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The pros, cons, and best practices when it comes to using generative AI

Colin Redbond, SVP Product Management, SS&C Blue Prism

We’ve all heard about growing interest in generative AI, especially following the release of ChatGPT. The artificial intelligence (AI) large language model, and ones like it, have the potential to revolutionize business operations and accelerate digital transformation journeys. Those that get ahead of this trend are set to gain a significant competitive advantage, with generative AI showing no signs of disappearing anytime soon.

While historically, AI projects have been long, expensive, and complex, generative AI has the potential to reduce time to value for digital transformation initiatives and make advanced technologies more accessible to a greater cross section of people thanks to its ease of use and learning capabilities.

How generative AI supports digital transformation

Digital transformation initiatives went into hyperdrive following the pandemic, but most organizations have yet to maximize business outcomes with their current automation plans. Intelligent automation combines technologies, like generative AI, robotic process automation (RPA), business process management, etc., to reengineer processes and drive business outcomes.

So, what’s the value of generative AI? Its range of capabilities and accessibility is unprecedented, marking an exciting time for the automation space and any sector standing to benefit from advancements in natural language processing, including healthcare, finance, and customer service. However, generative AI is still limited, in a sense, to its own domain knowledge. 

When you combine its unique capabilities with the power of intelligent automation, the impacts for digitalization are extraordinary. Generative AI can be used to automate tasks that were previously only possible for humans to perform, such as generating new marketing copy, designing new product prototypes, or creating personalized content for each customer. It can suggest automations and enable a greater cross section of workers to initiate the development of automations thanks to its ease of use. Automations can then be designed within designated governance parameters and best practices.

By automating these tasks, employees can reduce their workload, supporting work-life balance, while also increasing their efficiency, reducing company-wide costs, and improving the accuracy and quality of their output. Employees work with generative AI to deliver superior results.

When it comes to creative work, humans add color and empathy, which technology can only try to mimic. Generative AI gives them a starting point, helps with idea generation, etc. Human workers provide their uniquely human abilities to read between the lines and their emotional empathy for which AI is not substitute.

For many people, when they think “generative AI,” they think about written content or even AI art, but the use cases for generative AI relate to the day-to-day operations of most office workers.

For example, automated emails can exhibit a greater degree of personalization and improve resolution times. For more complex or high-level emails, generative AI can be used to draft an adequate, personalized email, with all needed information, and a human can then review and tweak if needed.

A similar process can unfold with contact center processes, generative AI bots can progress customer communications significantly before needing to loop in a human employee – if they need to be looped in at all for simple enquiries. This ensures human employees’ time is used effectively and as many customers as possible are being serviced, especially with generative AI bots being able to work around the clock. Error handling is improved with error messages providing context that enables immediate resolution.

Intelligent document processing (IDP) solutions, which use a combination of optical character recognition and AI to extract information that is locked away in documents, are enhanced by generative AI capabilities. This is especially important for financial and healthcare services. Generative AI’s understanding and learning features better equip it to contend with unstructured data, an area that has been a weak point for IDP solutions, which have been confined to structured and semi-structured documents at best.

Generative AI can also help improve the overall performance of intelligent automation systems by allowing them to adapt and learn over time by analyzing the results of previous tasks and using that data to generate new content or output.

The need for governance and risk management to unlock the potential of AI

However, businesses need to make certain considerations before they explore adding generative AI to their toolkit to accelerate digital transformation, since its outputs can have a significant impact on a company’s reputation, revenue, and legal liabilities. A clearly defined corporate governance risk management strategy and set of operating principles around this need to be developed. Done right, generative AI can support an automation strategy that is even more innovative, cost-effective, and productive than anything we have seen before.

Reasons why governance and risk management considerations are important when using generative AI:

  • Help ensure AI-generated content does not violate intellectual property, privacy, or other laws
  • Make sure use of generative AI aligns with your organization’s ethical principles
  • Maintain your organization’s quality standards and confirm outputs are consistent with expectations
  • Ensure the right information is used for the right purposes to protect sensitive information and privacy

How to develop a governance and risk management strategy

A clearly defined strategy and concomitant operating principles maximize the benefits of generative AI while mitigating any outfall. Developing a strategy involves several key steps:

  1. Define the scope: This includes the types of content you will be generating, the data you will be using, and the intended use cases for the content. This helps with identifying the specific risks and governance requirements that apply to your initiatives.
  2. Identify risks: These may include legal risks such as infringing on intellectual property, ethical risks such as bias in generated content, and security risks such as the potential for data breaches. You may need to engage with legal and compliance experts to identify all potential risks.
  3. Establish governance requirements: Based on the risks you’ve identified, establish governance requirements that will mitigate those risks. These may include policies and procedures for data handling, content review, and compliance with regulations.
  4. Develop a risk management plan: Outline how your organization will mitigate and manage risks. This may include risk assessments, monitoring, and regular reviews of governance practices, as well as processes for identifying and addressing any issues that arise.
  5. Train employees: It’s important to train employees on governance and risk management practices. This may include training on data handling, content review, and compliance with regulations. Make sure all employees who will be working with generative AI understand the risks and their responsibilities for mitigating those risks.
  6. Monitor and review: Monitor and review your governance and risk management practices on an ongoing basis. This will help you identify any gaps or issues that need to be addressed and ensure that your practices remain effective over time.

Like all advanced technologies, generative AI’s impact is positive – so long as you take the steps necessary to ensure you’re using it the right way. There’s no turning back the train, generative AI is here to stay – full steam ahead. The best approach is going to be to embrace it with care and work with providers when it comes to decision making around implementation. The possibilities behind generative AI are exciting – so let’s work to get it right and make it a force for good.

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Business

Conflicting with compliance: How the finance sector is struggling to implement GenAI

By James Sherlow, Systems Engineering Director, EMEA, for Cequence Security

GenerativeAI has multiple applications in the finance sector from product development to customer relations to marketing and sales. In fact, McKinsey estimates that GenAI has the potential to improve operating profits in the finance sector by between 9-15% and in the banking sector, productivity gains could be between 3-5% of annual revenues. It suggests AI tools could be used to boost customer liaison with AI integrated through APIs to give real-time recommendations either autonomously or via CSRs, to inform decision making and expedite day-to-day tasks for employees, and to decrease risk by monitoring for fraud or elevated instances of risk.

However, McKinsey also warns of inhibitors to adoption in the sector. These include the level of regulation applicable to different processes, which is fairly low with respect to customer relations but high for credit risk scoring, for example, and the data used, some of is in the public domain but some of which comprises personally identifiable information (PII) which is highly sensitive. If these issues can be overcome, the analyst estimates GenAI could more than double the application of expertise to decision making, planning and creative tasks from 25% without to 56%.

Hamstrung by regulations

Clearly the business use cases are there but unlike other sectors, finance is currently being hamstrung by regulations that have yet to catch up with the AI revolution. Unlike in the EU which approved the AI Act in March, the UK has no plans to regulate the technology. Instead, it intends to promote guidelines. The UK Financial Authorities comprising the Bank of England, PRA, and FCA have been canvassing the market on what these should look like since October 2022, publishing the results (FS2/23 – AI and Machine Learning) a year later which showed a strong demand for harmonisation with the likes of the AI Act as well as NIST’s AI Risk Management Framework.

Right now, this means financial providers find themselves in regulatory limbo. If we look at cyber security, for instance, firms are being presented with GenAI-enabled solutions that can assist them with incident detection and response but they’re not able to utilise that functionality because it contravenes compliance requirements. Decision-making processes are a key example as these must be made by a human, tracked and audited and, while the decision-making capabilities of GenAI may be on a par, accountability in remains a grey area. Consequently, many firms are erring on the side of caution and are choosing to deactivate AI functionality within their security solutions.

In fact, a recent EY report found one in five financial services leaders did not think their organisation was well-positioned to take advantage of the potential benefits. Much will depend on how easily the technology can be integrated into existing frameworks, although the GenAI and the Banking on AI: Financial Services Harnesses Generative AI for Security and Service report cautions this may take three to five years. That’s a long time in the world of GenAI, which has already come a long way since it burst on to the market 18 months ago.

Malicious AI

The danger is that while the sector drags its heels, threat actors will show no such qualms and will be quick to capitalise on the technology to launch attacks. FS2/23 makes the point that GenAI could see an increase in money laundering and fraud through the use of deep fakes, for instance, and sophisticated phishing campaigns. We’re still in the learning phase but as the months tick by the expectation is that we can expect to see high-volume self-learning attacks by the end of the year. These will be on an unprecedented scale because GenAI will lower the technological barrier to entry, enabling new threat actors to enter the fray.

Simply blocking attacks will no longer be a sufficient form of defence because GenAI will quickly regroup or pivot the attack automatically without the need to employ additional resource. If we look at how APIs, which are intrinsic to customer services and open banking for instance, are currently protected, the emphasis has been on detection and blocking but going forward we can expect deceptive response to play a far greater role. This frustrates and exhausts the resources of the attacker, making the attacks cost-prohibitive to sustain.

So how should the sector look to embrace AI given the current state of regulatory flux? As with any digital transformation project, there needs to be oversight of how AI will be used within the business, with a working group tasked to develop an AI framework. In addition to NIST, there are a number of security standards that can help here such as ISO 22989, ISO 23053, ISO 23984 and ISO 42001 and the oversight framework set out in DORA (Digital Operational Resilience Act) for third party providers. The framework should encompass the tools the firm has with AI functionality, their possible application in terms of use cases, and the risks associated with these, as well as how it will mitigate any areas of high risk.

Taking a proactive approach makes far more sense than suspending the use of AI which effectively places firms at the mercy of adversaries who will be quick to take advantage of the technology. These are tumultuous times and we can certainly expect AI to rewrite the rulebook when it comes to attack and defence. But firms must get to grips with how they can integrate the technology rather than electing to switch it off and continue as usual.

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Business

Recognising the value of protecting intellectual property early builds strong foundation for innovators

Innovation Manager at InnoScot Health, Fiona Schaefer analyses an essential facet of developing ideas into innovations

Helping the NHS to innovate remains a key priority during this period of recovery and reform. Even within the current cash-strapped climate, there is the opportunity to maximise the first-hand experience of the healthcare workforce and its knowledge of where new ideas are needed most.

Entrepreneurial-minded, creative staff from any discipline or activity are often best placed to recognise areas for improvement – the reason why a significant number of solutions come from, and are best developed with, health and social care staff.

NHS Scotland is a powerful driver of innovation, but to truly harness the opportunities which new ideas offer for development and commercialisation, the knowledge and intellectual property (IP) underpinning them needs to be protected. That vital know-how and other intangible assets – holding appropriate contracts for example – are key from an early stage.

Medical devices can take years to develop and gain regulatory approval, so from the outset of an idea’s development – and before revenue is generated – filing for IP protection and having confidentiality agreements in place are ways to start creating valuable assets. This is especially important when applying for patent protection because that option is only available when ideas have not been discussed or presented to external parties prior to application.

Without taking that critical initial step to protect IP, anyone – without your permission – could copy the idea, so anything of worth should be protected as soon as possible, making for a clear competitive advantage and ownership in the same sense as possessing physical property.

The common theme is that to be successful – and ultimately support the commercialisation of ideas that will improve patient care and outcomes – the idea must be novel, better, quicker, or more efficient than existing options. Furthermore, to turn it into a sound proposition worth investing in, it must also be technically and financially feasible. It isn’t enough to just be new and novel – the best innovations offer tangible benefits to patient outcomes and staff working practices.

Of course, even more so in the current climate of financial constraints, the key question of ‘Who will pay for your new product or service?’ needs to be considered up front as well.

Whilst development of a strong IP portfolio requires investment and dedicated expertise, when done well and at the appropriate time, then it is resource well spent, offering a level of security whilst developing an asset which can be built upon and traded. There are various ways commercialisation can progress and whilst not all efforts will be successful, intellectual property is an asset which can be licensed or sold to others offering a range of opportunities to secure a good return.

In my experience, however, many organisations including the NHS are still missing the opportunity to recognise and protect their knowledge assets and intellectual property early in the innovation pathway. This is partly due to lack of understanding – sometimes one aspect is carefully protected, whilst another is entirely neglected. In other cases, the desire to accelerate to the next stage of product development means such important foundational steps are not given the attention required for long-term success.

Good IP management goes beyond formally protecting the knowledge assets associated with a project, e.g. by patenting or design registration, however. When considered with other intangible assets such as access to datasets, clinical trial results, standard operating procedures, quality management systems, and regulatory approvals, it is the combination which will be key to success.

Early securing of IP protection or recognition of IP rights in a collaboration agreement, demonstrates foresight and business acumen. Later on, it can significantly boost negotiating power with a licensing partner or build investor confidence.

Conversely, omissions in IP protection or suitable contracts can be damaging, potentially derailing years of product development and exposing organisations to legal challenges and other risks. Failing to protect a promising idea can also mean commercial opportunities are missed, thus leading to your IP being undervalued.

Ideas are evaluated by formal NHS Scotland partner InnoScot Health in the same way whether they are big or small, a product, service, or new, innovative approach to a care pathway.

We encourage and enable all 160,000 NHS Scotland staff, regardless of role or location, to come forward with their ideas, giving them the advice and support they need to maximise their potential benefits.

Protecting the IP rights of the health service is one of the cornerstones of InnoScot Health’s service offering. In fact, to date we have protected over 255 NHS Scotland innovations. Recently these have included design registration and trademarks for the SARUS® hood and trademarks for SCRAM®, building and protecting a recognised range of bags with innovative, intuitive layouts. Spin outs such as Aurum Biosciences meanwhile have patents underpinning their novel therapeutics and diagnostics.

We assist in managing this IP to ensure a return on investment for the health service. Any revenue generated from commercialising ideas and innovations from healthcare professionals is shared with the innovators and the health board through our agreements with them and the revenue sharing scheme detailed in health board IP and innovation policies.

Fundamentally, we believe that it is vital to harness the value of expertise and creativity of staff with a well-considered approach to protecting IP and knowledge input to projects from the start.

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Business

Time is running out: NHS and their digital evolution journey

By Nej Gakenyi, CEO and Founder of GRM Digital

Many businesses have embarked on their digital evolution journey, transforming their technology offerings to upgrade their digital services in an effective and user-friendly way. Whilst this might be very successful for smaller and newer businesses, but for large corporations with long-standing legacy infrastructure, what does this mean? Recently the UK government pledged £6bn of new funding for the NHS, and the impact this funding and investment could have if executed properly, could revolutionise the UK public healthcare sector.

The NHS has always been a leader in terms of technology for medical purposes but where it has fallen down is in the streamlining of patient data, information and needs, which can lead to a breakdown in trust and the faith that the healthcare system is not a robust one. Therefore, the primary objective of additional funding must be to implement advanced data and digital technologies, to improve the digital health of the NHS and the overall health of the UK population, as well as revitalise both management efficiency and working practices.

Providing digital care

Digitalisation falls into two categories when it comes to the NHS – digitising traditionally ‘physical’ services like offering remote appointments and keeping electronic paper records, and a greater reliance on more innovative approaches driven by advances in technology. It is common knowledge that electronic services differ in GP practices across the country; and to have a drastically good or bad experience which is solely dependent on a geographical lottery contradicts the very purpose of offering an overarching healthcare provision to society at large.

By streamlining services and investing in proper infrastructure, a level playing field can be created which is vital when it comes to patients accessing both the care they need and their own personal history of appointments, GP interactions, diagnoses and medications. Through this approach, the NHS focus on creating world-leading care, provision of that care and potentially see waiting lists decrease due to the effective diagnosis and management enabled by slick and efficient technology.

This is especially important when looking at personalisedhealth support and developing a system that enables patients to receive care wherever they are and helps them monitor and manage long-term health conditions independently. This, alongside ensuring that technology and data collection supports improvements in both individual and population-level patient care, can only serve to streamline NHS efforts and create positive outcomes for both the patient and workforce.

Revolutionising patient experiences

A robust level of trust is critical to guaranteeing the success of any business or provision. If technology fails, so does the faith the customer or consumer has in the technology being designed to improve outcomes for them. An individual will always have some semblance of responsibility and ownership over their lives, well-being and health. Still, all of these key pillars can only stand strong when there is infrastructure in place to help drive positive results. Whilst there may be risks of excluding some groups of individuals with a digital-first approach, technology solutions can empower people to take control of their healthcare enabling the patient and NHS to work together. Tandem efforts between humans and technology

Technology must work in tandem with a workforce for it to be effective. This means the NHS workforce must be digitally savvy and have patient-centred care at the front and centre of all operations. Alongside any digital transformation the NHS adopts to improve patient outcomes, comes the need to assess current and future capability and capacity challenges, and build a workforce with the right skills to help shape an NHS that is fit for purpose.

This is just the beginning. With more invtesement and funding being allocated for the NHS this is the starting point, but for NHS decision-makers to ensure real benefits for patients, more still needs to be done. Effective digital evolution holds the key. Once the NHS has fully harnessed the poer of new and evolving technologies to change patient experiences throught the UK, with consistent communication and care, this will set the UK apart and will mark the NHS has a diriving example for accessible, digital healthcare.

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