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The brick-and-mortar retail industry is not in a good moment right now. Much of the turmoil in this industry comes from the fact that consumers are seeking a richer and indulging retail experience that reduces friction – much like what they have now become used to as they shop online. Consumers also expect traditional retailers to capture the essence of their individuality – who they are, what they like, and how they prefer to consume information. Retailers need to understand and align themselves with the expectations of the consumers in order to increase profitability and customer loyalty. They need to not only be aware, but also go full throttle for adopting technologies such as AI for influencing and revolutionising customer behaviour.
Retailers need to explore use cases pertaining to exponential technologies to address the disruption that their industry is going through. They need to catch up with how recommendation engines are redefining customer experience, how retail business value chain transformation is shaping up, and how AI can enhance the supply chain aspects of their business. And as I mentioned, awareness is simply not enough – they need to assess and adopt these technologies on a war footing to survive in the world we live in today.
The data-powered disruption of retail
Data in the retail industry is increasing exponentially in terms of volume, variety, velocity – and more importantly – value with every passing year. Smarter retailers are increasingly aware of how every interaction between the business and customers holds the potential to increase customer loyalty and drive additional customer revenue. Retailers that adopt AI today have the potential to raise their operating margins by as much as 60 percent.
But just having the data and building reports that summarise customer behaviour at an aggregate level are not enough. Insights are important, no doubt, but retailers desperately need systems that can make actionable decisions from the data. Insights into average user behaviour is simply too broad. Retailers need to now create a meaningful dialogue with each individual customer that honours their shopper’s preferred level and mode of engagement. This requires more than summarised reports. It requires technologies powered by AI – the ability to minutely understand every customer individually and take actions that each individual customer expects.
We now live in a time where data-driven decisions are extremely pervasive and useful. So much so that the worlds of ecommerce and traditional commerce are seamlessly merging. Every company is now omni-channel. Whether you think of Walmart buying Flipkart to boost their online presence or you take Amazon purchasing Whole Foods to bolster their brick-and-mortar presence. It is not about the web, the app, or the store – it is about having all of those. With the quantum of data available, we’ve seen an extraordinary few years in the retail industry – in the sense that data is actively deconstructing and rebuilding what retail will look like tomorrow. Traditional incumbents need to pay heed to the warning signs signalled by their defunct counterparts and aggressively embrace the data-driven disruption of retail.
AI transforming retail
Predictive analytics has been used in retail for several years now. However, in the last few years – with advances in technology and artificial intelligence – we are seeing the high speed, scale, and value that predictive analytics can deliver. The AI-enabled world of retail helps business transition into a world where consumers are always connected, more mobile, more social, and have choices about where they shop.
Deep learning in commerce
The retail industry is one with a lot of benefit to be gained from deep learning, in part because it’s such a data-rich industry and because there is some momentum around AI gathering already. Further a lot of the AI techniques enjoying success in other applications across industries powered by deep learning are well positioned to make serious impact on retail, streamlining processes, and transforming customer experience into something that largely resembles the experience customers get when they visit online portals.
Deep learning has been the fuel for much of the recent success in applied AI, so it is not surprising that some of the first attempts at augmenting the offline shopping experience have been making use of the power of deep learning in classifying images. If you look at something like Shelf analytics to seek out merchandising effectiveness, you can see the beginnings of how deep learning fits snugly in a retail context.
Now with minimal effort, retailers that can leverage automated AI capabilities will see a strong rise in customer engagement and sales. The best part is – this can be accomplished by data that is already available to them and captured in their enterprise systems. There’s more. The algorithms required for powering these systems, such as collaborative filtering, are relatively simple to deploy and efficient to run.
Intelligent product searches
Another great use case for retailers is leveraging AI for powering intelligent product searches. By using AI, customers can take pictures of things that they see in the real world, or even in an ad, and then locate a retailer who has that item in stock. This can easily serve as the start of a shopping experience. Typically, consumers do often see something that they like, but do not know the name of the item, brand or where they can source it from.
But taking photos is not the only modality for shopping, and there are other areas in the shopping experience where AI can play a part. In online commerce retail, for instance, customers often know what they are looking for but do not know the exact search terms or must scroll through multiple pages of inventory to find it. Deep learning can be of help here as well. Auto-encoding features of images in an inventory based on similarities and differences brings about a rich model of what is available in the inventory, and the model is surprisingly close to how we, as humans, perceive shoppable items. The model alone, of course, is not enough: We need a way to understand a shopper’s preferences as they interact with the inventory.
Speed and communication in real time
Just a few years ago, retailers used to take weeks to analyse customer data and make product offers. Machine learning and AI are changing the game by streaming live data and curating products in real time – based on their understanding of each customer. This significant drop in the amount of time taken between receiving data and powering an intelligent decision is made possible by AI and helps improve the uptake of products from retailers. For instance, by using mobile geo-location capabilities retailers can now offer deals or promotions when customers walk into or near the store (not after they’ve paid at the checkout and departed). Mobile push notifications on company apps allow retailers to track engagement the second it happens.
Given this rapid evolution, retailers have many choices on how to use AI in their marketing and sales strategies. Consumers are seeking a richer retail experience that reduces friction while also capturing the essence of who they are, what they like, and how they prefer to consume information. The sooner a retailer understands this and creates the best consumer experience they can, the sooner they will increase profitability and retention rates. I predict that this retail revolution will continue to unfold and expand over the next several years. But as the industry expands one thing is certain: in retail, personalisation and the customer journey are key, regardless of how you get there.
The ‘segment of one’ approach
A generic, aggregative understanding of customer behaviour is no longer enough. Individual segmentation is the next step for retailers looking to create a super-personalised experience for their users.
The worlds of traditional commerce retail and ecommerce retail are rapidly merging. I think ecommerce retail for many years was an interesting trend, but it was on the side, largely, of what was happening in retail. Today ecommerce retail is less an ancillary part of retail and more about the way business is now done. Online and offline experiences are fast coming together and without an omni-channel experience, it will be extremely difficult for a retailer to survive. That said, I do not doubt there is a future for brick-and-mortar retail, but there will need to be a transformation of retail real estate. Stores are going to become as much distribution and fulfilment centres as they are full-fledged shopping experiences. And they will need to be highly technology enabled.
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Artificial intelligence (AI) will empower banking organisations to completely redefine how they operate, establish innovative products and services, and most importantly impact customer experience interventions. In this second machine age, banks will find themselves competing with upstart fintech firms leveraging advanced technologies that augment or even replace human workers with sophisticated algorithms. To maintain a sharp competitive edge, banking corporations will need to embrace AI and weave it into their business strategy.
In this post, I will examine the dynamics of AI ecosystems in the banking industry and how it is fast becoming a major disrupter by looking at some of the critical unsolved problems in this area of business. AI’s potential can be looked at through multiple lenses in this sector, particularly its implications and applications across the operating landscape of banking. Let us focus on some of the key artifiicial intelligence technology systems: robotics, computer vision, language, virtual agents, and machine learning (including deep learning) that underlines many recent advances made in this sector.
Banks entering the intelligence age are under intense pressure on multiple fronts. Rapid advances in AI are coming at a time of widespread technological and digital disruption. To manage this impact, many changes are being triggered.
- Leading banks are aggressively hiring Chief AI Officers while investing in AI labs and incubators
- AI-powered banking bots are being used on the customer experience front.
- Intelligent personal investment products are available at scale
- Multiple banks are moving towards custom in-house solutions that leverage sophisticated ontologies, natural language processing, machine learning, pattern recognition, and probabilistic reasoning algorithms to aid skilled employees and robots with complex decisions
Some of the key characteristics shaping this industry include:
- Decision support and advanced algorithms allow the automation of processes that are more cognitive in nature
- Solutions incorporate advanced self-learning capabilities
- Sophisticated cognitive hypothesis generation/advanced predictive analytics
Surge of AI in Banking
Banks today are struggling to reduce costs, meet margins, and exceed customer expectations through personal experience. To enable this, implementing AI is particularly important. And banks have started embracing AI and related technologies worldwide. According to a survey by the National Business Research Institute, over 32 percent of financial institutions use AI through voice recognition and predictive analysis. The dawn of mobile technology, data availability and the explosion of open-source software provides artificial intelligence huge playing field in the banking sector. The changing dynamics of an app-driven world is enabling the banking sector to leverage AI and integrate it tightly with the business imperatives.
AI in Banking Customer Services
Automated AI-powered customer service is gaining strong traction. Using data gathered from users’ devices, AI-based relay information using machine learning by redirecting users to the source. AI-related features also enable services, offers, and insights in line with the user’s behaviour and requirements. The cognitive machine is trained to advise and communicate by analysing users’ data. Online wealth management services and other services are powered by integrating AI advancements to the app by capturing relevant data.
The tested example of answering simple questions that the users have and redirecting them to the relevant resource has proven successful. Routine and basic operations i.e. opening or closing the account, transfer of funds, can be enabled with the help of chatbots.
Fraud and risk management
Online fraud is an area of massive concern for businesses as they digitise at scale. Risk management at internet scale cannot be managed manually or by using legacy information systems. Most banks are looking to deploy machine or deep learning and predictive analytics to examine all transactions in real-time. Machine learning can play an extremely critical role in the bank’s middle office.
The primary uses include mitigating fraud by scanning transactions for suspicious patterns in real-time, measuring clients for creditworthiness, and enabling risk analysts with right recommendations for curbing risk.
Trading and Securities
Robotic Process Automation (RPA) plays a key role in security settlement through reconciliation and validation of information in the back office with trades enabled in the front office. Artificial intelligence facilitates the overall process of trade enrichment, confirmation and settlement.
Lending is a critical business for banks, which directly and indirectly touches almost all parts of the economy. At its core, lending can be seen as a big data problem. This makes it an effective case for machine learning. One of the critical aspects is the validation of creditworthiness of individuals or businesses seeking such loans. The more data available about the borrower, the better you can assess their creditworthiness.
Usually, the amount of a loan is tied to assessments based on the value of the collateral and taking future inflation into consideration. The potential of AI is that it can analyse all of these data sources together to generate a coherent decision. In fact, banks today look at creditworthiness as one of their everyday applications of AI.
Banks are increasingly relying on machine learning to make smarter, real-time investment decisions on behalf of their investors and clients.
These algorithms can progress across distinct ways. Data becomes an integral part of their decision-making tree, this enables them to experiment with different strategies on the fly to broaden their focus to consider a more diverse range of assets.
Banks are focussed to leverage an AI and machine learning-based technology platforms that make customised portfolio profiles of customers based on their investment limits, patterns and preferences.
Banking and artificial intelligence are at a vantage position to unleash the next wave of digital disruption. A user-friendly AI ecosystem has the potential for creating value for the banking industry, but the desire to adopt such solutions across all spectrums can become roadblocks. Some of the issues can be long implementation timelines, limitations in the budgeting process, reliance on legacy platforms, and the overall complexity of a bank’s technology environment.
To overcome the above challenges of introducing and building an AI-enabled environment. Banks need to enable incremental adoption methods and technologies. The critical part is ensuring that the transition allows them to overcome the change management/behavioural issues. The secret sauce of successful deployment is to ensure a seamless fit into the existing technology architecture landscape, making an effective AI enterprise environment.
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The year 2018 will be remembered as the year that artificial intelligence stopped being on the periphery of business and entered the mainstream realm. With increasing awareness and capability of AI among the numerous stakeholders, including tech buyers, vendors, investors, governments, and academia, I expect AI will go beyond just tinkering and experiments and will become the mainstay in the business arena.
With an increasing percentage of these stakeholders professing their commitment to leveraging this technology within their organisations, AI has arrived on the world scene. We are sure to see transformative business value being derived through AI in the coming years. As we come to the close of 2018, let us gaze into the crystal ball to see what 2019 will hold for this game-changing technology:
The rise of topical business applications
Currently, we have a lot of general purposes AI frameworks such as Machine Learning and Deep Learning that are being used by corporations for a plethora of use cases. We will see a further evolution of such technology into niche, topical business applications as the demand for pre-packaged software with lower time-to-value increases. We will see a migration from the traditional AI services paradigm to very specific out-of-the-box applications geared to serve particular use cases. Topical AI applications in this space that serve such use cases will be monumentally useful for furthering the growth of AI, rather than bespoke services that require longer development cycles and may cause bottlenecks that enterprises cannot afford.
The merger of AI, Blockchain, cloud, and IoT
Could a future software stack comprise AI, Blockchain, and IoT running on the cloud? It is not too hard to imagine how these exponential technologies can come together to create great value. IoT devices will largely be the interface with which consumers and other societal stakeholders will interact. Voice-enabled and always connected devices – such as Google Home and Amazon’s Alexa – will augment the customer experience and eventually become the primary point of contact with businesses. AI frameworks such as Speech Recognition and Natural Language Processing will be the translation layer between the sensor on one end and the deciphering technology on the other end. Blockchain-like decentralised databases will act as the immutable core for managing contracts, consumer requests, and transactions between various parties in the supply chain. The cloud will be the mainstay for running these applications, requiring huge computational resources and very high availability.
Focus on business value rather than cost efficiency
2019 will finally be the year that majority of the executive and boardroom conversations around AI will move from reducing headcount and cost efficiency to concrete business value. In 2019, more and more businesses will realise that focusing on AI solutions that reduce cost is a criminal waste of wonderful technology. Ai can be used to identify revenues lost, plug leakages in customer experience, and entirely reinvent business models. I am certain that businesses that focus only on the cost aspect will stand to lose ground to competitors that have a more cogent strategy to take the full advantage of the range of benefits that AI offers.
Development of AI-optimised hardware and software
Ubiquitous and all-pervasive availability of AI will require paradigm shifts in the design of the hardware and software that runs it. In 2019, we will see an explosion of hardware and software designed and optimised to run artificial intelligence. With the increasing size and scale of data fueling AI applications and even more complex algorithms, we will see a huge demand for specialised chipsets that can effectively run AI applications with minimal latency. Investors are showing heavy interest in companies developing GPUs, NPUs, and the like – as demonstrated by Chinese startup Cambricon, which stands valued at a whopping $2.5 billion since its last round of funding this year. End-user hardware such as smart assistants and wearables will also see a massive increase in demand. Traditional software paradigms will also continue to be challenged. Today’s novel frameworks such as TensorFlow will become de rigueur. Architectural components such as edge computing will ensure that higher processing power is more locally available to AI-powered applications.
‘Citizen AI’ to be the new normal
One of the reasons we saw widespread adoption of analytics and data-driven decision-making is because we built applications that democratised the power of data. No longer was data stuck in a remote silo, accessible only to the most sophisticated techies. With tools and technology frameworks we brought data into the mainstream and made it the cornerstone of how enterprises plan and execute strategy. According to Gartner, the number of citizen data scientists will grow five times faster than the number of expert data scientists. In 2019, I expect Citizen AI to gain traction as the new normal. Highly advanced AI-powered development environments that automate functional and non-functional aspects of applications will bring forward to a new class of “citizen application developers”, allowing executives to use AI-driven tools to automatically generate new solutions.
Policies to foster and govern AI
Following China’s blockbuster announcement of a National AI Policy in 2017, other countries have rushed to share their take on policy level interventions around AI. I expect to see more countries come forward with their versions of a policy framework for AI – from overarching vision to allaying concerns around ethical breaches. At the same time, countries will also be asked to temper their enthusiasm of widespread data proliferation by releasing their own versions of GDPR-like regulations. For enabling an ecosystem where data can be used to enrich AI algorithms, the public will need to be convinced that this is for the overall good, and they have nothing to fear from potential data misuse and theft.
Speech Recognition will revolutionise NLP
In the last few years, frameworks for Natural Language Understanding (NLU) and Natural Language Generation (NLG) have made huge strides. NLP algorithms are now able to decipher emotions, sarcasm, and figures of speech. Going forward, voice assistants will use data from voice and combine that with deep learning to associate the words spoken with emotions, enriching the overall library that processes speech and text. This will be a revolutionary step forward for fields such as customer service and customer experience where many bots have typically struggled with the customer’s tone of voice and intonation.
The growth of explainable AI
And finally, with numerous decisions powered by AI – and specifically unsupervised learning models – we will see enterprises demand “explainable” AI. In simplified terms, explainable AI helps executives “look under the hood” to understand the “what” and “why” of the decisions and recommendations made by artificial intelligence. Development of explainable AI will be predicated on the need for increased transparency and trust. Explainable AI will be essential to ensure that there is some level of transparency (and potentially, learning) that is gleaned from unsupervised systems.
Convergence of AI and analytics
This is a trend that is a logical consequence of the decisive power of data in business today. In 2019, we will see a merger of analytics and AI – as the one-stop for uncovering and understanding insights from data. With advancements in AI seen so far, the algorithms are more than capable of taking up tasks that involve complex insight generation from multi-source, voluminous data. This convergence of AI and analytics will lead to automation that will improve the speed and accuracy of the decisions that power business planning and strategy. AI-powered forecasting will help deliver faster decisions, with minimal human interventions and create higher cost savings for the business.
Focus on physical and cybersecurity paradigms
Two of the domains ripe for an AI transformation are the fields of physical and cybersecurity. As intrusions into physical and virtual environments become commonplace and threats become hugely pervasive, AI will be a massive boost to how we secure these environments. Advances in fields such as ML-powered anomaly detection will drastically reduce the time required to surface potential intrusions into secure environments. This will enable organisations to better protect user data. When combined with Blockchain, AI will give cybersecurity a huge boost through decentralised, traceable databases containing valuable client and strategic information. On the physical security side, Computer Vision is rapidly gaining currency in the fields of physical intruder detection. Surveillance cameras, originally manned by security guards, will soon be replaced by AI-powered systems that will be able to react faster and more proactively to intruders that pose a threat to physical premises. When you combine that with face recognition, working with a database of known offenders, we will see a quantum drop in the time required to adjudicate and address cases of theft and unauthorised entry by law enforcement agencies.
In summary, the broad directions that I predict AI will take include interventions to make it more embedded, responsible, and explainable; convergence with other exponential technologies such as cloud, Blockchain, and IoT; cybersecurity; a greater proliferation and development of use cases; and great strides in the technology and its supporting infrastructure. Enterprises would do well to adopt this revolutionary technology and ensure a strong availability of talent to conceptualise, develop, and unleash value from AI applications.
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Why AI in construction?
Of all the game-changing innovations driven by technology and artificial intelligence in the world today, the potential of one key sector remains untapped – the construction industry.
According to McKinsey, the engineering and construction sector globally is valued to be worth $10 tn per year. While that is a respectable size, the construction industry overall has largely been slow in the uptake of inventions in the technology arena. In fact, several construction business houses in India tend to be family-owned and extremely traditionally-run, and have tremendous inertia in embracing new age technologies.
However, the past few years is seeing a change in the way construction firms operate. With well-funded global start-ups such as WeWork entering the fray with an AI and analytics forward approach to real estate development; industry incumbents need to up their game in order to stay relevant. While McKinsey expects the permeation of AI in the construction industry to be modest right now, it does represent an opportunity for early adopters to catch the bull by the horns and build a sizeable competitive advantage. Those from this industry that have a ponderous and slow uptake of new technology will surely be eaten up by their competitors. Through this article, we explore some artificial intelligence interventions that can be transformative for the construction and real estate industry at large.
Image recognition for managing risk, safety and quality
The construction industry would do well to adopt these techniques and apply them to manage risk and worker safety. Working conditions in the construction industry for labourers tend to be managed mediocrely at present. We hear of numerous cases of mortality and severe injuries where workers do not follow established safety procedures. Other cases also include unsafe working environments where certain infrastructure in overall construction projects are unsafe for human work.
Construction companies could employ drones to capture images and videos of their construction sites on a continuous basis. By applying deep learning and other AI techniques, firms would be able to identify unsafe workplace behaviour as well as unsafe working environments and run training interventions to improve the safety quotient of their workplaces.
Continuous design optimisation
Construction activity has largely been seen as a waterfall-like process where all the designs, construction materials and their feasibility are evaluated at the start of the project. While this is undoubtedly a watertight approach to construction, it does cause delays in planning, leading to lost revenue opportunity in the near term.
Today, with data readily available for analysis, AI can help continuously optimise the design of each project. A recommender system-like approach would help contractors and engineers identify the right design as well as the materials required to execute it. Additionally, AI-powered technology could also help recommend architectural finishes based on the proposed design – thus enabling construction firms to finalise the design and material requirements early in the schedule, and finish construction faster.
Increasing talent retention and development
The construction sector is remarkably disorganised and heavily relies on contract labour for executing a project. While minor, the cost and time involved in fulfilling positions left by ex-labourers and training new entrants really adds up and reduces the overall efficiency in project management. In India, contract labour can often also be seasonal, with numerous workers migrating to their hometowns in droves leading to longer gestation periods for projects.
AI has been applied to talent retention and talent development use cases in multiple industries, and the same can be applied to the construction industry with relative ease. With unsupervised machine learning algorithms, contractors and their parent companies will be able to forecast talent shortage accurately, and plan to backfill labour resources efficiently. AI can also enable improved labour retention strategies by recommending best options for ensuring improved talent retention and availability.
Project schedule optimisation
Construction projects are typically long drawn with a sizeable period elapsing between envisioning the project to having it commercially ready. In this period, we often see many niggles with respect to the project schedule. Overuse of materials, time-consuming nature of restocking, people availability issues – all these can throw the overall project plan into disarray.
Preventive maintenance through AI
Maintenance in the construction industry happens largely at two levels. Firstly, it is the maintenance of a partially and incrementally developing property. The second is when the builder organisation is responsible for the continuing maintenance after it has been leased out to tenants. At both levels, maintenance can be a hugely cumbersome and time-consuming activity, albeit critical, that the construction company must perform in order for operations to move smoothly.
We live in a world of sufficiently advanced technology and AI can complement human effort in the process of preventive maintenance. By using sensors and cameras as the data capture layer, and applying machine learning algorithms over the data, facility managers can monitor their property with greater ease and identify guided interventions on where maintenance activity is required. Using this data can be doubly productive as it will provide the system inputs on when routine maintenance activity for all the working components of a modern property are required, and schedule accordingly.
A technology-driven paradigm shift is fast coming for the construction industry. As things stand right now, the industry employs close to 7 percent of the global labour workforce. The strong uptake of infrastructure projects notwithstanding, the sector has grown only 1 percent per year for the past decades – with a flatlining per worker productivity, incumbents would do well to embrace the wave of Artificial Intelligence to power their next phase of growth. Using AI techniques, engineering and construction industry giants would be able to accelerate productivity, increase business efficiency and bring a much-needed technology facelift to the industry.