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Ness Appoints New President and Global Chief Delivery Officer

TEANECK, NJ – November 29, 2017 – Ness Digital Engineering has appointed technology veteran Vinay Rajadhyaksha to President and Global Chief Delivery Officer. With more than 30 years of global IT experience, Rajadhyaksha will help the company execute its growth plans by further scaling Ness’ delivery capabilities across its globally-distributed, Agile teams.

“Vinay brings rich experience in managing practice-based delivery teams in a high-growth, global model,” said Paul Lombardo, Ness Digital Engineering CEO. “He’s the perfect fit for our organization as we look to deepen our investments in expertise, intellectual property development and partnerships in areas that our customers find valuable.”

Rajadhyaksha previously served as Head of Global People Supply Chain and Chief Delivery Officer (CDO) at L&T InfoTech. He also held CDO positions at Mastek and Mphasis. Rajadhyaksha will work out of Ness’ office in Mumbai, India, where he resides with his family.

“I’m excited to help Ness expand its delivery structure to further support and enrich the digital transformation journey for its customers,” said Rajadhyaksha. “And I look forward to working with the team to extend our digital engineering capabilities in both existing and new practice areas, supported by our culture where engineers have tremendous opportunity for growth and the freedom to innovate.”

About Ness Digital Engineering
Ness Digital Engineering designs and builds digital platforms and software that help organizations engage customers, differentiate their brands, and drive revenue growth. Our customer experience designers, software engineers and data experts partner with clients to develop roadmaps that identify ongoing opportunities to increase the value of their digital products and services. Through agile development of minimum viable products (MVPs), our clients can test new ideas in the market and continually adapt to changing business conditions—giving our clients the leverage to lead market disruption in their industries and compete more effectively to drive revenue growth. For more information, visit

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Ness Digital Engineering
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Amy Legere

How Digital Worlds Are Colliding – to Our Benefit

We have reached a point where technology is demonstrably starting to fulfill its original intent to make our lives easier. There are countless examples of this today, many delivered through mobile platforms. The next phase demands that technology is applied to make everything even smarter, with early examples of this being smart vehicles, smart homes, smart energy, and smart living. Our Digital World.

Underpinning this next intelligent wave of digital disruption is the “Internet of Things” (IOT). The current Internet of People (IOP), which we refer to as “the Internet” today, predominantly connects people to a global system of interconnected computer networks, typically via web browser-based systems of engagement. The Internet of Things will consist of countless billions of sensors, devices, machines, and other as yet non-describable “things”, all connected to the Internet, that usually don’t interact with people directly; but which are processing events & data, and taking actions, instrumenting and controlling the entire sphere of human habitation.

IOT is not a new concept. It’s existed for a while in Internet infrastructure components that have an IP address, and there are implementations of mainstream IOT springing up all around us, from IP CCTV through to Internet-connected smart thermostats and smart meters. What was once the realm of science fiction is rapidly becoming reality. We are just at the start of an unprecedented change in how our world works.

In the same way that space exploration and military spending have generated countless innovations that are now part of everyday life, the IOT will be a catalyst for new and different ways to do things that could not have been done before, or even conceived. But for now, several challenges and opportunities are emerging.

An example is the advent of Electric Vehicles (EVs). Recent advances in technology, an ever-decreasing reserve of fossil fuels, the increased efficiency of electric vehicle engines compared to diesel or petrol ones, and concerns around pollution and proposed central government legislation to address this, have initiated an irreversible transition from petrol and diesel-powered vehicles to electrically powered ones. At the same time, vehicle manufacturers have been heavily instrumenting and controlling vehicles with IOT technology, to the point where some vehicles can already drive themselves with little or no driver input, so that over time most vehicles will become autonomous.

Vehicle telematics are already used by some insurance companies to monitor driver behaviour and ensure they stay within the parameters they signed up to when taking out their insurance. Fleet telematics are used to reduce fuel consumption by improving a driver’s usage of a vehicle’s controls. Telematics can be used for other purposes, such as predictive maintenance, and will be a source of data for autonomous driving systems.

In the same way that F1 car engines are instrumented with several thousand events being processed every second, and their drivers wearing sensors in their racing suits to assess their personal health, modern vehicles are starting to assess the health of their subsystems and occupants. The health of the driver won’t be especially relevant in the future, as most vehicles will drive themselves with minimal human input and complete reliance on IOT sensors and control systems.

EVs will be one of the most advanced technology platforms that humans will interact with on a daily basis, but one where IOT technology is taking care of all the complexity and shielding users of these vehicles from having to do anything other than use their time in transit more constructively.

While most people think of EVs in terms of cars and trucks, their application is much wider. EV concepts are already being applied to global shipping, especially large container ships, and will also transform the aviation industry with electric aircraft. In the future, almost everything that moves people or goods will be powered electrically, will run autonomously, and will share telemetry.

Another example is that of Smart Homes, and the IOT technology that enables them. Today, smart meters are being deployed to help electricity utilities and distribution network operators better understand how power is being used in their distribution networks, so they can optimise these in near real-time to maximise efficiency. In parallel, disaggregation algorithms that use Artificial Intelligence and Machine Learning can determine disparate electrical device utilisation within a home at any point in time. There has been a rise in Smart Thermostats that enable people to adjust the temperature in their home to match their needs at any point in time, and from anywhere. The insights from these home energy innovations can inform people how to minimise energy consumption while maintaining a pleasant home environment. The technology to do this (small embedded sensors, low-power short-range wireless protocols, cloud-based data aggregation, intelligent algorithms, next-best-action processing, and visualisation-derived insights) is being adopted quickly, and costs continue to fall as IOT becomes a mainstream mass-market.

While current initiatives have focused more on reducing energy consumption in the home, there are several others that are looking at smart door-locks, smart window openers, smart domestic appliances, and home automation in general. In the home of the future, life will be both more comfortable and more convenient.

Another example is the transition to Smart Energy. Our current way of generating electricity in remote power stations, especially nuclear ones that are situated a safe distance from populous areas, requires long transmission and distribution grids and networks. This is being disrupted by the ever-increasing demand for electrical energy, especially for Electric Vehicles. They require so much power to charge their batteries that existing domestic electrical installations will struggle to cope. To charge a typical EV battery in one hour today would require 100A of current at 220v. Current distribution grids assume that most houses are drawing far less than this, perhaps 4A on average throughout the day, and certainly aren’t scaled to supply 100A to several houses in one locality at the same time. If the rise of EVs continues as expected, and other demands on electrical consumption increase, then even if significantly more power stations were built, entire transmission grids would need massive investment to satisfy that demand. These investments don’t make economic sense.

This precise scenario has led to the concept of smart grids that provide localised power generation, storage, transmission, and consumption. Solar or other renewable energy will be generated locally, often by individual homes or businesses, each of which will have a Home Battery array capable of storing several kWh of energy. Every local power generator (home or business) will store, trade/sell and consume energy within their local area, thereby augmenting existing grid power and minimising the need for updated transmission infrastructure and more power stations. And all of this will be facilitated by IOT sensors and control systems, which will manage everything.

Opportunities exist to bring related IOT systems together, to create “mash-ups”, based on integrating adjacent datasets. For example, by correlating electricity usage with temperature measurements and other metrics from wearables and medical devices, an elderly person’s health can be remotely assessed in real-time – without invading their privacy too deeply. As we all expect to live longer, there’s some comfort in knowing that we won’t ever be left completely alone.

We are moving to a world where smart vehicles, smart homes, smart energy, and even “smart people” – the instrumentation and probably technological augmentation of individuals – will all work together as part of a smart lifestyle to help ensure we exist in as healthy, comfortable and convenient an environment as possible. And already, these different areas are starting to collide positively – both in terms of new initiatives driving technological innovation, and new technologies enabling new ways of doing things. This “network effect” is likely to accelerate the pace of change even more, to the point where it becomes possible to instantiate a new idea or desire within a very short time period.

Humans have struggled over millions of years of evolution to be here today. We progressed relatively gradually until around the 18th century, the start of the industrial revolution in Europe. Information technology and digital disruption have accelerated this pace of change, especially in the 21st century. While technology may be offering us a safe and healthy world, our evolutionary instincts and the way our brains work haven’t changed that much – evolution takes many generations, unless we discover how to evolve ourselves medically. It will be interesting to see how we cope as a species in this new ultra-convenient smart world.

How Banking Institutions Can Improve Personalization for Their Customers

Issuers and banking institutions have access to large amounts of customer data. This includes data on payment habits, investment activities, purchases and so on. And irrespective of how customers chose to interact with different brands – they expect uniform and personalized customer experiences. While retail and hospitality industries are constantly pushing the envelope to improve personalization experiences for their customers, banking institutions have tremendous potential for improving personalization.

In this article for PaymentSource, Sanjay Bhakta, Senior Director of Solutions, Ness Digital Engineering, discusses how the use of right tools and technologies like data, analytical models, and AI and machine learning can help banking institutions improve personalization for their customers.

“Personalization requires a wide spectrum of offerings that must be delivered via a frictionless, seamless, and pleasurable experience to customers. Orchestrating that “experience” and providing the content for the offerings and the operational frameworks to deliver it are distinctly “non-core” activities and present a real challenge outside the comfort of “Business as Usual,” notes Sanjay.

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Successful Issuers Match Retailers In Personalizing Service

In a new article for PaymentsSource, Ness Digital Engineering discusses the tools banks and issuers should utilize to ultimately improve personalization for customers, citing the banking institutions USAA and BBVA as examples. These include leveraging customer segmentation data to better profile customers using data management best practices, outlining better analytical models using AI and machine learning, agreeing on their future capabilities model, and potentially employing a technology partner specializing in digital transformation.

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Internet of Things: An Innovation Enabler

Internet of Things is one of the most disruptive new technologies that has the potential to transform businesses and human lives. While a clear majority of use cases for IoT have been around its ability to drive operational improvements, is it time to think about the next level and how it can be used to deliver breakthrough innovations?

Let us take the example of an automobile. The predominant application of IoT by manufacturers is to monitor critical parts and pinpoint possible failures/incidents requiring maintenance. Or they monitor the usage on a day-to-day basis which can then be used to pinpoint potential changes in design to make the products better. These are point improvements to help improve the operational capability of the products. What if the same operational data is married with data on weather conditions? This might lead to fundamental breakthrough changes in product design. For example, parts of the automobile may perform well under normal weather conditions but experience breakdown in situations where multiple factors come into play like weather and altitude or weather and terrain conditions. We can consider other examples such as soil monitoring for agriculture where monitoring soil condition for moisture, nutrients etc. can be coupled with the overall weather condition to determine the nature of intervention (i.e. reducing or increasing water to crops).

The best example is probably the self-driving car pioneered by Google and followed by auto industry players like Ford. This is a breakthrough innovation encompassing a number of technologies such as Machine Learning in addition to IoT. Another good example pertains to the maintenance of roads -gathering the condition of roads and sending the data to the local municipalities to help fix pot holes or roads in deteriorated condition. This involves the use of IoT in conjunction with geo location technology; something that has never been done before. These success stories highlight how a convergence of technologies (the predominant technology being IoT) is helping deliver breakthrough innovation in products and services.


The availability of cheap computing power courtesy cloud service providers, the ability to gather accurate data from the source seamlessly and the availability of tools to transform this humungous data into actionable insights, provides the perfect ingredients to leverage IoT to deliver breakthrough improvements in products across many industries. This in turn has the power to herald the creation of products and services that customers had never imagined! So, manufacturers can actually create markets where none exist today and open customers’ minds to new possibilities, especially in the context of millennial customers. In the process, they will be seen as innovators; a tag that many industry players would love to wear on their chest.

Ness with its deep expertise in product engineering, data analytics and customer experience is helping clients extract more value out of their digital initiatives. Contact us to learn more about how we can help.

Business and Technology Collaboration is Crucial for Great Customer Experience

Customer experience has always been fundamental to business, but with digital revolution creating new demands for enterprises, there is a renewed thrust on enhancing customer experiences across the numerous customer interaction channels and touch points. This requires adoption of new operating models that drive better collaboration and help break down functional silos to enable greater transparency and seamless interactions. Collaboration between technology and business teams plays a vital role in building great customer experiences.

In an article for CMSWire, Tim Burke, executive vice president, Ness Digital Engineering, shares his thoughts on the importance of digital customer experience in the business strategy, and the value of collaboration in developing enhanced customer journeys. “To break through, companies can start with a customer experience audit, and pick one or two areas to work on that can help them generate insight and momentum without biting off more than they can chew,” notes Tim.

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Software is Eating Manufacturing, Faster and Faster

In an article for Software Magazine, Jean-Paul de Vooght, director of client solutions for Ness Digital Engineering, discusses how a focus on outcomes ultimately gives rise to exciting confrontations of emotional and machine intelligence, specifically for manufacturing. Outcomes solve the duality of end users being able to leverage platform capabilities from the vantage point of device autonomy, while at the same time, retaining a clear perspective over the experience to avoid alienating humans as we move further towards automation. Also, while “journeys” between human and machines reveal events and needs of both alike, outcomes remain centered on human endeavors.

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The Rise of Smart Energy

From time to time particularly large waves of disruption have a significant impact on the world we live in. Although it’s not well known outside the energy and utility industries yet, electrical power systems and infrastructures worldwide are about to be transformed out of all recognition. This is being driven by several factors. One of these is the emerging Internet of Things that will enable new business models and new ways of working – a number of our customers in other industries are already reaping the benefits of similar transformations.  Another is a significant increase in demand for electrical power. We will experience these disruption waves in our daily lives, and they will alter our world. This will create new industries and potentially destroy old ones.

The supply of traditional fossil fuels is declining to the point where soon it won’t be economically viable to build new coal, oil or gas-fired power stations. While nuclear will always remain a non-carbon emitting option, Fukushima showed how things can very quickly go badly wrong in one of the most safety-conscious industries in the world.

Electricity consumption is on the rise due to an ever-increasing number of users and a plethora of electrically-powered devices, some of which will consume significantly high levels of energy; partially offset by efforts to improve the energy efficiency of those devices. At the same time, the world’s population continues to grow, contributing to our global carbon footprint and increasing levels of health-impacting pollution. All of this is driving innovation around more clean energy generation using renewables.

Two areas of significantly increasing demand on electrical energy are:

  • Colder climate dwellers will rely more on electricity for business and domestic heating as supplies of oil and gas diminish. While efforts are ongoing to improve building insulation, there is likely to be a net increase in the demand for electrical heating. Hot climate dwellers already use electricity for cooling, and if global warming continues the demand for electrical cooling will increase.
  • The nascent transition from fossil-fuelled vehicles to electric vehicles has begun. To charge a typical Electric Vehicle (EV) battery in one hour requires around 100A of current at 220v. Existing distribution grids assume that most homes are drawing far less than this, perhaps 3A on average throughout the day, and certainly aren’t scaled to supply 100A to several homes in one locality at the same time. Dedicated EV charging stations will require major upgrades to existing electrical distribution infrastructure to deliver the power needed. Additionally, battery technologies are continuing to improve, and with better energy densities comes a corresponding surge in higher charging demands.

Over the last century electricity has been generated in power stations located some distance from where it is consumed – especially nuclear ones that are situated well away from populous areas. Long transmission and distribution infrastructures have evolved to manage the distribution of power from generation to consumption that can balance supply with demand. Up to now this balance has been fairly straightforward to maintain and manage.

However, that infrastructure is unsuited to the electrical demands of the future. It would need such a major update over a short period that it doesn’t make economic sense. Electrical resistance already causes power to be lost through heat all across the transmission grid, losses which increase with current drawn. The predicted increase in electricity demand, especially the very high power demands of EVs, that will ramp-up significantly, mandates a change to existing distribution infrastructures.

This has led to the concept of smart grids, a forward-looking approach that will provide localised power generation, storage, distribution, and consumption. Renewable energy will be generated locally, often by individual homes or businesses, using sources such as roof-top solar or backyard wind turbines. Home battery arrays (like Telsa’s Powerwall), capable of storing several kWh of energy, will feature prominently within the smart grid. Every local power generator (home or business) will store, trade/sell and consume energy within their local area, thereby augmenting (and over time replacing) existing grid power and minimising the need for updated transmission infrastructure and more power stations. And all of this will be facilitated by Internet of Things (IoT) sensors and control systems, which will manage this new intelligent infrastructure. And, energy can be exported from the local smart grid to the national grid or vice-versa, if required.

This movement will result in a localised energy market in order to balance supply and demand within the smart grid. A home owner may agree, for example, that smart grid control systems can manage individual heaters within their home, remotely switch off their freezer for a period of time while ensuring that it gets switched on again before breaching a temperature SLA, or determine at what time overnight their EV is charged, so that demand can be satisfied elsewhere in the grid and their own bills will fall. Energy stored in home batteries will become a commodity that is traded locally. The smart grid will automate all of this, and new types of local grid cabling connecting everything will emerge. People will be incentivised to invest in a certain level of “home renewables” generation, and a certain level of local (in-home) storage, with a diminishing reliance on national grid infrastructure. New energy micro-trading platforms will be established, and tariffs for electricity will become completely variable, and automated, so that at any point in time, energy in a particular location has a specific value that is determined solely by supply and demand, rather than pre-defined by any one supplier or government.

Stakeholders are well aware of the impending disruption to their industries, and work (and significant investment) has already taken place to better instrument and control local transmission and distribution grids as a stepping stone towards a future state. These grids are getting smarter, and many countries have actively encouraged or even mandated the adoption of smart meters. Their primary purpose is to enable utilities to understand exactly where power is in demand at a granular near-real-time level, so that they can help optimize the way that distribution networks can best supply that power. While smart meters are being deployed for both gas and electricity, gas consumption – which is primarily used for heating and cooking – has a very predictable utilisation, and gas is likely to diminish as a natural resource over time anyway. Electricity demand is more opaque, and it will become very hard to predict accurately due to EV battery charging.

Because initial smart-meter implementations have been designed for the benefit of the utilities and not consumers, little thought has been applied to the user experience. The hype surrounding some national advertising campaigns hasn’t been matched by the reality on the ground. This has led to some new market entrants offering an alternative smart metering initiative aimed solely at consumers where, rather than replacing an existing meter, a current sensing clip is simply placed around the incoming supply line. Instantaneous power demand can then be analysed through rapid sampling of current drawn and processing of this data to determine which devices are being switched on and off within the home and the current drawn by each one, giving consumers real insights into their power consumption and valuable personal guidance on how to change their behaviour. Over time, utilities and smart metering companies are going to build or acquire this capability and incorporate it into their smart metering solutions.

All of this is going to disrupt entire industries and markets and create new areas of opportunity. These range from the mining of minerals for and manufacturing of home batteries and solar panels, to smart grid platforms, the rise of IoT players, new energy trading platforms, and as yet unknown and unintended impacts to national grid systems. Many governments are keen to do something to support this transition, but they’re not sure what.

For utilities who currently own the customer relationship, they know their industry will be completely upended. They will either need to transform out of all recognition to stay ahead of all the advances in distribution, technology and usage, or they will cease to exist. It’s going to be a seismic shift, and it will alter the established ways of doing things irreversibly.

Artificial Intelligence in Financial Services

Ness recently hosted a thought-provoking discussion in Manhattan for a selection of high-powered guests from within the Financial Services industry. The subject for discussion was the prevalence and maturity of those two, now ubiquitous letters: A and I. I was compere for the event and, as I looked out through the audience (and having seen the guest list), I saw representations from investment banks, wealth managers and hedge funds; and job titles from client relationship and trading leaders to data architects and Head of Analytics. We really had a cornucopia of interests, perspectives and appetite for innovation.

As the compere for the event, I set the stage by putting the Ness Point of View forward. In this space, through our work with many Financial Services companies and other adjacent (or even unrelated) industries, we see some fundamental truths which need addressing. In a world of AI-hype, there is enormous potential for misdirected efforts which lead on to unsatisfactory results, and these risks will only be mitigated by tackling these truths from the beginning.  Core to it all is structured data, talent and business-focused use cases. Misery awaits those who hire five data scientists and set them in a corner waiting expectantly for Sesame to Open. There are universal business principles which apply here as with every other hot topic: you need senior, open-minded C-suite support. You need ordered data with structure, hierarchy, taxonomy and ontology before you can start training an AI engine to look for patterns. And, you need top talent in leadership, management, execution and validation roles. This is a team effort, and cross-functional, complementary skills are essential for any AI initiative to take off and deliver commercial value.

The drivers for AI’s general growth spurt are frequently cited: low cost of enormous computing power and storage in the cloud, accessibility of open source platforms with commodity algorithms to personalize with your data, and the ubiquity of data generating sources like mobile phones and IoT sensors. To those I added some Financial Services-specific factors to channel the discussion:

  • Privacy, security and compliance with the regulator have been used historically as a reason not to move as fast as some of the Silicon Valley behemoths in AI (think Google Search, Skype Translate and Amazon/Spotify recommendation engines).
  • Regulations of the last 10 years mandate a move to “Digitize Everything.” Although this was intended to create audit trails for compliance purposes, it has been the catalyst for forcing structure on previously unstructured data sets: an essential ingredient for AI.
  • An appetite for finding “insight in usage” has meant intelligence is being uncovered from clickstream analysis of on-screen behaviour by consumers, analysts and buyers and sellers. This is now being augmented (and enriched) by data from voice calls and social media sentiment analysis. (Indeed, some AI-based investment strategies are based on volumes of retweets of a particular news item and now an interpretation of what the text in those tweets really suggests).

My guests on our distinguished panel included Shekar Pannala, CTO of S&P Global Ratings, representing the large enterprises and S&P Global, which generate value through managing and finding insights in billions of data points every day. S&P Global Ratings is focused on delivering what they are calling Essential Intelligence to “provide users with the tools to make critical decisions with conviction.”

Shekar was joined by Sean McDermott, Senior Analyst from Corporate Insight, an analyst firm that provides recommendations and expert analysis on improving the digital offerings and overall user experience to over 100 financial institutions. Sean writes widely on FinTech trends and how disruption in Wealth Management is moving into disruption in Insurance and Retirement Planning. He specialises in the Robo-Advisory space.

Last, but by no means least, I called upon Rajeev Sharma, Chief Solutions Officer at Ness, and one of Ness’s most passionate advocates for advancement and maturity in architecting these Platforms of Intelligence. Rajeev provided a technology perspective in his inimitable style and brings decades of relevant enterprise experience, coupled with his academic odyssey at the MIT Sloan School of Management and School of Engineering Systems.

I asked the panel about the landscape and appetite out there in the market for AI-inspired strategic offerings – and what were their observations on common obstacles holding companies back.

Shekar noted a home truth: that S&P Global Ratings works in an extremely regulated environment and that whatever AI solutions they create need to respect and not violate that framework. It may well be okay for certain Hedge Funds to openly attract investors to follow their bespoke AI-investment strategy, but S&P Global Ratings is used as a prime resource by companies, governments and individuals to make serious decisions about other people’s money, so their innovations must be ready for scrutiny from the regulators.

Sean added that the Robo-advisory industry was improving its offers rapidly. The likes of Betterment and WealthFront had set the ball rolling but had some of their thunder stolen when the giants like Vanguard and Charles Schwab responded with similar all-digital offerings. The battle is now on to offer more features that deliver on the need for “extreme personalization,” whereby the intelligence is taking account of known needs, behaviors and preferences, and advising, guiding and maybe executing on some of those personal investment drivers. Bank of America is pushing Erica and there is now Finn by Chase. The move to a personalized intelligent assistant advising you what to do next is accelerating fast. And, it will be part of the working day of every financial analyst very soon.

Shekar touched on the need for building solid business cases and technology accelerators as proof points ahead of making major investment decisions. He also stressed the potential from accessing and integrating seamlessly with new data sources and adjacent datasets. Indeed S&P Global Ratings is making investments in companies which specialize in satellite imagery which will surface insight and generate data for analysts in the commodities, transport and logistics markets. S&P Global Ratings is also making investments in automating as much of the ratings and publishing business as makes sense, removing inefficiencies and redundancies to help get accurate information out in near real-time.

Next, I asked about the nature and nurture of talent. Do you train it or buy it in or buy a company? There is a lot of jostling for position going on out there, and it seems that for many FinTech startups, being acquired by one of the big boys with all their scale and balance sheet is a more likely outcome than overtaking them in the market. S&P Global Ratings and Corporate Insights both see a large appetite for investing in talent and partnerships to make it happen faster. Rajeev made a strong point to sum it up: Ness has invested in training over 100 engineers to take them from being a strong engineer to be a full-stack, industry-ready AI engineer. The reason for doing that is because talent in this area is so hard to find with any scale. Many firms will struggle to mobilize a decent-sized team and get to market quickly with a solution for this reason. If they choose to do it internally, they may end up having to buy a company to get the talent – and they are expensive hen’s teeth at the moment.

I got to ask the big questions like were the days of the human analyst numbered, and whether a machine was likely to offer better investment advice than a human any time soon. Both Shekar and Sean smiled knowingly, and seemed to follow the same line. It may well happen – but not in the immediate future.

Driving the use cases that I heard about from the audience in the Q&A were demands from investors, customers and employees for extreme personalization. Based on intelligence latent in related adjacent data sets (but previously untapped and locked out of sight) is a market-driven need for real-time notifications, guidance, pattern spotting and calls to next best action to make my day (whoever I am) more fulfilling and valuable – and simultaneously, less mundane and less productive.

It was a stimulating discussion with a positive energy directed to making things better rather than noodling over a dystopian future where we have no say in the narrative. I very much look forward to continuing those discussions in subsequent conversations with new-found colleagues.