In our last article we looked at how productivity is one of the core benefits of a bank gaining control over its data and analysing it more effectively. But once a bank has gained this control and insight, how does it go a step further by augmenting sales teams with AI and machine learning tools that can digest large data sets and alert them to the needs of their clients?

Traditionally, banks have always held an information advantage over their clients – in fact their business models have been specifically designed to leverage the market information to which they have access and transform it into value enhancing insight.

But in recent years, this advantage has been slowly chipped away at as the markets have become increasingly electronic and the buy-side has upped its game in terms of the data to which it has access, and its ability to analyse large amounts of it. As market and price transparency has increased, one of the core competitive advantages of a bank’s sales desk has been eroded.

Against this shifting backdrop, banks are starting to realise the potential power of innovative machine learning and AI tools in helping them to upskill and maintain their competitiveness in the sales arena.  There is a dawning realisation that backward looking BI analysis is not fit for purpose in driving business forward, especially in this era of utilising AI to squeeze every possible efficiency and productivity from the resources at hand.

Some are now starting to deploy these technologies to enable predictive and prescriptive analytics, as well as connecting systems to prompt them as to the next best action for their clients. By absorbing information that might otherwise be missed, AI delivers the analysis to drive new sales engagement with clients and by delivering those insights at the optimum time.

Investment and adoption at scale is expected to increase significantly over the coming years. This comes as no surprise when you consider it has been estimated by McKinsey that AI can potentially unlock $1 trillion of incremental value for banks^[1]. These tools can be thought of as a GPS for the sales desk – those banks without it will struggle to compete against more forward-leaning firms who are empowering their employees with the most advanced digital tools.

The evolving role of the salesperson

According to a recent report from PwC, almost 80% of banking and capital markets CEOs see skills shortages as a threat to their growth prospects.^[2] This is because, quite simply, banks haven’t managed to keep pace with the changing manner in which their clients want to interact with them.

While no one is suggesting robots will completely replace salespeople any time in the near future, there are certain skills that can be enhanced when man and machine work together in tandem. One of the main skills that clients increasingly demand from banks is a more customised and tailored experience, which in turn drives a more intimate and refined relationship.

In addition, as electronification continues to grow, sales teams tend to manage a larger pool of clients across asset classes. Clients expect salespeople to provide a seamless service in multiple asset classes and have a global view of flows across the organisation.

Data therefore needs to be aggregated from across the organisation and made available to salespeople in one consolidated and comprehensive view so they can, for example, alert clients about new investment opportunities as they unfold – no matter the asset class.

Bridging the skills gap

In recent years, a growing number of large investment banks have launched ambitious projects to apply AI and machine learning techniques to previously unexplored data sources, in order to bridge the skills gap and improve how they sell to clients. A recent survey found that 75% of banks with over $100 billion in assets are currently implementing AI strategies.^[3]

Using the right technology, a combination of internal transaction data, external data feeds and unstructured data sources such as newsfeeds, can be standardised and aggregated into one holistic view. AI-powered advisory tools can then be applied to help banks anticipate client activity in order to build inventory for expected demand, identify unique and unforeseen market opportunities, extract timely information from news and websites, and alert sales based on market triggers.

Using AI and machine learning you can, for example, see which customers are likely to defect and move their business elsewhere, and therefore up your defensive measures.  After all, it is much more expensive to acquire a new customer than it is to maintain an existing one. You can also become more responsive and relevant to clients, because you are able to see what customer activity you anticipate on a particular day and then serve that customer with the appropriate inventory.

This technology has been leveraged over the last number of years to improve the service high-street banks deliver to retail customers. However, within investments banks the benefits of these same tools are beneficial to sales desks covering all types of clients including corporates, hedge funds, asset managers, insurers, pension funds, central banks and even internal clients.

Some banks are also exploring the use of natural language generation (NLG). This is a software process that automatically transforms data into a written narrative, making lightning-fast generation of expert business intelligence and reporting a reality in today’s financial markets.

NLG can generate intuitive prose that reads as if it were written by the best quant in the house at the click of a button, equipping sales teams with the collateral they need to offer up the most appropriate trading opportunities to their clients. These reports can even be prepared with enough variance and nuance in language and style to keep the copy fresh and engaging to the reader. This power of NLG is driving enormous time saving benefits across the organisation by taking laborious daily tasks and automating them at the click of a button.

Becoming AI-first

These are just a handful of examples of how AI and machine learning can help sales desks deepen customer relationships, provide personalised insights and recommendations, and, ultimately, turn the profit dial in their favour.

Banks that fail to make AI central to their core strategy and operations—often referred to as becoming “AI-first”—will risk being overtaken by competition and deserted by their customers in the coming years.

The current operating environment is both uncertain and challenging for investment banks, but a carefully planned programme that builds on cutting-edge data analytics and AI technology holds the key to driving growth and delivering the modern, information-driven trading experience that clients demand.

After all, it’s typically during periods of stress where relationships are forged. As a bank, if you’re able to guide a client through the fog of confusion, you will likely have a relationship for life – and AI and machine learning can assist in facilitating this.

But don’t just take our word for it. A client recently told us that since deploying AI technology across the front desk, their sales team had made 20% more calls, had 22% longer conversations with clients, and this had resulted in significantly more volume seen and executed. If you’re a salesperson known to have the best information, the client will call you first. It’s that simple.

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Microservices represent a radical shift in how organisations approach application development

The world of enterprise software development came of age with the emergence of ‘single purpose’ software applications aligned to a specific business function. It started with accounting programmes in finance, but with time, many areas such as manufacturing, supply chain and inventory management also benefited from the emergence of purpose-built applications.

Monolithic structures like ERPs, for instance, were designed to increase efficiency by transmitting information across business functions. Problems started creeping in, however, when businesses customised these applications to cater to their own unique requirements.

More often than not, increased customisation rendered these applications slow and clunky since they were too rigid to scale, making frequent iterations difficult. The IT department that was supposed to incite productivity became the reason for falling behind.

From a software lifecycle management perspective, monoliths carry larger risk than smaller applications. Implementing, updating and maintaining these applications can be a daunting task since there are so many moving parts that require constant attention. Microservices enable businesses to overcome this challenge.

Rewiring software architectures

In short, the microservices approach to software development means building applications with lots of smaller, modular parts – allowing enterprises to create software that’s more agile and independently scalable.

For example, if a business is shifting a monolith application to the cloud, it means relocating the same clunky software architecture on a separate system, along with all the shortfalls. This is why it’s important to build nimble and agile applications that can engage customers quickly and meet demands as soon as they appear.

A recent survey reveals that 63 percent of companies are currently using microservices architecture. A further 60 percent are doing so to attain faster turn-around times for new services and products, and 54 percent to drive digital transformation and next-gen applications.

What’s more, it’s easy to see why.  Microservices allow companies to deploy application functionalities as discreet lightweight services, which interact with businesses through application programme interfaces (APIs).

This allows businesses to deliver small application changes incrementally while speeding up delivery and reducing service disruptions, since if something needs changing, one ‘module’ can be removed rather than having to rewire the whole application. Considering that mobile and other digital applications are extremely dynamic and require frequent updates, microservices prove to be extremely effective there too.

This said, microservices architecture doesn’t entail combining several software components together. Rather, it involves the seamless integration of independent application functionalities that can communicate with each other through APIs. These interfaces allow enterprises to escape monoliths, as they serve as a ´contract’ between microservices.

To simplify their transition from a monolithic to microservices architecture, enterprises need a roadmap in place, specifically an ‘A-B-C’ approach:

• Abstract: Creating a layer of abstraction (or API layer) to access the capabilities required to service the customers, employees, partners, and machines
• Build: Aligning these capabilities to improve user experience, while separating the experience from how systems are architected
• Change: Breaking down the back-end services to more manageable microservices

The challenges that come with adoption – and how to overcome them

This simplified path to adoption may mean microservices is poised to become the default model of software lifecycle management going forward. However, according to a survey from Lightstep, a whopping 99 percent of organisations have reported challenges when adopting microservices.

For one, for microservices to perform at its full potential in terms of speed and consistency, continuous integration, testing, and delivery processes are required. One way to overcome this would be to deploy a service virtualisation strategy. This can help developers and testers to quickly simulate testing environments (even if the production environment is complex), reduce dependencies and allow ease of integration.

Also, with a microservices architecture that’s driven by APIs, organisations might have to keep track of hundreds of services running simultaneously. In such circumstances, monitoring even the smallest of changes in an application is tough, which is why developers should embed telemetry and analytics into the platform to simplify operations and change management.

Finally, it’s vital that every team involved in the microservices value chain takes responsibility for securing the services. Ensuring that calls are always routed through a secure service API gateway helps establish consistent security policies.

In short, teams developing microservices should care just as much about ensuring quality, operating and securing the software as much as developing it.

Time for change

Software architecture design might not appeal to all decision-makers across the board, but whether they like it or not, software applications now lie at the core of how a business operates. Their nimbleness, overall performance and resilience directly impacts business agility and ultimately revenue.

Microservices represent a radical shift in how organisations approach application development while moving to a software-centric model. If one’s thing’s for sure, it’s that it’s time for businesses to start exploring its potential to redefine the services they deliver to their customers.

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Retail-specific artificial intelligence startups are boasting billions in funding, cutting deals for tech innovations that range from robotics to carefully-crafted communications — and it’s only just getting started.

Driverless grocery delivery options and Amazon Go’s cashier-less stores present very visible examples of AI in the wild, but there’s far more in store — literally.

Reimagining Operations

Only 26% of today’s active AI use cases in retail encompass operational technology, but those cases pack a serious punch.

Data-driven intelligence can help retailers make smarter moves at every step, from procurement to on-shelf pricing. Optimized supply chain planning, theft detection, seamless pick-and-packing, and trend prediction can all be shouldered by clever AI algorithms and savvy robotics.

For example, the team at Bossa Nova builds real-time robots to perform basic inventory management: shelf scanning, data mapping, and product monitoring. The company’s already cut a major deal with Walmart for quick, incredibly accurate shelf stocking assistance, and are further developing their technology to meet needs beyond standard grocery and big box stores.

By saving associates’ time and guesswork for efficient stocking — all while collecting valuable data in purchasing trends — the friendly robots offer perfectly seamless integration into the modern retail space with big value benefits.

Communicating with Customers

The perks of AI for warehousing, inventory, and supply chain applications present themselves clearly, both in efficiency and sheer ROI. But the key to effectively engaging an AI strategy for retail rests in plain sight: optimizing interaction with the customer.

In an already saturated marketplace, engaging the customer through quality experiences and genuine communication can make the difference — and the sale — even above and beyond the all-powerful price point.

Subway Restaurants has already made international news this year with a targeted communication campaign, locking in customer loyalty through innovative one-to-one mobile marketing tied directly to the in-store experience. The company launched a partnership with Mobivity Holdings Corp. using Google’s Rich Communications Services to speak directly to customers — and place tailored promotions right within reach.

The custom communication has already garnered Subway over 10 times a return on their investment, taking data-driven details and AI capabilities to task in providing a premium customer connection. Loyalty — once defined by a card subscription or in-store coupon — has gotten smart and gone mobile, and it’s getting people in the door.

Personalized Purchasing

Subway’s mindful messaging illustrates just one angle of AI for personalized purchase experience: retailers are employing chatbots, voice shopping, and even custom apps to meet shoppers on their own terms.

Beauty powerhouse L’Oreal joins trendsetters like Sephora and Estee Lauder in embracing AR apps to give shoppers a real-time product testing experience: the company acquired startup Modiface in March of 2018. Modiface specializes in crafting incredibly realistic AR try-on apps, revolutionizing the customer shopping experience for hair, make-up, and much more.

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