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TAKING A MORE HUMAN APPROACH TO AI

Author: Chris Pope, VP Innovation, ServiceNow

 

Artificial Intelligence (AI) is all about machines, obviously. Except it’s not. In truth, discussions surrounding AI may often centre around how competent, intuitive and contextually aware the machine brains we are building have become.

 

But really, AI is all about us―the humans—and how it can make our lives better.

 

Chris Pope

There was a time, perhaps even inside the current decade, when AI tools and functions were still associated with the fanciful ‘talking computers’ that featured in many 1980s movies. It wasn’t that long ago that we considered AI as something of a ‘toy’ and its application in mission-critical enterprise applications was still somewhat laughable. Of course, now we take talking computers completely seriously. So much so that we’re equally focused on the proficiency of computer speech recognition.

 

Application of AI

But as far as we have come, we still need to look at the real world use cases of AI and ask how it can help us make our lives better. If we’re not applying AI to our human work experiences to examine and analyse where it can make those experiences greater, then what are we doing here in the first place?

 

The truth is, many enterprises large and small have been struggling with finding the appropriate use cases for new and emerging AI technologies. Companies need to find the workflows inside their business models that can benefit from AI. Only then can they start to architect towards turning those operational throughputs into truly digital workflows.

 

So how do we define AI-enabled digital workflow Nirvana and how do we get there?

 

Typically, the process starts with a technology audit and a process of assessment, quantification and qualification running throughout the IT stack in question. Individual business units will need to step back and identify their work problems and challenges as they look for the elements of their workflows that can be digitised.

 

Everybody across every line of business function will be involved―we need to crowdsource and collaborate to identify strategic areas of business operations that still exist as predominantly manual, accurately measurable and fundamentally repetitive.

 

These are the parts of business that represent liquid gold, i.e. once we tap the seam, we can channel these functions into AI-driven services that subsequently run as digital workflows. Individuals are liberated from drudgery, productivity is increased and employees have a greater experience—a new virtuous circle is established.

 

Practical examples

Think about a typical office. When people leave the company, we need to manage who has a key fob for access to the car park. This is a perfect example of the type of job that has typically been performed manually through the use of a spreadsheet. This is time consuming, error-prone and obviously creates security issues.

 

But it’s also (I hope obviously) a perfect example of the type of task we can evolve to become a digital workflow driven by intelligence stemming from AI. Our analytics engine should know that an employee is leaving the firm and so reports, alerts, emails and perhaps even mobile device management, to cancel the key fob, can all happen automatically.

 

If we can make all those things smarter and more intuitive, then we can build better experiences faster.

 

Uber hasn’t actually done anything fundamental to change the way taxis work or drive. It has changed the digital workflow that governs the ability to book and pay for the service. The list of services-centric examples in this space is growing every day.

 

Automating a bad process doesn’t make it good

In the technology industry, we are often bad when it comes to decommissioning things. Think about how many business processes probably exist today that firms need to eradicate and get rid of.

 

There’s no point in applying AI to these aspects of the business. As we know, automating a bad process doesn’t make it a good process; it just makes it an automated bad process! So, this re-engineering is actually an opportunity to clean out your cupboard and stop doing the things that you no longer need to do.

 

An example that came out of a recent hackathon, we conducted, is a tool to help with filing of patents. One of our hackathon teams used AI and ML to trawl the web for all registered patents using word recognition. They wanted to identify connected words to see if a new invention already existed in some form already. This would have been costly manual work, that may have been handed over to a specialist (in this case, a patent lawyer), but now we can digitise these aspects of the business.

 

The human factor baseline

We as humans now need to engineer the existence of AI into our own mindsets and consider how it can help us work differently. This includes knowing what things we don’t need to worry about anymore. For example, we don’t take a map out with us these days, because we use a smartphone—so what else can we stop doing?

 

As we move down the more humanised road to AI, we will find that AI itself gets smarter as it learns our behavioural patterns, penchants and preferences. We must still be able to apply an element of human judgement where and when we want to, but that’s already part of the current development process as we learn to apply AI in balance when and where it makes sense.

 

The future of AI is smarter, and it is also more human. The end result is more digital at the core, but more human on the surface. If that still sounds like a paradox, then it shouldn’t. We’re at a crucial point of fusion between people and machines and it’s going to be a great experience.

 

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REACHING THE NOT-SO DIGITAL NATIVES

DIGITAL

By Garry Hamilton, Group Business Development Director, Equator

 

It’s 2020. There’s no denying that banks and financial institutions have found themselves in a war against the tech giants in recent years. But can they win? Can consumers ever be truly satisfied? Or will institutions in this space stick with what they know regardless of how well it is working? In the digital-first now, FS companies have moved into an uneasy but rewarding landscape. Just as with consumer goods, they find themselves in a space where they no longer innovate ahead of consumer aspiration and demand, instead finding themselves increasingly under pressure to catch up.

 

The experiences consumers have with global giants such as Google, Amazon, Facebook and Apple (GAFA) define their expectations for all digital experiences. To stay up to speed, FS companies need to understand the shift in consumer demand as well as the multitude of threats to a business model that’s seen as traditional and staid. In short, they need to prepare.

 

DIGITAL

Garry Hamilton

The paltry, taped-together digital offerings from the incumbent financial service brands no longer stand. However, these brands still go to market with products and services defined by internal processes and limitations, giving little consideration to true service design principles or customer experience.

 

Thankfully this is changing, in part by credible (and incredible) upstart fintech companies, chipping away at the monoliths. At Equator, we work with several brands in this space, including Santander and Virgin Money, all of whom have realised the tides are changing. Major finance brands are no longer looking for the sharks in the water that come after all they do, instead realising that it’s a multitude of piranhas that pose the most significant threat.

 

Case in point: TransferWise has demonstrated that something as mundane as foreign exchange can be made fresh; Atom Bank has shown that a lean approach to savings and loans can drive solid business without being so reliant on rate, and Starling Bank has demonstrated that a serious focus on making the tech work can yield excellent results. Consumer choice for financial services has never been greater.

 

The hidden threats that GAFA may pose on traditional finance brands are, as yet, not fully realised. Apple has already demonstrated its ambition in the US with its digital credit card offering. Amazon has Amazon Pay and shown interest in the insurance market. Facebook is out there with its (stumbling) cryptocurrency effort, and Google’s feature-creep into aggregation (and payments) indicates a genuine and poorly understood threat from some of the wealthiest and most capitalised tech companies in the world. It’s hard to imagine a reality where consumers reject financial services from these brands.

 

But for incumbent brands in this space, the opportunity to maintain success lies in two key areas. Firstly, data. While it’s commonly understood that this is the currency that enriches the GAFA businesses, consumers’ financial behaviours are still broadly out of reach. Banks and financial institutions with historically loyal customers are sitting on a gold mine of data that can be turned into actionable insights. Insights that could deepen loyalty, increase relevance and make historically uninteresting and stuffy institutions appear modern and relevant.

 

Secondly, these organisations have significant human knowledge capital. These people know how the wheels turn, how to negotiate regulation and compliance, and how to manage risk. When you look to the most successful start-ups, their success is less borne of wealth, but more of knowledge and how financial systems operate. That cannot be underestimated. Banks and financial institutions need to strive to keep their staff loyal – not just the traders with their extreme bonuses. They’re not the ones that tech businesses would come after.

 

Getting a financial service off the ground isn’t cheap, but that’s not something GAFA worry about. Instead, it’s the complexity of negotiating the regulations and marketplace. What FS brands need to watch out for is that the fintech piranhas do not become sharks – not necessarily through growth but through acquisition and consolidation. Acquiring TransferWise, Monzo or Starling Bank is still pocket change to these organisations. And they DO have the technical wherewithal to bring autonomous platforms together and make a success of it, something high street banks and insurance companies have proven incapable to see through.

 

To survive and thrive, financial brands should take advantage of the one thing they’re historically good at – assessing and mitigating risk, with the critical difference being that keeping it the same as it’s always been is no longer the safe option. At Equator, we’ve already seen clients, such as AXA and Lloyds, acquire or partner with fintech start-ups. There’s a real effort from the high street banks to deliver a Monzo-esque functionality to their customer base. And we see real innovation in everything from insurance to loans and savings.

 

But there is still a long way to go. Regulation in the UK has been reasonably balanced between control and competition since 2007. However, technology continues to outpace the law, and we need to keep the pressure on the regulators to allow for new customer engagement models, new ownership models and new ways to deliver financial products and services.

 

In the last few years at Equator, we’ve assisted many major financial institutions take on tomorrow by helping them innovate and bring new products and services to life. We’ve helped Virgin Money bring their innovative B banking service to life, pioneered original service design in the most mundane of places for Tesco Bank and a lot more besides. We know that there are many enthusiastic brands out there looking to take on tomorrow and bring digitally-enabled services to life. But the sector still has some growing up to do. Crucially, it needs to accept that the disruption that came after the 2007 financial crash has nothing on what is around the corner

 

We’re still only really getting off the ground with the second payment services directive. Open banking is creeping in. We’ve yet to see the promised liberation of the payments sector (which should be huge), and it’s fair to say we should expect more niche disruptors to emerge, as money continues to pour into the sector. And that’s not even covering off the effect that machine learning and automation will continue to have in the industry over the coming years. If you ever dared to think finance was dull, get ready for a disruptive and exciting time.

 

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RISK VS REWARD: IS AI TAKING OVER?

AI

Xavier Fernandes, Analytics Director at Metapraxis

A study by Oxford University academics into “The Future of Employment” in 2013 prompted apocalyptic headlines which stated that in the future 40% of jobs will be automated thanks to advancing technology.

The researchers subsequently claimed that the truth was in fact a little more prosaic; rather than facing complete automation, the research found that 40% of jobs faced some aspect of automation in their activity. So with new ‘AI processes a likely reality for almost half us, what does that mean for our current roles and should we be worried?

 

The fourth revolution?

The first industrial revolution saw machines replacing muscle, both human and animal. The second and third saw electrical power, mass production and computerisation revolutionise the job market. Now, with daily headlines of AI as an employment superpower, there is some concern that AI is bringing a fourth revolution, and with it, unknown circumstances.

This ‘fourth industrial revolution’ is defined by replacing brain power with machines. Our thinking capacity is what inherently sets us apart from other species, so it’s not surprising that any encroachment on it triggers some existential angst.

 

AI

Xavier Fernandes

Evolve to reap the rewards

While many businesses still don’t fully understand the capabilities of AI, those who fear its development are, instead of embracing it, missing all the benefits that it can bring to the workplace. Businesses that utilise AI appropriately are seeing vast improvements across their entire value chain; better customer experience, reduced costs, and more insightful analysis to support management decisions.

AI is particularly useful for supporting tasks with repetitive activity, for example, performing financial checks and assessing large sets of data within financial services firms. AI performs particularly well within this context, spotting outliers before a human expert would notice them, allowing impending problems to be flagged and avoiding costly mistakes.

There is also an increasing focus on maximising customer lifetime value through the use of AI. Being able to predict existing customers’ needs as well as track trends in their financial circumstances is supercharging the old cross-selling approach with testable, predictable outcomes.

With potential benefits like these on offer, management teams of innovative financial services are increasingly relying on AI to help them with some of the heavy-lifting of analysis. Using advanced data capabilities and learned behaviours, AI analyses market trends to provide predictions of future performance. This insight is invaluable and allows management teams to change direction and correct any problems accordingly. This offers a huge advantage over those that have not adopted such tools.

 

Supporting the workplace

Algorithms and AI are typically ‘smart’ at doing one, tightly-constrained task, but they can be less helpful with many of the activities that humans find straightforward. In most white-collar jobs, automation tends to replace certain tasks in the job, rather than the role in its entirety, as the need for human intelligence is still highly necessary. In particular, we still need human input to first challenge, and then synthesise, this information before taking action. Employees should therefore work with the business to proactively identify what areas of their role could be automated, so that they can focus on the areas that add real value to the business’ commercial goals.

Challenging AI is certainly still important. We know that algorithms can be much better than humans on certain, bounded tasks. However, many algorithms rely on existing data sets to build their understanding. As a result, when a business unit has ‘symptoms’ that fall outside of that body of knowledge, the algorithm may suggest the wrong course of action with costly results.

Indeed, even with plenty of data, algorithms will reflect any biases the data set contains. We’re seeing this with some legal sentencing algorithms where there is evidence that they are treating disadvantaged people more harshly. Getting the answers to why and how far we should trust our algorithms should therefore become an everyday part of any job affected by AI.

Rather than depending entirely on AI for all decisions, workers should be taking all these new, AI-generated insights and using them to complement the human decision-making process. No manager of a complex business ever has enough time to sieve through all the analysis available, but with AI driven algorithms able to flag up any issues and indicate where action needs to be taken, we may find that we have some AI ’colleagues’ who will cover our backs and suggest innovative options. Yes, there will be times when the algorithms get it wrong, but as long as we’re watching out for those, the future is bright.

 

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