentiment analysis are used to look at Big Data for fraud detection. Often the claim reports span across multiple pages leaving very little room for insurers to detect scams. Here analytics comes to use which not only helps sift through unstructured data but also detect possible frauds. Once the fraudulent claims are spotted, it accelerates the payment process of the legitimate claims leading to a higher number of satisfied customers. 

  • Social Customer Relationship Management (SCRM)

Social CRM or social customer relationship management is the third process in which the insurers link social media to the CRM. Insurers Invest in sophisticated analytics tools to verify the validity of claims and detect fraudulent crimes. This enables greater transparency with genuine customers and increases the faith they show in the insurance organization. Introducing social customer relationship management leads to a shift to a customer-centric ecosystem which is again beneficial to the business in the long run.

Final thoughts

As we reach the cusp of a massive data science revolution, the near future will witness insurance companies only increasingly using data science analytics. And why not when it helps optimize marketing strategies, enhance income, improve business and reduce costs. It will not be surprising if the use of data science in insurance makes huge leaps in the near future, and insurance businesses leverage it to the fullest. We may rather believe in this optimistic trend and wait and see what awaits us at the end of the road.

How Artificial Intelligence is Fast Transforming Aviation?

According to forecasts, AI investments in the global
aviation market are expected to reach a  $2,222.5 million by 2025, demonstrating a CAGR
of 46.65% in the
period between 2018 and 2025. This trend is fast disrupting how the players
approach their data streams, operations, and customer-centricity goals. Interestingly,
we can already witness certain AI-specific use cases in aviation which include
baggage-check in, client inquiries, plane fuel enhancement, and facial
recognition. You may wonder why the players are taking resort to AI for facial
recognition — for faster boarding. Kiosks for facial recognition cut down on
boarding time, resulting in greater convenience and a higher number of
satisfied travelers.

Why do airlines need
to board the AI bandwagon?

However, achieving high customer satisfaction
levels may become more challenging as airline traffic doubles up to almost
eight billion in
the next two decades. Most airlines may struggle to keep pace with consumer
demand. Reasons for the robust demand have been identified as strong economic
growth, a growing middle class, and increased spending on services. Here too AI
has a crucial role to play.

With the high proliferation of data, as the
airlines collect a vast amount of customer preference info, scheduling history,
and employee information, developing practical AI solutions becomes easier.
Airlines use the data sets and advanced AI-algorithms to offer superior personalized
services to a larger consumer base and improve employee incentives, ensuring
that services do not deteriorate despite rising consumer demand.

Changes we can witness
already

While the traffic is all set to
increase, the competition too will heighten. In such a scenario, AI is being
leveraged by the aviation industry for autopilot systems, which assists the
pilot in controlling the plane better. They reduce repetitive tasks of the
pilots, manage turbulence and help in weather forecasts determining whether it
is safe to fly. In case of adverse weather, AI engines suggest alternate
routes.

Although auto-pilot remains the most
successful use case of AI, the use of the technology stretches beyond
auto-pilot in multiple ways:

  • AI disentangles the procedure related to
    baggage screening in a few airports. Osaka Airport has introduced an AI
    platform—Syntech One 200, which screens the baggage for multiple passenger
    lanes saving on resources and time. This AI software can also detect knives,
    threats, and weapons both ensuring and bolstering security.
  • Also, AI-based virtual assistants are being
    used to improve client services. These assistants answer basic queries allowing
    the customer service personnel adequate time to take care of more pressing issues.
    A leading airline is already using Alexa to answer common customer queries such
    as the status of a trip, check-in request, and whether wi-fi is accessible on a
    new flight.
  • AI helps detect booking-related problems. It
    analyses past flyer information, historical data and monitors the weather to
    predict which passengers will not show up or may swap to another flight. It
    provides the ground staff with up-to-date information on the number of people
    likely to board the flight.

Use
of AI in fleet and operations management

Besides the above use cases, AI also
finds use in enhancing the fleet and operations management. The Alibaba Group
recently announced that it has provided AI-based solutions to the Beijing
airport where they are using it to help pilots find parking easily. This
AI-system gauges congestion, facilitates flight route optimization, and
analyses real-time data to detect delays.

It would surprise you that besides
flight route optimization, fraud detection in aviation is another area where AI
finds use. Advanced algorithms analyze a customer’s flight and purchase pattern
to detect fraudulent credit card transactions, which saves airlines millions of
dollars, eventually.

Enhancing customer
service for retention

While fleet and operations management
is being well tackled, AI engines also contribute majorly to customer service. It
is for this reason that 52% of
airlines have thought of implementing AI initiatives in their
customer service operations in the next five years. The AI engines adopt
behavior tracking techniques and keep track of purchase history to make
personalized offerings. Intelligent systems carry out sentiment analysis,
evaluating real-time customer reactions to provide actionable insights on how
to improve pre-flight, in-flight, and post-flight service.

Monitoring
the “health” of aircraft

Another interesting
area where AI finds use is predictive maintenance. Airlines are fast embracing
AI to monitor the “health” of aircraft. AI systems predict when a part requires
maintenance, ensuring repairs without delay. Data is fast analyzed, which
enables timely preventive actions. They use AI systems on valves, generators and
brakes which extend the lives of the parts and minimize disruptions.

In case you are
wondering how predictive maintenance is carried out—AI systems make use of Natural
Language Processing (NLP) to scan maintenance logs, predict failure, and
recommend fixes. This saves the maintenance costs of airlines and also keeps
passengers safe. Interestingly, this upgrade to AI has led to a significant
reduction in flight delays owing to malfunctions.

Making the skies friendlier in future

Till now, we
discussed how AI is bringing about changes, but what lies in store for the
future? AI promises to ensure a reduction in the number of bags lost in a
unique way. Using AI, intelligent machines will enable bags to be autonomously
managed from the moment a passenger checks-in to when it arrives at the
destination — all without human intervention. 

Besides, interesting
research is underway in using Fitbit biometric data inflight to offer better
customer care during the journey based on body temperature and heart rate. And, what
more could we ask for, convenience, comfort, and now inflight care based on
biometrics.

As airlines embrace AI, it is us the
end customers who will undeniably stand to benefit the most.  

How Machine Learning is a Game Changer in Manufacturing?

In the prevailing times, the manufacturing process for companies can be cost-intensive and time-consuming if they are not equipped with suitable technologies. One critical and emerging technology which is all set to revolutionize manufacturing is machine learning (ML). From improving productivity to bringing about efficiency gains, ML algorithms have been seen to transform each step of manufacturing. These algorithms reduce the length and cost of manufacturing processes and harness the potential of varied datasets to make smart manufacturing a reality. They collect data from the manufacturing environment and use sophisticated and advanced computing power to arrive at actionable insights.

According to the predictions of a leading market intelligence provider, the smart manufacturing market will be worth $320 billion by 2020 and grow at a projected CAGR of 12.5%. Now, what is smart manufacturing? As most of us are aware that Industry 4.0 is underway, there is a shift from traditional manufacturing to smart manufacturing where machines are trained to understand processes and robots assemble a product with high levels of precision. This primer provides us with insights into how ML finds significant use in modern-day manufacturing.  

ML methods used in manufacturing

The three types of ML methods used in manufacturing include supervised, unsupervised and reinforcement learning. Let’s understand each of the three with use cases:

Supervised learning

Supervised learning is an ML technique where large training data sets are applied to the systems. The system is trained with data that has already been categorized into one or more groups, and the groups here are referred to as labels. The system makes use of the algorithms to understand the data structure and then classifies the output data under the right category. Let’s take the Statistical Learning Theory (SLT) as a case in point. Bayesian Networks are the most well-known application of SLTs. An American multinational information technology company in Corvallis, Oregon manufactures numerous precision products at immensely high speed. One essential component in the production of these products was the alignment of the cap to the base. Hence, the positional accuracy of the cap was critical. The company developed a prototype to monitor the performance of the alignment process in real-time. For the monitoring model, they used Bayesian networks which led to positive results. 

Unsupervised learning

In the case of unsupervised learning, the evaluation is not dependent on pre-classified labeled data. Instead, the algorithms detect patterns in the unclassified data and these groups of related observations are called clusters. The final objective of unsupervised learning is to decipher unknown but evident relationships between the clusters. Clustering is a common example of unsupervised learning which is being adopted by companies such as Acta-Mobilier which provides high quality lacquered furniture. The company was grappling with rework rates almost as high as 30%. This resulted in a significant gap between the increasing customer requirements and the speed of implementing new processes. The company implemented clustering algorithms to address this production logistic problem which eventually reduced the rework rates.

Reinforcement learning

In addition to supervised and unsupervised learning, manufacturers also adopt reinforcement learning. Take for instance, a world leader in industrial robotics which integrates deep reinforcement learning into its industrial robots. Towards the end of 2016, an American tech major collaborated with this leader in factory automation to use the latter’s AI chips in the Smart Factory for efficiency gains.

While the use of ML algorithms will only be on the rise, there are some key areas where the transformation is already evident: 

In supply chain management

It is interesting to note that according to a Gartner study, by 2020, 95% of supply chain vendors will depend on supervised and unsupervised learning. ML algorithms provide supply chain operators with significant insights into how to improve the supply chain performance, anticipate the anomalies in logistics, and identify areas that can be automated leading to scale advantages. ML is revolutionizing supply chain management in four key ways:

  1. Reducing logistics costs
  2. Detecting inconsistent supplier quality levels
  3. Reducing risk and potential for fraud and;
  4. Providing end-to-end supply chain visibility

In predictive maintenance

Predictive maintenance is another critical area in manufacturing which ML is fast transforming. One of the Big Four accounting firms anticipates that implementation of ML and analytics by manufacturers will rise by 38% in the next five years only to enhance predictive maintenance. 

ML specifically helps in:

  • Remaining useful life (RUL) prediction: In this case, the algorithms give insights into when a machine will fail so that maintenance can be scheduled well in advance. Regression is used to calculate the RUL of an asset.
  • Anomalous behavior: Anomalies are detected through time series analysis.
  • Failure diagnosis: ML algorithms are used to diagnose likely failures beforehand. The algorithms also recommend mitigation and maintenance actions after the failure.

The use of ML in predictive maintenance can be understood better with a real-life use case. A manufacturer of industrial equipment in the beverage industry integrated the existing machines with an ML-based monitoring and prediction system. This reduced problems of inefficiency and reactive customer service. It also helped optimize the equipment maintenance schedules which took place at defined time intervals previously and did not cater to maintenance needs in real-time. Eventually, the use of the ML-based prediction system led to business scalability and optimized the cost structure. 

In quality control

Lastly, quality control is another function where ML finds significant use. According to a prediction by Forbes quality testing done with machine learning can boost detection rates by 90%. Machine learning allows algorithms to inspect products and identify flaws. These ML-based algorithms make use of samples in the training set and create a library of the possible defects. Further, they learn from samples to distinguish the faultless from the flawed. 

As ML heralds a new age of predictive manufacturing, optimized supply chains, and enhanced quality control, it will also mean a quantum leap to make Industry 4.0 a reality. What’s more, machine learning being a self-learning system will only lead to better outcomes resulting in improved production throughput at superior quality levels. 

The Fast Evolution of Artificial Intelligence—how will you win?

Artificial Intelligence (AI) is fast making a foray into our personal lives. Take for instance the portrait mode effect while we capture images through smartphones, the social media feeds in our timeline, (it is all curated by AI) when we use Google Maps for navigating or call an Uber. In the case of Uber rides, it is AI that decides the price and car that match our request. Without a doubt, AI is the most transformative technology today. 

Enterprises and AI

As any new technology is meant to improve productivity, businesses especially cannot afford to overlook the potential of AI. Gartner predicts that AI will rank among the top five investments for more than 30% of CIOs by 2020. Going by the McKinsey Global Institute Insight, by 2030, 70% of companies would have adopted at least one type of AI technology. To realize the true potential of AI business will need to take several actions sooner rather than later. By leveraging the first-mover advantage they can take productivity and efficiency to new heights and outpace their rivals. 

In the real scenario, while developments in AI are business-ready there is a strong disconnect between the enthusiasm around AI and its actual deployment in applications. The majority of the AI projects lie in the pilot stage. To catapult to the launch phase companies should look at initiating an assessment of their AI-readiness and identify quick-win opportunities which lead to prompt financial benefits, justifying the deployment costs of AI. 

The proliferation of AI across sectors

To succeed in AI efforts businesses need to unify their AI efforts with the greater business transformation efforts. In the insurance sector, a major health insurance provider is already using AI methods to customize benefit design which has led to a 180% growth in their new member acquisition rate. What lies ahead in aviation? AI-based virtual assistants are helping pilots to increase their efficiency and productivity. A major American airline is using Amazon Alexa to answer common passenger questions. By using AI in predictive maintenance analytics aviation players are trying to predict how and by when aircraft maintenance schedules ought to be completed. 

The healthcare and life sciences sector is also not behind when it comes to the adoption of AI. Healthcare is one sector that is ripe for disruption through AI. Very soon healthcare players will use the power of a popular AI-platform to empower clinicians helping them decide on the most appropriate therapy and also provide decision support with evidence-based data sets. Interestingly, AI is all set to become the cornerstone of precision diagnosis. Without replacing doctors, it will only complement them, ensuring they stay on the top of their game.

Returns from deploying AI

Deploying AI technologies alone is not sufficient, it is equally critical that organizations demand ROI. AI unlocks new revenue streams increasing the top-line figures, it also brings about efficiencies in operations improving the bottom-line. We can use AI to cut down on repetitive tasks, develop new products and bypass the invention process ourselves. AI intervention will only improve customer experience initiatives and marketing and sales functions leading to top-line growth. 

While AI has the potential to boost profitability by an average of 38% by 2035 across 16 industries, it has its limitations. Despite the strong potential, AI systems need to be continually trained and recalibrated. We have reached a stage that AI will not be restricted to performing tasks. Instead, we need to raise it to act as a responsible member of society. According to experts in the next few years, AI will work along with humans in collaboration as their co-workers. We can no longer treat AI as a mere software but rather embrace the concept of citizen AI. 

Need for responsible AI

With great power comes responsibility, and so is the case with AI. Most of the fears and concerns related to AI revolve around loss of privacy, significant biases in decision-making, and gradually losing control over automated systems. Responsible AI ensures transparent, ethical, and accountable usage of AI and guards critical decisions against biased algorithms. The need of the hour is transparent AI which allows one to check whether AI models are well-tested. In organizations, one could ascertain whether the AI models are aligned to the core principles for larger business goals. 

While AI proliferates across most sectors, the skepticism around it will decrease with the right approach and timely decision-making. 

About TCG Digital

TCG Digital accelerates enterprise digital transformation with hyper-contemporary technologies, advanced analytics, data strategy, application development, mobility, RPA, cybersecurity, and IoT to deliver Velocity to Value . 
We are the flagship technology consulting and solutions company of “The Chatterjee Group”, a multi-billion dollar portfolio of corporations. This affiliation empowers us with access to global talent, subject matter expertise, and a ‘1,000 digital minds’. 
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