The State of Play  

The ongoing rollout of SMETS2 meters is expected to run until the end of 2025. This rollout subjects all suppliers to legally binding installation targets and was set about in 2021 following the surpassed deadline of the original obligation. This obligation ensures suppliers use ‘all reasonable steps’ to roll out smart metering to their domestic and small non-domestic customer base. The impact of this prolonged rollout has resulted in the loss of customer trust, which has also been exacerbated by the installation of non-communicating smart meters due to poor signal coverage (estimated to make up 12%).  

This next phase of the rollout will work to correct these past issues that previously limited customer buy-in. The planned upgrade from the 2G/3G network, driven by the Smart DCC (Data Communications Company), will improve the signal coverage of smart meters and help eliminate issues around outdated or non-communicating meters. We foresee that this upgrade could help to reassure customers who feel that before any new advancements are made, the priority must be that all currently installed devices are working and communicating as intended. This potential upgrade could not only rectify long-standing problems with connectivity but also encourage those disenfranchised to buy into new smart metering technologies which rely upon customer consent. 

What future benefits could SMETS2 meters bring to customers? 

We have already seen that the introduction of SMETS2 meters has brought many customer benefits. These include ease of switching, more accurate billing, and, more recently, new wave tariffs geared towards helping customers save on their energy bills. Modern tariffs, such as time of use and EV agreements, are reliant upon accurately receiving half-hourly readings, which are better supported by the newer SMETS2 industry standard.  

Future benefits could be achieved through the development of a digital spine which would act as an organisation’s digital ecosystem, connecting different systems, applications and data sources. This centralised source would receive energy usage data from SMETS2 meters and could allow suppliers to create a flexible, agile, and scalable foundation for digital transformation initiatives. However, customer data could only be transformed after gaining individual consent from customers. 

Enhancing smart metering capabilities could bring immediate benefits to customers. Initially, customers could access advanced appliance-level billing, which may encourage usage changes that could result in a financial benefit. Every active household appliance has a unique usage signature that can be used to highlight specific usage data for the customer in their monthly bill. Should the customer provide consent to their supplier to accessing and centralising their usage information, we could see this access transform into a more interactive network. Through a device’s unique usage signature, there is the capability for SMETS2 meters to identify non-critical devices that are on standby and remotely turn these off during peak consumption times when wholesale energy costs are at their highest. This would dramatically reduce the cost of energy across the entire country, resulting in lower tariff prices.   

Standardising smart metering with the rollout of SMETS2 meters will bring improved resource efficiency to the utilities industry and enhance customer experience. When used resourcefully, SMETS2 meters already allow insights to be retrieved for both the benefit of the supplier and the customer, which allows for more transparency around accurate billing and the environmental impact of usage. In the future, however, we expect to see the industry taking further advantage of the capabilities that SMETS2 meters can provide with the customer’s consent. With new advances, there is the opportunity to create an interactive smart space for suppliers and customers while accelerating the UK’s ability to reach Net Zero. 

If you’re interested in discussing how an experienced outsourcer can transform your supply business, please get in touch, or connect with us on LinkedIn.

However, world-leading analysts Gartner recently shared their insights with CX Today, stating that companies that rely too heavily on AI will be opening the door to serious risks. According to Emily Potosky, the Senior Director of Research for Gartner’s Customer Service and Support Practice, even the most advanced AI algorithms still have limitations. They shouldn’t be seen to replace humans but instead as a solution to augment and empower existing teams. 

The Impact of AI on Customer Service Jobs 

Fears that AI will replace human beings are increasingly common, now that AI is more advanced than ever, concerns are growing with the rise of deep learning models, generative AI, and neural networks. 

Up to 3 in 4 Americans now believe AI will reduce the number of jobs available to workers, and customer service agents feel more pressure than most. Countless contact centre solutions are enabling companies to reduce their reliance on human skill sets with generative AI bots and automated workflows.  

According to one HFS Research report, customer service and sales were two of the top departments most likely to be impacted by AI’s transformation. 

However, while AI solutions can accomplish a lot in the contact centre, analysts like Potosky believe some level of human assistance will always be crucial. She’s not alone in this opinion. In a recent Gartner survey, around 61% of business executives believe AI won’t reduce their human headcount in the contact centre by more than 5%.  

The Limitations of AI in Customer Service 

We’ve discussed previously how AI tools can augment human professionals, helping them streamline and optimise repetitive tasks.  

For instance, McKinsey suggests up to 64% of data collection activities can be automated, but that doesn’t eliminate the need for a data scientist for bot training, data analysis, and creativity.  

Similarly, in the customer service landscape, AI tools can creatively respond to common questions, help customers troubleshoot problems, and even complete tasks like processing transactions. 

However, they can’t understand emotions and context to the same degree as humans, which leads to the potential for misinterpretations. They also can’t show empathy, creatively develop new ways to solve problems based on their experiences or reassure a concerned customer. Not to mention, there are various other, more technical challenges to consider. Replacing humans with bots entirely in the contact centre would require a great deal of technical knowledge, development expertise, and constant monitoring. 

Augmentation 

At DDC OS, we believe in giving people the tools and resources they need to be as effective as possible. Like Gartner and many others, we see AI as a way to enhance human performance, not replace it.  

As Potosky noted to CX Today, AI tools can provide employees with the context, support, and guidance they need to excel in their roles. AI might reduce business leaders’ dependence on skills and expertise, widening the talent pool to engage in customer-facing work. This may mean that business leaders don’t need to hire as many additional staff members to handle spikes in call volume. However, it won’t eliminate the need for humans. Instead, it will help companies manage rising costs and challenges by: 
 

• Improving productivity: Rather than just replacing human agents with automated workloads, AI can be a helping hand that reduces stress and strain during busy seasons. It can help teams hit the ground running with the correct information when queries are escalated and accelerate the path to problem resolution. 
• Increasing retention: In an environment with rapid turnover, like the contact centre, AI can help reduce turnover risk. Companies could use AI to route “simpler” queries to new hires while they’re learning how to excel in their role.  
• Enhancing development: As customer expectations evolve, companies can use AI to deliver employees more personalised and effective training & development programs. Generative AI can be used to create training initiatives, deliver real-time coaching sessions, and identify opportunities for additional training. 

The Future of AI in the Contact Centre

Many organisations have already cut down their human teams, rushing to adopt AI simply because they want to keep up with the competition, without understanding the potential risks they’ll face.  

However, Gartner believes the increasing prominence of AI in the contact centre will lead to significant changes in the years ahead, influencing how companies adopt the technology. In the coming years it is likely the EU will update its consumer protection laws to give every customer the right to speak to a human. It’s certainly something that may occur if, AI at some stage leads to harm to a customer.  

Taking the Right Approach to AI Adoption

Ultimately, relying exclusively on AI to manage customer service processes can be a big risk. It risks the well-being of your customers and impacts your brand’s reputation and ability to retain consumers long-term. AI has limitations, from the common hallucinations of generative AI tools to the lack of empathy and sometimes biased behaviours that bots demonstrate. Despite this, Gartner believes that by 2028, a third of customer service interactions will involve autonomous agents. 

We believe there’s nothing wrong with embracing AI’s potential, particularly if it augments and supports your agents, increases employee engagement, and streamlines customer service interactions.  

To find out how we can help you, get in touch or follow us on LinkedIn. 

Knowing how to capture the right data and use it efficiently isn’t just helpful; it’s a necessity. Capturing and processing data still requires significant human intervention; whilst automation (in the form of Robotic Process Automation or RPA) and now AI continue to refine and enhance the process, it is yet to eradicate the need for data capture specialists.  

These technological advancements have certainly, over the years, helped to greatly increase processing volumes and the speed at which they are handled. They minimise inaccuracies and can even help identify patterns. The question is, what does all this mean for the future of data capture workforces?  

The Impact of AI on Data Capture Processes 

Data capture has long stood as a pivotal process in the realm of information gathering. It encompasses the collection of data from various sources (text, images, PDFs, voice recordings, and more). The primary objective is to collect valuable data and convert it into a format that can be processed, analysed, sold, and so much more. 

For years now, technological advancement has been revolutionising the data capture landscape. AI and automation tools have helped to drive an efficient, streamlined, and powerful process. In fact, the market for automatic data capture and identification tools is expected to grow to a value of $134.7 billion by 2032.  

How AI Improves the Data Capture Process

AI-driven solutions offer sophisticated capabilities for the extraction, recognition, and interpretation of data, potentially minimising human intervention.  

With artificial intelligence, companies can: 

• Improve data quality: With adaptive learning and machine learning tools, companies can benefit from systems that automatically adjust processes and improve the accuracy and quality of the data captured for assessment.  
• Translate raw data into insights: Data analysis is a complex process which requires significant expertise. While AI may not yet be able to match the skills of an experienced data analyst, it can help identify data and rapidly offer insights into potential trends and patterns, making the data captured by businesses more valuable.  
• Streamline complex data capture: AI tools can capture complicated data from a variety of structured, semi-structured, and unstructured documents. Pre-processing tools can even annotate key data fields and ensure data is labelled correctly for analysts.  
• Accelerate data capture: AI tools can capture data from systems, processes, and interactions in real time. This reduces the need for employees to spend endless hours creating notes and uploading documents into systems. Real-time tracking also ensures companies can maintain complete visibility into their operations.  
• Automate data validation: Manually validating data can be a time-consuming and error-prone process. AI-based solutions can enhance the processing workflow, using templates and pre-implemented methods to validate and identify data. The right tools can even ensure data is stored in the right place or sent to the correct teams.  

AI and Data Capture Experts: Better Together

So, if AI in the data capture landscape is so powerful, does it eliminate the need for data capture experts, such as data entry professionals and analysts? Simply put, the answer is a resounding no. While McKinsey suggests about 64% of data collection activities can be “automated”, many companies have discovered that the best results come from using AI tools and human skills in tandem.  

AI can certainly help data capture experts accomplish more in less time and even improve the accuracy of data capture strategies. However, data capture experts are still necessary for many parts of the process. For instance, AI can use machine learning to attempt to determine which forms of data need to be captured for your business, but it can’t effectively separate the best insights for your business goals from endless volumes of data points.  

AI tools also have a number of limitations. AI tools, despite their capabilities, do come with certain limitations. Even if they’re trained to detect and redact personally identifiable information from documents, there are instances where important details can be overlooked – particularly where the format of the data source varies or is inconsistent in structure. Some solutions can make mistakes when processing data, and others struggle to process certain forms of unstructured data with a high level of accuracy. Such limitations can have significant impacts on businesses when it comes to data protection regulations.  

Additionally, AI can only handle so much of the data collection and analysis process. It can collect data rapidly but can’t always check it for correctness or convert information into valuable formats, consider medical data or allergen information, for example. The accuracy of this data is essential; mistakes can be a danger to life. Can we, or would we, ever be inclined to trust that process to ever go ahead without human interaction? Many of our client projects use a blend of OCR technology and human agents in a hybrid solution, leveraging both the advantages of technology and the skills of a data capture specialist. For example, OCR might read a form/document/label image of a product, and a human data capture agent perform a QA check to ensure the data aligns with processing rules and third-party regulations and corrects any OCR errors, thus feeding back to the OCR system and improving the technology.  

Ultimately, AI is having a positive impact on the data capture landscape, just like it’s leading to progress in various other business operations and tasks. However, it’s simply a tool that can help to improve efficiency, scale data processes, and boost productivity. It’s not a replacement for human experts. AI should be seen as an assistant for data professionals.  

It should empower these experts to be more effective, efficient, and accurate in their roles than ever before, allowing them to unlock better results for businesses. While AI is a powerful tool for positive change, it’s not a magic solution for every data entry or management problem. If you’re planning on implementing AI into your ecosystem, don’t rush it. Consider what you want to achieve and engage with those delivering that task currently; they are the data experts and will have valuable perspectives and insights into the best use of AI technology and where it can play the most effective role. 

Take the time, consider the applications that can best benefit your business and teams, and make sure you’re leveraging the right combination of machine intelligence and human expertise.  

Why not engage with an expert data capture outsourcer who can help you identify the right approach to solve your challenges and unlock value in your data?  

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