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Industrial Revolutions – How AI Refactors Finance, Manufacturing & Healthcare

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Author: Lori Witzel, Thought Leader Alumnus, Spotfire, a business unit of Cloud Software Group

 

Today, Artificial Intelligence (AI) is big. AI is massive in terms of its ingestion of information through Large Language Models (LLMs) in the realm of generative AI. It is hugely diverse in terms of its scope across predictive, causal, generative and other AI use case types – and it is widespread and all-encompassing due to its applied relevance across every industry vertical.

Whether we like to call this the fourth or even fifth industrial revolution where levels of human-machine interaction and collaboration reach even loftier heights than many of us ever imagined possible in our lifetimes, the change AI is bringing about in industry is… big.

Boosting business value

Now that both business and technology leaders realise they need to evidence some level of AI optimisation and acceleration to drive new business value, these same departmental leaders also understand that it is vital to grasp the vast potential of AI to accelerate automated insights from predictive intelligence and analytics.

We now sit at a strategic inflection point where the opportunity exists to use pragmatic, insightful and (above all) functionally relevant AI-powered analytics to accelerate operational efficiencies across industries. This is a chance to actually change the way companies work; it’s a chance to create new business models and overhaul operational constructs that have been around for decades.

Simply put, we can now think about accelerating positive forces of digital disruption with new tools inside new methodologies, but inside industries that we already recognise. Let’s consider three very important examples in the shape of finance, manufacturing and healthcare.

Finance, money & banking

As we know, the financial sector runs on numbers. This core fact underlies this industry’s applicability for AI-enriched acceleration and automation. Because our AI engines (of whatever modal type) are designed to drink from large volumes of data to train and learn, the shape of the banking industry is inherently well-aligned with the use of AI technologies.

When we consider the user-level changes in finance and banking that we have seen played out in the last decade about the development of mobile banking and money management, we can immediately see where AI will apply. We need automated intelligence if we are going to build applications that can make decisions faster than any human operative could.

Users already expect instant service and assistance from the applications they use on their desktops and mobile devices. We need AI to keep pace with this new cadence. These same users now expect to be able to access instantaneous decision-making when engaging with historically manual processes related to banking transfers, deposits, trades and actions related to the new world of cryptocurrencies.

As stated in the CA Business Journal, “The advent of digital currencies, artificial intelligence and mobile applications has encouraged the proliferation of startups, which challenge traditional financial institutions by offering tailored services for today’s tech-savvy consumers.”

Manufacturing

It might sound like a simplification, but the manufacturing industry has many parts. Indeed, it has many parts figuratively, operationally and literally. With global supply chains having been jolted so markedly in the wake of the pandemic and other disruptive world events, every businessperson and consumer now has a more acute sense of where production lines are operating and where they are stalled or experiencing outages.

As we now build a more connected and collaborative world, applying AI to everything from error detection in production lines to customer delivery will be crucial to the future success of the manufacturing base in every country.

According to TechTarget, “Manufacturing plants, railroads and other heavy equipment users are increasingly turning to AI-based Predictive maintenance (PdM) to anticipate needs. If equipment isn’t maintained in a timely manner, companies risk losing valuable time and money. On the one hand, they waste money and resources if they perform machine maintenance too early. On the other hand, waiting too long can cause the machine extensive wear and tear.”

On the march towards new levels of efficiency, AI will now empower manufacturing organisations to augment their processes and reach a new level of capability in terms of managing supply chains more efficiently. This will encompass everything from monitoring the incoming stream of raw materials and the management of core utilities, all the way through to tracking parts and products across the manufacturing plant floor… and ultimately down to final deliveries to customers.

Healthcare

While many industries are described as having mission-critical functions, it is surely healthcare that is most directly understood to operate systems, equipment and processes that are life-critical. As we now use AI to crunch through massive volumes of data to detect life-threatening illnesses, healthcare professionals also have the opportunity to use automation to analyse and identify impurities on a medical production line more quickly. These are of course actions that could mean the difference between life and death.

Not only will we now use AI for detection and healthcare systems management, but we will also make use of it to accelerate processes that drive drug discovery and clinical trials. Although we stand at yet another inflection point as we humans start to build trust in AI-enriched medical practices, the industry will ultimately show the true worth of AI in this space as we get used to relying on data-driven solutions to streamline and automate administrative tasks, support physicians and look after more accurate and readily available patient records.

Building a data value chain

All these functions start with data and, crucially, it needs to be data that runs through systems that offer appropriate levels of governance. This means enterprises need to use data virtualization and data management tools that enable automated alerts and process changes so that information is properly prepared for data systems consumption.

It’s at this point that organisations in the three verticals noted here and elsewhere can start to say that they have established a data value chain i.e. an approach to information management that sees a business adopt industry-specific, rapid and pragmatic AI across the enterprise.

This is when a company starts to be able to meet and exceed customers’ expectations and demands. By using AI-enriched predictive and precise analytics, enterprises can now get ahead of their competitors, make real-world reductions in live operational costs and reduce their time-to-decision window in all aspects of business.

Business

How can law firms embrace automation and revolutionise their payments?

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Attributed to: Ed Boal, Head of Legal at Shieldpay

 

Once again, AI is dominating international headlines. This time, it’s due to a closed-door meeting this month between tech leaders and US senators to discuss the technology’s regulation.

AI and automation isn’t just for the likes of Big Tech. We’re seeing predictive and automated technologies transform almost every sector and the legal industry is no exception. In fact, recent research from HBR Consulting found that 60% of law departments had implemented a legal data analytics tool last year and more than 1 in 4 indicated they were using AI for at least a single use case.

However, adoption isn’t without its challenges. Reticence remains among some and there’s also the danger of ‘transformation fatigue’ slowing real progress. If law firms want to reap the many benefits of automation – including revolutionising their payment processes –  these challenges need to be carefully considered and thoughtfully addressed.

 

An area of great opportunity

Often seen as conservative, the legal industry has been gradually warming up to the idea of automation and technology.

While some pioneering firms have been quick to embrace automation tools, others remain cautious about disrupting their established workflows. As we navigate this landscape, it’s clear that certain areas of legal services are ripe for innovation.

One area is contract management. The process of drafting, reviewing, and managing contracts has traditionally been time-consuming and prone to human errors. Automation can alleviate these pain points by streamlining the entire lifecycle of contracts, from creation to renewal, thereby enhancing efficiency and reducing risks.

Another promising domain is legal research. Thanks to advancements in natural language processing and machine learning, legal professionals can now leverage AI-powered research tools that analyse vast volumes of legal data to provide accurate insights and case precedents swiftly.

But, while progress is undoubtedly being made, the legal sector still lags other sectors when it comes to innovation.

 

What’s getting in the way of progress?

This isn’t always down to a resistance to change. Often, it’s a result of firms spreading their resources too thinly across numerous technology initiatives.

Ed Boal

Attempting to tackle everything at once can result in ‘transformation fatigue’, where the benefits of individual innovations get diluted – leading to frustration and slower progress.

Before legal firms embark on digital transformation projects, a critical first step is introspection. Recognising and acknowledging areas where legacy processes and manual tasks still hold sway is paramount to optimising the impact of automation.

For many firms, archaic practices continue to consume valuable time and resources, diverting attention from higher value, billable tasks. One often-overlooked area is payments.

Legal firms play a critical role in complex transactions, from M&A and real estate deals to litigation and arbitration payments. The associated admin and processes represent a drain of firms’ time and resources. Spanning everything from collating stakeholder payment details and verifying payee identity to ensuring compliance with Know Your Customer (KYC) and Anti Money Laundering (AML) regulation, this adds unnecessary stress for lawyers – who would rather dedicate their time and expertise to their clients’ legal needs.

The repercussions of such time-consuming financial processes reverberate throughout the entire organisation. Administrative burden weighs heavily on the team, affecting productivity and ultimately, the bottom line: recent research from Shieldpay, surveying the UK’s Top 100 law firms, found that almost 1 in 3 (32%) say KYC collection and verification checks take 4-9 working days.

At the same time, firms are exposed to significant financial risk which can make handling client funds a costly endeavour. Not only are they penalised with fines if found to be in breach of stringent client account rules but firms are also subject to hefty premiums for Professional Indemnity (PI) insurance. No wonder 73% of all legal professionals and 90% of junior law professionals are concerned about the risks and time costs associated with holding client funds.

 

Revolutionising  payment transactions

In short, manual payment processes are more than just an inconvenience for modern law firms. They can damage relationships with clients – who have come to expect a fast, painless and automated payout experience in a digital world – and impede revenue generation by tying up top talent in an endless cycle of paperwork and (unbillable) admin.

So how can firms take the pain out of legal payments?

Fortunately, new payment technologies have emerged as a formidable ally. Third-party payment providers offering solutions for law firms, such as escrow and paying agent services for specific transactional deals, or more embedded payment solutions such as managed accounts (TPMAs) – i.e. outsourced client account functions – offer secure and instant transactions, while prioritising transparency and automation.

TPMAs operate as an escrow payment service in which the third-party – a licensed external payments partner – receives and disburses funds on behalf of a firm and their client(s).

With advanced encryption ensuring data security, working with a regulated payment partner means legal professionals and their clients can engage in financial transactions with peace of mind – while law firms benefit from improved operational efficiency.

And the advantages don’t stop there. Enhanced transparency builds a sense of confidence and trust, while the elimination of manual data entry and repetitive tasks allows legal professionals to devote more time to legal services and fostering stronger relationships with their clients.

AI and automation has much to offer the legal sector. But its adoption must be carefully planned in order to avoid transformation fatigue that risks stalling progress altogether. With typically shallower pockets than Big Tech giants, it’s important for law firms to focus their efforts on specific areas that could benefit from automation, rather than rush to overhaul their entire way of working, all at once. This controlled phase-out is the key to avoiding adoption frustration, seeing a real impact on profits and productivity and setting firms up for real, lasting change.

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In-platform solutions are only a short-term enhancement, but bespoke AI is the future

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By Damien Bennett, Global Director, Principal Consultant, Incubeta

 

If you haven’t heard anyone talking about artificial intelligence (AI) yet, then where have you been? Conversations about AI and its advantages to society have been a key talking point over recent months, with advances being made in the generative AI race and ChatGPT opening a whole plethora of possibilities. Many have highlighted the advantages of AI, but notably it’s ability to create human-like content.

But these discussions have only scratched the surface of what AI is capable of doing. It is for far more than just essay writing, adding Eminem to your rave and photoshopping dogs into pictures.

In marketing, we have been using AI for years, for everything from analyzing customer behaviors to predicting market changes. It’s enabled us to segment customers, forecast sales and provide personalized recommendations, having a huge impact on how our industry works.

It is even, for the more savvy marketers of the world, becoming a key tool in maximizing budget efficiency – which is apt, considering over 70% of CMOs believe they lack sufficient budget to fully execute their 2023 strategy.

Now, as AI becomes more intelligent, the number of efficiencies it can unlock continues to rise. Not only can it help brands get the most out of their available resources and identify any areas of waste, but it can also help highlight new opportunities for growth and maximize the impact of your budget allocation.

The trick, however, is to veer away from the norm of using in-platform solutions with a one-size-fits-all approach and create your own, bespoke solutions that are tailored to your business needs.

 

Pitfalls of in-platform solutions

In-platform solutions aren’t by any means a bad thing. In fact, built-in AI tools have become increasingly popular, owing to their ease of integration, user-friendly interfaces and minimal set up requirements. They come pre-packaged with the platform, offering the user the ability to leverage AI technologies without the need for in-depth technical expertise or the upfront cost of building a solution from scratch.

However, the streamlined and accessible nature of in-platform AI solutions comes at the expense of complexity and customization. They are designed to serve a broad user base, but for the most part are built using narrow AI solutions with predefined features and workflows.

This makes them great for assisting with common AI tasks, but they lack the flexibility to tailor functionality towards unique business requirements or innovative use cases, limiting the potential efficiencies and cost savings that can be unlocked. Additionally, if a business’ competitors are using the same platform, they are probably using the same AI solution, meaning any strategic advantage gained from these will be reduced.

Bespoke AI solutions, on the other hand, may carry a higher initial investment – but can offer a significantly more attractive ROI over a short amount of time.

 

Why customized and adapted AI is the key

The difference between bespoke AI and in-platform solutions is similar to that between home cooked food and a microwave meal. Yes, it is more time consuming to prepare, and yes it likely carries more of an upfront cost, but the end result is going to be far more appealing and will carry more long-term value (financially… not nutritionally).

That’s because bespoke solutions, by nature, will have been tailored to address your brands specific needs and challenges. These custom-built tools allow for much greater efficiencies by streamlining workflows across different channels, automating more complex tasks, and providing deeper, more relevant insights.

The increased level of optimization can significantly improve productivity and reduce operational costs over time, offering a higher ROI. The increased flexibility of bespoke AI also allows brands to implement innovative use cases that can significantly differentiate them from their competitors.

The data analyzed can be specifically chosen to match business requirements, as can the outputs of the AI tool, providing a significant advantage when understanding and acting on the insights provided.

Additionally, these tools are, by nature, more scalable. They can be updated, upgraded and expanded as needs change, ensuring they continue delivering value as the business grows. They can also be designed to integrate with any existing IT infrastructure, from CRM systems and databases to marketing platforms and sales tools – leading to more efficient and effective decision-making.

 

Managing finances with AI

It’s no secret that AI in marketing automation has, and will continue to, revolutionize the way marketing is done. It has a bright, if slightly terrifying, future and can help CMOs to unlock new efficiencies, maximize the impact of their budgets and increase their ROI. And as this technology becomes more advanced, its impact will only increase.

But we already know that…and so does everyone else.

So, in order for businesses to make themselves stand out from the crowd , they must look to fully adopt the power of AI. Creating a customized and unique AI solution could be the way to set yourself apart from your competitors. A bespoke AI tool can provide brands and businesses with features unique to them and their business needs. As a result, companies will benefit from more useful data and better results to make more data-driven decisions for their business. Ultimately, this will help brands to maintain a competitive edge over their competitors, deliver ROI and most importantly optimize their budgets.

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