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HOW AI IS TRANSFORMING WORKPLACE LEARNING AND DEVELOPMENT

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WORKPLACE

Darren Hockley , MD at DeltaNet International

We’ve come a long way since the early days of workplace e-learning. From the text-only prototypes of the 1980s, to the advent of multimedia capability in the ‘90s, and then mainstream internet, flash video, and HTML5 in the 2000s.

The demand for more innovative, relevant, and engaging workplace training has meant e-learning has continued to be disrupted and reimagined by technological advancements – a fact that’s almost certainly responsible for the medium’s longevity.

e-learning’s rise as the preferred training choice of many organisations today seems to fly in the face of its original sceptics – many of whom spoke out against its detraction from ‘human’ experience, or else called it ‘homogeneous’, suggesting it lacked the personal touch a real-life trainer could provide.

Whilst today there are many well-documented benefits of e-learning, the industry’s innovators have continued to take these issues seriously and address them in earnest. Design-techniques like immersive and scenario-led learning, e.g., were created to encourage critical thinking and problem-solving skills amongst digital learners.

The issue of personalisation also began to be addressed as Learning Management Systems (LMSs) evolved. Using pre-assessment tools, the systems could highlight gaps in knowledge and create personal development plans for each learner. Indeed, it was (and I believe, still is) this shift away from a ‘one-size-fits-all’ approach to workplace training that informed the intelligent e-learning of 2020 and beyond.

 

A New Learning Experience

Today’s Learning Platforms are very different from the ones used even a few years ago. One major difference being the incorporation of artificial intelligence and machine learning into their application – an innovation that was largely enabled by the Experience API, or xAPI.

xAPI revolutionised the way data is collected and analysed, allowing learning systems to build a much more comprehensive picture of organisational learning. One reason for this is that xAPI – unlike its predecessor, SCORM – is not limited to e-learning courses or an LMS. It can collect and aggregate data across multiple sources and track learning experiences wherever they take place, both on and off the learning platform.

Any learning activity users undertake (e.g. articles accessed on knowledge bases, queries submitted to helpdesks, documents shared on collaboration platforms, information input into performance management portals, quizzes taken and re-taken, online searches performed, and so on) is recorded as a statement and saved inside of a Learning Record Store (LRS) unique to each user. This data helps create comprehensive learning pathways for each employee – and it also helps to inform the learning design of the future.

 

Here are some of the ways AI is transforming corporate learning and development: 

Personalisation

There’s a reason many e-learning companies no longer refer to their learning platforms as a learning management system or ‘LMS’; the name doesn’t quite cut it anymore. These days you’re much more likely to hear about ‘learning experience platforms’ or LXPs.

LXPs mark a move away from formal, externally managed learning systems and concentrate instead on delivering a learning experience built around the user and their predisposition for learning.

Like LMSs before them, LXPs are built to deliver learning content, but they are also built to be able to learn themselves. This type of artificial intelligence is known as machine learning.

Driven by xAPI’s ability to aggregate data, today’s LXPs can track and respond-to user behaviour (i.e. what learning style they prefer, what they tend to search for, how long they spend learning, how many attempts it takes to pass, and etc.) and deliver content it infers meets our goals, interests, and preferences.

As you can imagine, this process creates highly personalised learning environments that are vastly different from the e-learning of yesteryear.

 

Learning in the Flow of Work

More sophisticated still, LXPs are being built to interrogate what users are working on and look for learning opportunities to keep the workflow on track. This is a development known as ‘learning in the flow of work’ and it is designed to support work, rather than distract from it.

Rather than stopping work to attend a seminar or sift through an hour-long course looking for vital information, LXPs will simply be able to suggest snippets of information/short videos as we work to help clear up areas of confusion – allowing employees to get on with the task at hand.

The same logic applies to the LXP’s ability to curate lengthier content if necessary. For example, if the system identifies a reoccurring knowledge gap. By pre-empting a learning requirement, the platform will be able to filter and curate content from multiple sources (e-learning course libraries, newspaper articles, journals – basically anything online) and create digital ‘textbooks’ made up of the type of learning content it knows we respond well to. These might be digestible study guides, videos, summaries, quizzes, podcasts, practice tests, gamified challenges, and so on.

 

Measure the Impact of Learning

Of course, no e-learning is perfect, and quality control is another area AI can help us out with. For instance, AI can explore the efficacy of learning content by analysing learner performance data.

It can, for example, identify instances where significant groups of learners have failed to answer a question correctly and interrogate their data to find commonalities amongst them. Perhaps they have all overlooked a specific course update or learning material that the LXP can then prioritise and promote to close the knowledge gap.

Alternatively, it’s possible the course content itself requires further attention from its developers and designers. The issue could be misleading or outdated information, or confusion over some instruction given in the course itself. Artificial intelligence expedites this investigation process and completes necessary data analysis without human bias or emotion impacting the results.

It’s in this way that I see AI complementing rather than replacing human experience when it comes to corporate L&D. In the future, I see AI redefining our understanding of personalised workplace training and envision it enabling e-learning suppliers to be more creative, more progressive, and – dare I say it – more human than ever before.

 

Darren Hockley is MD at DeltaNet International. The company specialises in creating engaging compliance and health and safety e-learning, and provides an intelligent learning platform, for businesses around the globe.

Business

Accounting Automation in the Future

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Accounting automation is the process of streamlining repetitive tasks in financial processes. For example, some processes like invoicing are time-consuming and repetitive. Automation can reduce manual labor and save businesses both time and money. Also, it helps improve accuracy, reduces errors, and provides more accurate financial reporting.

Accounting automation in the future will be increasingly important for businesses to stay competitive. But every new change comes with both advantages and challenges. Let’s dive in to get ready for this future trend.

 

Potential Future Benefits of Accounting Automation

Increased Efficiency and Cost Savings

Accounting automation is a great way to increase efficiency and cost savings. For example, AI bookkeeping uses advanced algorithms to automate many accounting tasks. So, companies can track expenses, prepare financial reports, and more using AI.

It reduces the time needed for manual entry. So, businesses can spend fewer labor hours on tedious processes. They can increase efficiency by freeing up resources for more strategic work. It also helps reduce errors and inconsistencies associated with manual processes. So, the cost of compliance is lower because of greater accuracy.

 

Improved Accuracy and Reliability

Accounting automation can improve accuracy and reliability in accounting processes. For example, Automating bank reconciliation is less prone to errors from human mistakes or miscalculations. You can automate the process to identify discrepancies between the bank statement and accounting records. It helps to ensure that financial reports remain accurate and reliable. So businesses can take corrective action faster than processing data manually.

 

Streamlined Business Processes

Streamlined business processes involve eliminating unnecessary steps, reducing paperwork, and automating repetitive tasks. This allows businesses to focus on higher-value activities, such as developing new products, improving customer service, and developing strategic plans for the future.

 

Making a Better Decision

Accounting automation can enhance decision-making in 3 ways.

1. It enables businesses to access real-time information from multiple systems. So they can identify trends for better decision-making.
2. Automated accounting also helps with forecasting, budgeting, and auditing tasks. It enables businesses to be more proactive in their decision-making processes.
3. Also, automated accounting tools can integrate with enterprise resource planning (ERP) systems. They can manage data across the enterprise and make concise decisions that are favorable to the company as a whole.

 

Increase Customer Satisfaction

Accounting automation can help businesses increase customer satisfaction by streamlining their processes and providing a more efficient customer experience. For example:
4. Automated accounting systems can automate tedious manual tasks such as invoicing, data entry, and payroll processing. This allows businesses to focus on other aspects of their operations that are more important for customer service.
5. Automated accounting systems can also provide customers with more accurate and timely financial information. The information can help them make better decisions about their finances.
6. Also, accounting automation enables businesses to respond quickly to customer inquiries. It helps reduce wait times and improve the overall customer experience. So, you can build better relationships with their customers.

 

Improved Accessibility

Accounting automation takes place online or comes with cloud-based solutions. So, you can access your information and do your job from anywhere instead of being confined to one spot.

 

Challenges to Implementing Accounting Automation in the Future

Cost of Technology Infrastructure Upgrades

Automating an accounting system often requires businesses to invest in new hardware and software, such as servers and other associated equipment. These upgrades come with a hefty price tag that may be difficult for small businesses to afford.

There are also extra costs, such as installation fees, setup charges, software licensing fees, cloud storage costs, and maintenance fees.

 

Training Requirements for Staff Members

Accounting automation involves using advanced technology to automate certain processes. So, it creates a need for trained staff members who can handle the new technology. Training requirements vary depending on the type of software used.

Some common training includes record-keeping procedures, software applications, and troubleshooting skills.

 

Regulatory Compliance Issues

Accounting automation can be a time-saver, but it also requires firms to be aware of the applicable rules and regulations. Companies must ensure that their automated systems are compliant with relevant laws and regulations such as Generally Accepted Accounting Principles (GAAP), International Financial Reporting Standards (IFRS), and other applicable accounting standards.

Besides, they must also comply with legal requirements related to taxes, financial statements, and other reporting obligations.

So, businesses must consider the complexities of regulatory compliance when automating accounting.

 

Security and Data Protection Concerns

As businesses move their accounting processes to the cloud, they are exposed to a wide range of potential security risks. Data breaches can cause significant damage to the business’s financial and reputational integrity. Besides, the complexity of automated accounting systems can make it difficult to identify and detect suspicious activities or errors in the system.

To ensure data is kept secure, businesses must have strong measures in place to protect against unauthorized access, encryption, and regular backups of data.

Furthermore, companies must train their staff on the proper use of the system. It helps staff to know how to protect confidential information from being accessed or misused by unauthorized personnel.

Businesses may also need an experienced IT team to monitor and maintain the system to keep up with any changes or updates for optimal performance.

 

Final thoughts

Accounting automation has come a long way in the past few decades. It is likely to continue to advance in the future. As technology continues to evolve, more businesses will likely begin taking advantage of automation in their accounting processes. So, businesses should be aware of the potential challenges and prepare to stay competitive.

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Three ways data can help financial organisations thrive in today’s economy

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By Rinesh Patel, Global Head of Financial Services, Snowflake

 

Financial organisations are caught in the middle of an ever-evolving landscape caused, in part, by emergent fintechs, shifting consumer expectations and increased regulatory change. Businesses are therefore turning to their data, re-imagining how they collect, process and analyse it, to drive growth and opportunity.

Despite this intention though, firms can often find themselves overwhelmed with the amount of data at their fingertips. Data tends to reside in individual departments that have no secure, efficient way of sharing it with other teams, creating silos of information. When teams need to collaborate, organisations are faced with additional costs and complexities in the movement of that data. The current infrastructure used by many financial institutions is not able to support the changing requirements of the industry, where data is the lifeblood.

Rinesh Patel

Firms looking to harness their data should leave behind their outdated legacy architecture and implement an enterprise data strategy with a cloud-native platform. They can reposition themselves to accelerate time to market and value, with differentiated products and improved client offerings to gain a critical competitive advantage. Here are three ways that financial services are using better technology and enhanced data management to add business value.

 

Adhering to regulatory requirements

The volume of global regulations and reporting obligations has risen exponentially in the past decade, creating greater complexity and security challenges for firms capturing and processing data. Many of these regulations were taken by supervisors to ensure financial stability after the financial crisis of 2008. Regulators have greater expectations of firms with the aim of risk mitigation and transparency. With advanced technologies facilitating data capture, storage and analysis now available, supervisory bodies are also keen in part, to ask for additional disclosures because it’s now possible to demand more documentation and seek greater transparency.

The landscape of differing interpretations, overlapping regulatory requirements across asset classes and geographies and strict, even unrealistic deadlines for implementation have forced customers to take tactical quick-fix solutions, elevating operational risk and the chance of regulatory fines. Compliance departments have therefore been spending years building reporting processes, managing inconsistent data sets, maintaining ageing data stores and importantly overseeing differing levels of governance, adding more cost and complexity to the task at hand. For a large multi-segment global bank or asset manager this fragmented and manual approach to data management and analysis is not sustainable given the scale of processes and multi-geographic considerations that they have to comply with.

As regulators continue to push the long-term structural change agenda, financial services must now ready themselves to meet more robust reporting requirements to comply with the ever-changing regulatory landscape. The objective is to simplify and better manage data across teams with the governance and security provided by technological capabilities now offered through modern cloud capabilities to drive needed reporting. This will allow firms to replace old and inconsistent data with a centralised data architecture, providing a single source of truth. The time and cost reduction from data sourcing, ingestion, and the normalisation of data for analysis, can shrink to significantly streamline reporting processes.

 

Customer 360 experience

Consumers provide financial institutions with a vast amount of information, ranging from their banking habits to their behavioural preferences. Financial organisations have traditionally been slow to tap into the totality of this information to provide a better experience for customers.

The quest to provide greater visibility and a 360-degree view of customer behaviour is at the core of financial services organisations’ priorities. Customers want smooth, easy digital experiences that can speak to their desire for ease of use and convenience. This is seen in the ways virtual banking consumers have opted for technologies that are simple to interact with, self-directed and frictionless when it comes to carrying out digital transactions. New regulations, such as PSD2 and rules around open banking have also primed customers to expect more.

The challenge for legacy institutions is to bring the ease and usability of digital-first platforms with the sophistication of a major, global provider. Tapping into the full spectrum of data created by consumers is central to a successful transition.

Wealth advisory, investment management professionals are increasingly looking at data capabilities to support ongoing relationship management with their clients. Using data to understand customers in this way helps banks to successfully move customers up the wealth value chain. Wealth management organisations can digitise the investment process – from finding customers to managing accounts, and offering bespoke plans. Effective use of data in this sector can free up time for advisors, helping to retain key customers and charge higher commission levels thanks to a new level of personalised service.

 

Developing an effective ESG strategy

Environmental, social and corporate governance (ESG) considerations have grown in significance with increasing stakeholder pressures, driving a response by firms to prioritise their sustainability agenda. To understand, evaluate the problem and take action, firms need access to technology providing holistic ESG data capabilities and solutions, with performance and scale.

Financial firms are amassing large data sets from the public sector, including government reports, scientific bodies and private sector reports, to understand and address the climate challenge. Businesses are moving with urgency to acquire robust data sets, to meet ESG criteria and sustainability metrics needed to evaluate impact and make progress against their own commitments. There are several pervasive business use cases for teams experiencing ESG data challenges, including portfolio construction, financial planning and regulatory reporting that will require an effective ESG data management strategy.

Ever present challenges in the ingestion, standardisation, and sharing of ESG data will be at the forefront of every organisation – as they process the magnitude of the challenge and transform their operations to address the issue. With cloud-native solutions, firms can use ready-to-use query data across established marketplace data sets. They can then share that data across teams in a secure, governed way – with greater speed to market. Organisations can meet the need for scalable analytics, and access a data ecosystem to build their own proprietary ESG applications for different user and workflow requirements.

 

A business fit for the future 

With data cloud solutions, businesses can effectively analyse the vast amounts of data available to them, equipping them to meet the ever-changing financial landscape. Leaving behind legacy systems will open up a multitude of opportunities and benefits that will drive business growth. This includes developing a 360 view of the customer, improved data governance and the opportunity to use data to support an effective ESG strategy. Without the ability to harness data through the cloud, companies will get left behind the competition and struggle to meet the standards that modern consumers expect.

 

 

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