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HANDLING DIGITAL TRANSFORMATION AMID GLOBAL UNCERTAINTY

Karl Van den Bergh, CMO, Gigamon

 

For most financial services firms, digital transformation (DX) was already a challenging, complex and costly – albeit necessary – journey required to keep up with a rapidly evolving technological landscape. However, these difficulties have been compounded by the current global uncertainty, which has seen the explosion of remote working and distributed infrastructure, as well as rapidly changing IT networks. What’s more, banks and financial services are facing increasing pressure as economic downturn prevails and the threat of the greatest recession in three centuries looms over the UK, with GDP falling by 20% in April.

The pressure the current global situation has placed on IT systems that were never designed for this scenario is intense and, as a result, key digital transformation shifts are taking place. Now, more than half of financial services organisations plan to accelerate the implementation of their next generation of technology strategies, and this is already underway. One third of jobs advertised at UK banks are tech-related – an increase of 46% over the last three years – and the way they recruit is changing. Banks are turning to pros from big-tech companies, proving they acknowledge the need to provide their consumers with seamless tech experiences, similar to those from Apple and Google.

In practice, recent DX shifts have included the rapid scaling of remote access infrastructure, enabling apps to meet heightened demand and adapting to new, expanded security perimeters. IT and security teams have had to adapt rapidly to these new challenges in order to maintain network performance, security and a seamless end-user experience – and they have had to do all of this in a minute time frame with reduced resources.

 

Karl Van den Bergh

Adjusting to the fluid workplace

The most notable shift experienced over the last four months has been towards a remote workforce, as people have been forced to stay home as much as possible. Before this shift occurred, 59% of organisations had more East-West traffic than North-South traffic, which shows the scale of this transition. IT and security teams were able to make the move from LAN to WAN quickly, but often only as a temporary fix. However, now it is looking likely that a fluid workplace will prevail during the return to work phase through to the end of the year and beyond, IT teams must solidify these temporary processes in order to emerge victorious from this economic uncertainty.

The patchwork response many IT teams took in response to the work from home (WFH) shift was magnified within financial services, as big banks typically have a legacy IT problem. In fact, nearly 50% of banks don’t upgrade old IT systems as soon as they should. Repurposing older or existing infrastructure will cause issues such as failures and bottlenecks within the new network architecture. To avoid these detrimental issues that could lead to reduced productivity from employees – which could encourage customers to look elsewhere – financial services firms must ensure they have accurate visibility into the network. Having a clear view of all data in motion will enable IT and network teams to undertake capacity replanning, identify critical traffic and optimise bandwidth usage to ensure the network runs smoothly.

 

Optimising mobile apps

Banking customers increasingly want speed and convenience, and COVID-19 accelerated the shift to online as customers were unable to engage with their banks physically. A recent McKinsey survey found that one in five customers in Britain have tried online banking for the first time during the COVID-19 crisis. New application containers, microservices and virtual machines are being set up to quickly meet this sudden growth in demand. However, this can lead to a mismatch in application capacity and infrastructure capacity, as IT teams may find it hard to keep up with fast-working DevOps and applications teams. In the worst case scenario, as app capacity increases, infrastructure capacity and user experience may lag and bandwidth issues could appear. What’s more, traffic and app usage may not be adequately monitored for security threats, which could be hugely damaging in an industry as critical as financial services.

In order to counter issues that may arise from increased app use, IT teams must ensure they thoroughly monitor and visualise app usage, and take appropriate action in response. Surges in traffic at a particular time of day may cause security tools to become overwhelmed and fail. By visualising where and when these surges occur, IT and security pros can decide whether the traffic needs to be assessed by certain tools, as well as filtering out safe or low-risk traffic to preserve bandwidth for other apps. By optimising apps and security tools in this way, financial services organisations can do more with less and maximise the ROI from their existing investments.

 

Maintaining borderless security

Employees have successfully adjusted to remote working, and many are set to continue this as businesses maintain a fluid workplace going forwards – with the workforce able to work freely between the office and their homes. This means the network has turned literally ‘inside-out’: traffic that was previously inside the firewall is now entering the network from outside. It is no longer enough to rely on a perimeter-based approach to security, as the network is going borderless. With employees now connecting to the corporate network from personal devices and routers, ensuring network security has never been more important as it’s impossible to guarantee these users are following the correct protocol and using patched devices. In fact, 94% of IT pros in the financial services industry say they lack confidence in the ability of employees to safeguard customer data.

The need for unhindered security is driven home within the finance industry due to the sensitivity of the data it holds and the potential damage that could be caused if a malicious actor found their way into the network, especially as attacks exploiting banking trojans rose sharply during May this year. Therefore, the need for a Zero Trust approach is more prevalent than ever. This network architecture sees the behaviour of everything on the network – regardless of whether it’s inside or outside the perimeter – scrutinised, with access granted based on its behaviour rather than any pre-existing credentials.

The finance industry is set to face a challenging year as the full impact of the COVID-19 crisis becomes apparent, but this doesn’t negate the need for DX. In fact, accelerating digital initiatives has never been more important as financial services firms simply can’t afford to suffer network downtime or security incidents during this challenging time. Optimising existing investments and ensuring IT teams have complete visibility into the network, can help financial services organisations accelerate their digital journey and enable them to emerge victorious from this economic downturn.

 

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Technology

HOW TO ACHIEVE THE BEST POSSIBLE CUSTOMER EXPERIENCE THROUGH ARTIFICIAL INTELLIGENCE

By Craig Charlton, CEO of SugarCRM

 

Before high definition televisions were introduced, home entertainment was limited to a grainy picture on a small CRT box in the corner of your living room. It could in no way compete with the picture quality seen in cinemas, for instance, with details hard to pick out. The difference was like chalk and cheese. In the early two thousands this all changed with the introduction of high definition LCD tv’s, which took home entertainment to a new level and made it feel like a truly immersive experience. More recently we’ve seen the introduction of 4K and even 8K devices, which have taken the experience even further.

In the world of CRM and CX, we’re now at a similar watershed moment. Currently, most businesses have a fragmented, dated and distorted picture of their customers, which is affecting the level of service that they can achieve and their ability to grow. Poor data quality is hitting organizations where it hurts, costing them time and money. It’s important that organisations act now to replace their current hazy view of their customer, with a sharply focused picture that’s rich in breadth and depth.

We call this a high definition customer experience, or ‘HD-CX’, and by delivering on it, businesses can reach new levels of performance and predictability, and increase customer lifetime value. There’s a long way to go however, as research indicates that 91% of data in CRM systems is incomplete, and 70% of CRM system data goes bad each year[2].

 

Craig Charlton

 Where can CX take us?

Forrester states that over the next 5-10 years CX will become “crucial for brands to survive, for them to avoid disintermediation, irrelevancy, blandness, and/or cluelessness about customer sentiment.”[3] Those brands that choose to wait 5-10 years before delivering a HD-CX experience however, will have found themselves disrupted and behind the curve.

To leapfrog competitors and fuel growth, companies need to  obtain a high definition view of their market, business and customers as soon as possible. Right the way from formulating ideal customer profiles (ICP) and identifying sectors with a propensity or intent to purchase, through to customer lifetime loyalty. HD-CX is all about drawing on accurate, up-to-date information from multiple sources and from across the organisation to reach new levels of business performance and predictability. The value of performance and predictability applies to businesses of all sizes and in all industries.

 

Why is being ‘time aware’ so crucial?

It’s about time we redefined the 360-degree customer concept and added the key missing component: Time. It was Benjamin Franklin that said, “Lost time is never found again.” Although nobody can stop the flow of time, what we can do is ensure a complete historical record of every change event in the customer journey, and augment this information via a rich repository of relevant information to ensure full situational and directional awareness of a customer.

It’s no good just having a 360-degree view of one moment in time—right now. Recording every change event in the customer journey is essential for predicting future outcomes. Accurate predictions enable companies to make better business decisions, manage risk, respond to problems and take advantage of opportunities. Organisations need to gain insight into the past, present, and future of their customer-facing business processes.

 

Stay ahead of your competitors with artificial intelligence

Making sense of all this data can be a perennial issue for companies, with the average company holding on average 162.9TB of data[4]. To make sense of this data, create a competitive advantage and deliver an unparalleled level of predictability across a whole array of different business use cases, Artificial Intelligence is the key.

Understanding your current state and how you got there is essential, but what if you had the ability to look into the future toward what your business could be? I’ve already stressed the importance of having a complete historical record of every change event in the customer journey to ensure full situational and directional awareness of your customers and your business, but AI considers the other direction of time: the future.

AI has the ability to deliver exceptional predictions, even with limited or incomplete CRM data by leveraging vast external data to consider factors your data doesn’t cover and surface insights that you may not have known existed. These unparalleled predictions allow businesses to make confident decisions and focus on the highest priority activities across marketing, sales, customer service, and more. However, not all AI-powered prediction is created equal and predication accuracy is essential for success. A proven platform with deep learning models combined with both the best quality external data and CRM data, is a combination that most companies can’t provide their customers.

If companies successfully create a time-aware and high-definition picture of their customers, they will benefit from greater customer relationships and unparalleled oversight of their businesses. However, they must utilise AI to provide exceptional customer experiences and business predictions. If businesses don’t adapt to change and instead continue to operate with an old, outdated, standard-definition view of their customers, they will lose out to competitors as they won’t be able to deliver an experience that their customers expect.

 

[2] https://www.dnb.co.uk/content/dam/english/business-trends/b2bm-db-improve-the-quality-of-your-marketing-now-1-0.pdf

[3] https://go.forrester.com/future-of-cx/#:~:text=The%20wheel%20of%20change%20is,relationships%20stable%20amid%20unprecedented%20upheaval.

[4] https://cdn2.hubspot.net/hubfs/1624046/IDGE_Data_Analysis_2016_final.pdf?t=1496694598964

 

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USING ARTIFICIAL INTELLIGENCE TO ACHIEVE CIRCULAR ECONOMY

By Professor Terence Tse, ESCP Business School

 

It is really only a matter of time before the two main trends, artificial intelligence (AI) and circular economy, would come together. A milestone of this convergence was the white paper “Artificial intelligence and the circular economy”: AI as a tool to accelerate the transition, jointly published by The Ellen MacArthur Foundation and Google earlier this year. It has kick-started the discussion on how AI can be used as a tool to help accelerate and scale our transition to a circular economy. This can be achieved by unlocking new opportunities through improving product and material design, enhancing circularity-based business models, and optimising circular infrastructure. The paper draws on the food and consumer electronics industries to illustrate the circular benefits driven by AI. The forecasted value that can emerge from these is encouraging: up to $127 billion and $90 billion a year in 2030, respectively.

 

The pace will be slow

No doubt these are very good news. It also shows how innovative technologies can take circular economy to the next level. Yet, I believe the path leading there will be full of challenges, not least because, contrary to what general media would like to get us to believe, the development of AI is, in reality, really slow.

 

There are several reasons attributable to this sluggish pace

First, there is a general shortage of AI-proficient graduates. Training up AI researchers takes time. Universities are not churning out data scientists fast enough to meet the job market demand. For those who are graduating, they will most likely be snapped up by the technology giants. Indeed, it has been estimated that some 60% of AI talent are in the employment of technology and financial services companies, leading to a ‘brain drain’ in academia, which in turn, slows down the production of qualified graduates. Small circular economy-based companies (as well as AI start-ups) will struggle to have the same hiring power, as they often lack the ability to match the levels of salaries and prestige offered by large organisations.

Another reason why circular economy-aimed companies, large or small, will struggle to deploy AI is that the technology remains a very expensive investment. AI is, at the moment, far from a plug-and-play technology. Arguably, there are off-the-shelf AI applications available in the market. But what this one size fits-all technology solutions can really do is often very limited and their effectiveness low. Inevitably, for AI to work at an acceptable, value-creating level, it is necessary to integrate it into the existing wider IT system. Customising AI applications to be embedded in the system architecture is very complex and hence very costly.

To make matters worse, the market is seemingly inundated with self-proclaimed AI companies. A recent report has suggested that 40 percent of start-ups in Europe that are classified as AI companies do not actually use artificial intelligence technologies in a way that is “material” to their businesses. As someone who researches and works in the business of AI, I can readily observe this phenomenon has already eroded the trust of many companies, making them increasingly cautious when proceeding with investment and deployment of AI.

 

Gradual developments, not quantum jump

For these reasons above, the adoption of AI, and by extension, in the area of circular economy, will be slow. This, however, does not mean there will be no advancement. Instead of “big bang” new business model creations, AI will most likely produce circular advantages through baby steps in operational enhancement gradually. For instance, one of the important elements in achieving circular economy is better asset management. In a recent research project for the European Defence Agency, my colleagues and I have discovered that there is a wide spectrum of operations for ministries of defence to save money and practise circular economy, from refurbishing and repurposing small military equipment items to reduce waste and minimise the use of virgin materials to extending the service years of capital assets. Unquestionably, the same may be applied to civilian activities. For example, combining the power of AI and drones can extend the longevity of major infrastructure such as reactors and bridges.

Advancements in drone technologies have allowed them to be deployed to take pictures at heights that are dangerous for inspectors to reach. The contributions of AI come from its ability to analyse and identify cracks as well as defects on assets that are not always visible to human eyes from captured images. Consequently, problems are detected before the assets become irreparable, thereby lengthening their lifetime.

A seemingly insignificant but potentially huge possibility of waste reduction would be saving on paper use. In the insurance industry, for instance, there is still a huge reliance on actual paper, with the communications between various stakeholders, including the underwriters, brokers and insured, passing on a large number of physical documents. AI techniques, in particular natural language processing, can help speed up the digitalisation of documents as they can go beyond the point of just reading and processing text to recognising and recording signatures and rubber stamp marks. Little by little, it will be possible to lower paper consumption.

 

The future is now

Both AI and circular economy are by themselves breakthrough ideas that are set to change the world dramatically. Combined, it can be a very powerful force of good. But this can only be achieved if we can synthesise them. For AI and circular economy to work together, it is necessary to educate AI developers to be more familiar with the idea of circular economy as well as making circularity practitioners and researchers more AI-savvy. Holding just half of the equation, we risk missing out on most of the intelligence. After all, no matter how smart machines can be, ultimately, it is the human intelligence – or stupidity – that determines the kind of future that we will be having.

 

Extract of “The AI Republic: Building the Nexus Between Humans and Intelligent Automation”

 

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