The role of GenAI in reshaping how we interpret and leverage information cannot be overstated, and it is truly proving to be a differentiator, further separating early adopters and laggards. Traditionally, data has been siloed within specialist teams—scientists, engineers, and analysts—leading to bottlenecks that stifle agility and slow decision-making. By implementing GenAI products, businesses can break down these silos by enabling users at all levels to engage directly with data, putting data insights in the hands of every business user, engineer and application builder. When leveraging GenAI for data analytics, businesses are able to guide and automate certain tasks, cutting down the time this would normally take between different data teams, which in turn empowers users to be more productive and help employees serve themselves more efficiently.

Early adopters of GenAI for data analytics, like EcoLab and Verizon, are reaping significant rewards. EcoLab, a global sustainability provider, has trained GenAI on clients’ operational and financial data to quickly and effectively identify the best resources for them, reducing the cost of time and resources. This proves that companies who have already adopted the technology across their operations are seeing benefits such as democratised data access, the ability to deliver actionable insights rapidly and monitor the benefits which have the ability to transform the company. Through being able to leverage, analyse and make data-driven decisions quicker and more effectively through GenAI, employees can prioritise other operations, which accelerates a company’s growth and enables them to scale quicker.

As well as reporting operational benefits, companies are seeing a measurable return on investment with GenAI, reaffirming the technology as both a financial asset and a key competitive differentiator. For example, Verizon—the telecoms giant—has developed a “centre of excellence” which allows them to monitor the ROI they are receiving for GenAI, meaning they can constantly monitor the financial benefits they’re receiving from the technology, and where it’s proving to be most beneficial. When investments are made, it is important to show the value and return making it suitable for the bottom line.

Democratised data access for decision making across levels of a business enables businesses to grow, scale and become more efficient.  This was highlighted in a recent report by MIT sponsored by ThoughtSpot found that nearly half of adopters of GenAI for analytics anticipate a 100% or greater ROI within three years. By moving away from a centralised framework, businesses enhance accessibility to data, and improve performance of data-driven decisions, which is beneficial for businesses in the long-run.

It’s a fact that innovation and originality is also crucial to empower and drive businesses towards efficiency and growth. GenAI for data analytics plays an important role in business innovation as through extracting insights from multiple datasets, GenAI can build a complete and personalise of different customers’ behaviour and habits in an instant. When using traditional in-house teams, although they may be able to reach the same conclusion, they may struggle to do it as quickly. Whereas harnessing GenAI for data analytics means results are achieved quicker, and therefore more can be achieved each day.

In the same way that GenAI has been widely adopted across businesses, agentic AI is now rapidly making inroads into businesses, and already transforming everyday operations. Agentic AI is offering businesses a step beyond GenAI—through operating autonomously to perform tasks in a human-like way. Agentic AI uses large language models (LLMs) to manage multiple agents, which can handle a vast range of operations from data search and analysis to driving complex data-led decisions. The technology’s systems go beyond when it comes to automating end-to-end processes, reducing manual effort and increasing efficiency across operations, to the point where they are widely described as being human-like.

Just as companies have been integrating GenAI products, more companies are now beginning to integrate agentic AI products into their ecosystems. Autonomous agents, such as Spotter, are allowing businesses to work with data in ways that hadn’t been previously possible – allowing them to converse with the system in the same way they would a human data analyst. By embedding the products into their existing business applications, when users ask questions of their data, they can now get the structured and efficient answers they need, which are adapted to both the industry and persona of its users.

To unlock transformative business value, companies must integrate GenAI and agentic AI without delay. Staying ahead of the curve ensures they dismantle bottlenecks, achieve rapid, data-driven insights, and enjoy substantial ROI—essential advantages in an AI-powered future.

The post From assistance to agency: how GenAI for analytics is unlocking measurable business value appeared first on TechInformed.

This isn’t just a local issue — data suggests that globally, temperatures reached unprecedented highs this July, marking the hottest month ever recorded.

These rising temperatures are putting pressure on a variety of industries, from energy and transportation to agriculture, and preparing for the summer months is becoming a top priority.

For food retailers, the challenge is particularly unique. As an industry already operating on tight margins, supermarkets must keep perishable produce cool and in optimal conditions even while outside temperatures soar.

At the same time, they need to combat skyrocketing energy costs and prevent equipment breakdowns as cooling systems are forced to work harder in the heat.

To meet these challenges, food retailers are turning to advanced digital technologies. These innovations can improve energy efficiency, reduce the risk of catastrophic machine failures, and ensure optimum conditions for fresh produce, all while minimising food waste.

Fresh, safe, and available

For food retailers, keeping produce fresh, safe, and available for consumers is a top priority. This is especially challenging during heatwaves when warmer conditions increase the risk of spoilage and force refrigeration systems to work overtime to maintain the ideal conditions.

In such scenarios, traditional methods have fallen short, making it clear that precision and control are key.

Digital solutions, such as IoT (Internet of Things), have the potential to overcome these challenges, by providing unparalleled visibility and control over the critical equipment in a supermarket, such as the refrigeration systems and HVAC units.

By mining and monitoring millions of raw, real-time data points from these machines, advanced solutions can then analyse the data, detect inefficiencies, predict potential failures, and make automatic adjustments to ensure optimum conditions for produce.

Take refrigeration, for example. Traditionally, the complex nature of managing vast amounts of refrigeration equipment meant all products were chilled to the lowest temperature required by the most sensitive items, like meat.

This one-size-fits-all approach led to unnecessary energy use and often compromised the quality of less sensitive items. Now, with digital technology that integrates third-party data such as merchandising systems, retailers can tailor refrigeration temperatures to the specific needs of each product type, ensuring maximum freshness and minimising waste.

This level of precise, micro-control over operations not only enhances food quality but also drives significant reductions in food waste. It is this unique ability to combine granular control with macro-level outcomes that provides retailers with a robust and resilient approach to machine management, even during the most challenging conditions like heat waves.

Ensuring energy efficiency

Integrating digital technology into retail infrastructure can significantly reduce unnecessary and costly energy consumption, helping to protect retailers’ bottom lines. During periods of extreme heat, cooling equipment consumes more energy as it works harder to maintain regulated temperatures.

Research from Imperial College, London shows that a 2ºC increase in average summer temperatures will lead to a 6% rise in an estate’s refrigeration energy consumption  over the summer months. This highlights the critical need for energy efficiency in food retail operations.

Significant energy efficiencies can be gained and sustained by deploying IoT software to collect and make sense of hundreds of thousands of data points from machines, controls, and systems across entire retail estates.

Such technology can offer powerful solutions by contextualising this data with other connected systems to gain visibility and precise control over machines, allowing them to optimise the machines, as well as the schedules and processes these systems run.

For example, lighting and HVAC systems can be automatically adjusted based on store hours or weather conditions, significantly reducing energy consumption during off-hours or lower temperatures to limit costs.

The real-time data generated by interconnected assets supports a comprehensive and holistic energy management strategy. During warmer months, when refrigeration units are under increased stress, additional load-shedding capabilities can be implemented. This means energy can be strategically redistributed from less critical units, such as those storing beverages, to crucial units that require more power and lower temperatures, like those preserving fresh produce.

By using these advanced digital strategies, retailers not only optimise energy use but also ensure that their operations remain resilient and efficient, even in the face of rising temperatures.

Mitigating breakdown

Leveraging data and IoT technology provides a significant maintenance advantage, especially during heatwaves when cooling systems must work harder and are more prone to faults and failures. In these high-stress conditions, machines are under greater strain, making the risk of breakdowns and energy inefficiencies higher.

By continuously monitoring cooling assets for incremental changes, IoT solutions can identify and alert when an asset performance deviates from ideal conditions and is demonstrating behaviours of a fault or failure.

When performance issues are identified, advanced solutions can immediately and automatically adjust the system to maintain ideal operating conditions, preventing negative outcomes such as increased energy consumption or spoilage of valuable stock. If these automated adjustments do not – or cannot – resolve the issue, the system will promptly alert an engineer to take further action.

This approach to early fault detection and swift intervention is crucial in preventing catastrophic machine breakdowns. It shifts retailers from a reactive maintenance model to a predictive one, enabling them to address issues before they escalate. By catching problems early, retailers can maintain operational efficiency, reduce the risk of costly downtime, and protect their business from the adverse effects of equipment failure.

Heat waves vs Innovation 

Heat waves pose significant challenges for food retailers, including soaring energy bills, increased risk of equipment breakdowns, and the threat of food spoilage due to compromised conditions. These issues demand robust solutions to protect both business operations and product safety and quality.

However, retailers are rising to the occasion with impressive innovation and resilience and by embracing cutting-edge digital technologies, they are not only addressing the immediate challenges of higher temperatures but also transforming their operations for long-term sustainability. These advancements enable food retailers to improve cost and energy efficiency, enhance operational reliability, and reduce food waste, proving that with the right tools, they can thrive even in the face of extreme heat.

Read more here: Deadline for IoT devices to meet new UK security laws strikes

The post How supermarkets are staying cool in a warmer world appeared first on TechInformed.

Recent stats state that AI has the potential to unlock £119bn annually in revenue through driving productivity. However, this potential can only be fully realised if the technology gains acceptance from all levels of an organisation, especially from grassroots employees who are, understandably, concerned about its impact on their jobs.

The pressing question then becomes: How can employers alleviate fears around AI while fostering a culture of trust and innovation? It’s a delicate balancing act and HR is uniquely positioned to lead the way.

Innovation vs employee well-being

When it comes to introducing AI into the workplace, openness and transparency are non-negotiable. Employees are naturally wary of the impact AI might have on their job security, and as HR professionals, we need to front-load communication with honesty. It’s essential that employees see leadership as genuinely considering the broader impact of AI and not just the bottom line.

The role of HR is critical here. We are the conscience of the business, often reminding leadership of the people impact while balancing financial goals. It’s not a matter of asking, “Should we be adopting AI?” but rather, “How do we implement AI in a way that’s beneficial for everyone?” AI should not be framed as a replacement for people but as a tool to enhance their work.

By automating tedious, repetitive tasks, we free up employees to focus on more strategic and creative endeavours. Jobs are evolving, not disappearing, and it’s HR’s role to highlight this evolution. Our message to employees should be clear: AI is here to support you, not to sideline you.

In some cases, AI will inevitably replace certain roles or reduce the need for as many people in highly repetitive areas and it’s HR’s responsibility to communicate this honestly, ensuring employees understand the evolving nature of work. Our message should be clear: AI is here to support growth and efficiency, while reshaping roles, not always replacing the value people bring.

Trust through engagement

National surveys often reveal many employees believe AI will lead to fewer jobs, but two-thirds of employees believe it won’t replace them. This indicates a level of optimism, but there’s still underlying anxiety. Employees worry about using AI in their day-to-day roles, fearing they might be judged for leveraging technology to lighten their workload.

The key to addressing this anxiety is in how HR managers can build trust. By highlighting the benefits AI brings – such as space for increased productivity, creativity, innovation and necessary thinking time – we can shift the conversation from fear to empowerment.

It’s about creating meaningful roles that engage employees and ensure they continue to feel valued. Part of our job is to reskill and retrain the workforce to adapt to AI. This doesn’t mean we take responsibility for every employee’s learning journey, but we must actively understand how AI is affecting job roles and where support is needed.  By doing so, we create a workforce that feels equipped and empowered to work alongside AI, rather than being overwhelmed by it.

Redefining the role of HR

HR’s role is evolving alongside the technology we manage. It’s no longer just about managing people; it’s about managing change. We need to take a proactive stance in helping employees see AI as an ally. This means taking the stigma away from “just sitting and thinking” or using AI for initial ideas. Employees often feel guilty if they’re not constantly busy, but some of the best insights come during those moments of reflection.

AI can help employees carve out time for creativity by automating routine tasks, but HR needs to guide this process carefully. We must avoid the risk of lethargy and complacency. AI should be a tool that sparks innovation, not one that leads to laziness. Encouraging employees to use AI to kickstart projects, but not relying on it for everything, helps maintain a balance between human creativity and technological efficiency. I believe that both HRs and management should share how they integrate AI into their everyday work to alleviate the worry and doubt about using the technology in their own projects.

HR also has a responsibility to create guidelines around the use of AI in the workplace. By understanding AI’s role in enhancing work, we can spot when it’s being used inappropriately and intervene to support employees in using it effectively. Usually, it’s the different tone of voice that stands out in an AI generated piece of work and it’s up to the employee’s line manager or HR manager to offer guidance in how to integrate their own personality and tone into their AI generated projects.  These learnings will ensure that AI is seen as a tool for job enhancement, not job replacement, and protects the integrity of the work being done.

Steps for embracing AI

One practical step HR can take is to embed discussions about AI into the employee experience from the start, including during recruitment and appraisal processes. By making AI part of the conversation from day one, we can remove the mystery and anxiety around it. This transparency ensures employees know what is expected of them and how AI will play a role in their day-to-day responsibilities.

The role of HR is to ensure that AI is used responsibly, both by employees and leadership. It’s a collaborative effort, one that involves every stakeholder in the organisation. Empowering the workforce, regulating AI’s use and fostering an environment where AI is viewed as a tool for growth rather than a threat are key to a successful AI integration.

From HR to the future

HR professionals themselves need to be prepared for the shifts AI is bringing. Job design is changing, and we must adapt by incorporating AI-related skills into job descriptions and creating new roles that align with this new era. By staying ahead of the curve and understanding AI’s potential HR can lead the organisation through this transition smoothly.

AI is here to stay and its impact on the workplace will only grow. However, by approaching its implementation with openness, transparency and a focus on employee well-being HR can ensure that AI becomes a tool for empowerment rather than a source of fear.

Our role is to bridge the gap between leadership’s excitement and employees’ concerns while fostering a culture where AI enhances, rather than diminishes, the human element of work.

The post The HR’s guide to supporting employee use of AI appeared first on TechInformed.

Enterprise leaders across verticals want to harness the power of AI to improve efficiency, extract data-driven insights, and drive positive business outcomes.

While AI tools are indeed on the path to delivering value to many organisations, the increased visibility around this quickly evolving category exposes another by-product of AI usage: elevated organisational risk.

Recognising this risk has spurred several global regulators and lawmakers to action. One prominent example is the directives the US government outlines, which aim to ensure a safe and sustainable path forward for government-facilitated AI efforts.

Big tech’s closed AI ecosystems are hindering trust and development, report claims

On October 30, 2024, the White House issued the Executive Order on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, a framework designed to guide US federal agencies as they adopt AI-powered capabilities.

The AI Executive Order was notable for its depth and clear directives, including specific calls to action for more than 20 agencies. Implementation deadlines spanned between 30 and 365 days.

As we examine the AI landscape one year after this directive, the progress made, including the recently released National Security Memorandum on Artificial Intelligence, is encouraging.

While these actions effectively establish the baseline expectation that privacy and security cannot be afterthoughts when adopting AI but must be intentionally integrated into AI systems from the beginning, one action-filled year is not the end of the story.

It is important to continue this commitment to creating an environment where AI risks are acknowledged, privacy is respected, and security is foundational.

At its core, Secure AI is about minimising risk and enabling trust and security while enhancing decision-making, protecting privacy, and combating risks.

To deliver the best outcomes, AI/ML capabilities need to be trained and enriched using a broad, diverse range of data sources.

Privacy-Enhancing Technologies

Foundational to these efforts are Privacy-Enhancing Technologies (PETs), a family of technologies uniquely equipped to enable, enhance, and preserve data privacy throughout its lifecycle.

PETs allow users to capitalise on the power of AI while mitigating risk and prioritising protection.

Data is the foundation upon which AI is built, so it may seem obvious that the privacy and security challenges that have long been associated with data also extend to AI tools and workflows.

Yet, within many organisations, the fog of AI hype seems to have hidden this reality. Since the surge of activity driven by the host of Generative AI tools that burst onto the scene in late 2022, numerous AI efforts have advanced without a passing thought to the security implications or long-term sustainability.

Responsible AI innovation requires action — and systemic action requires resources.

Like the AI Executive Order directives that initiated a number of workstreams in the US, there remains a role for global governments to work alongside industry to support safe, responsible, trustworthy, and sustainable AI practices.

Technical AI experts from nine countries and the European Union will soon meet in San Francisco to discuss international cooperation on AI safety science through a network of AI safety institutes.

Legislative and regulatory actions and the funding of tools and technologies that prioritise privacy and security further bolster global AI leadership.

Dedicating resources to adopting technology-enabled solutions, such as PETs, will help ensure that the protection of models and workflows is foundational, safeguarding the vast amount of sensitive data used during AI training.

Reflecting this pursuit, the European Union approved the EU Artificial Intelligence Act in March 2024. This consumer-centric act mandated the right to privacy by stating that personal data protection must be guaranteed throughout the entire lifecycle of the AI system.

“Measures taken by providers to ensure compliance with those principles may include not only anonymisation and encryption but also the use of technology that permits algorithms to be brought to the data and allows training of AI systems without the transmission between parties or copying of the raw or structured data themselves.”

The NCSC Guidelines for Secure AI System Development were released in the UK in November 2023.

They identified security as a core requirement, not just in the development phase, but throughout the life cycle of the system and pointed to PETs as a means of mitigating risk to AI systems: “Privacy-enhancing technologies (such as differential privacy or homomorphic encryption) can be used to explore or assure levels of risk associated with consumers, users and attackers having access to models and outputs.”

Sustaining momentum

As the AI market continues to expand exponentially, leaders must understand and support efforts to drive the responsible use of these technologies.

That support includes crafting directives, policies, and budgets to advance Secure AI efforts. It also includes working with tech leaders, academics, and entrepreneurs who have a strong stake in advancing the adoption of these technologies in a secure and sustainable way.

Prioritising the safe, secure, and responsible use of AI and providing the funding necessary to sustain its advancement will ensure the impact of these transformative tools far into the future.

The post Trustworthy AI: one year after the Executive Order appeared first on TechInformed.

Most global employees (70%) are willing to use AI to help manage their workload — a productivity boost that will be particularly welcome in the UK, where productivity levels are dwindling.

But as AI adoption accelerates, can we be sure our relationship with it is headed in the right direction? Vanta’s State of Trust 2024 Report details how AI governance and risk management are still relatively nascent.

For instance, only 43% of UK organisations currently conduct or are in the process of conducting regular AI risk assessments.

The EU AI Act may shift in the right direction, but there is concern over how the act will adapt to evolving AI threats. Moreover, UK organisations do not have to comply with it unless they operate within Europe.

This means a lot rests on UK organisations’ self-regulation. However, when it comes to formal policies for governing AI usage, only 42% of them have or are in the process of implementing a company AI policy.

This indicates the lag between security and the increased use of AI tools — and the need to discuss trust when discussing AI.

The risks of AI without trust

While AI is helping drive productivity, it also complicates the security landscape. Most UK businesses (66%) plan to invest more in security around AI use within their organisation in the next year.

This is a clear reaction to the fact that since the proliferation of AI, cyber-risks and threats have gone up — with businesses reportedly experiencing more phishing attacks (35%), a rise in AI-based malware (34%) and more compliance violations (27%).

As if that weren’t enough, the potential damage of using AI without risk management and an in-house policy goes beyond data breaches and cyber threats — it hits a company where it hurts most: its reputation.

More than half (53%) of UK organisations say that customer trust results from good security practices, meaning they must do more to protect it.

Demonstrating trust in the age of AI

Security professionals are already under pressure from a challenging security landscape and the burden of manual compliance tasks.

AI is helping while further complicating this, and teams can’t be expected to take on more without losing their focus on mission-critical work. But equally, they can’t afford to ignore the problem either.

Below are three ways for organisations to ensure they maintain a baseline of cybersecurity readiness at all times that go beyond the basic requirements of the EU AI Act.

This includes ensuring that their use of AI remains compliant and they are reaping the benefits without exposing themselves to unnecessary risks.

1. Strengthen their entire trust network

Trust is not just a reflection of an organisation. It also extends to its partners and the wider business ecosystem.

For instance, almost two-thirds (63%) of UK businesses agree that third-party breaches negatively impact their organisation’s reputation.

AI is set to complicate this further as more companies add AI to their tech stack and/or develop their own tools.

Therefore, UK organisations must strengthen their entire trust network to maintain trust.

Companies must raise the security bar and build a bespoke standard of trust that centralises visibility, however and wherever AI is being used.

This is how they can define good security — for themselves and the companies they work with.

2. Take tools (and customers and employees) seriously

Within the workplace, half of UK organisations (49%) have concerns about the use of AI and the risks it poses for the organisation’s security.

Plus, there are increasing news stories surrounding the consent-free use of customer data to train AI. In fact, our research found that while 29% of UK organisations require opt-in from customers to use their data for AI training, 74% of companies don’t offer an opt-out option.

When trust and transparency matter so much in and outside of an organisation, it’s imperative that companies go beyond the standard when demonstrating trust.

3. Power trust with automation and AI

The irony of AI complicating the day job of your security team is that it can also play a crucial role in helping relieve the compliance burden they face.

When asked about AI’s transformative impact, UK leaders listed the most transformative areas as improving the accuracy of security questionnaires (45%), streamlining vendor risk reviews and onboarding (43%), eliminating manual work (37%), and reducing the need for large teams (32%).

This shows just how transformative AI can be in unlocking efficiencies for security teams and ultimately driving business value for organisations.

By looking to AI as the solution rather than just the problem, organisations can do more with less.

This can transform the compliance burden security teams face, and companies can protect themselves against themselves and focus on mission-critical work instead.

This is how organisations keep pace with where the world is headed and ensure that trust goes on the journey with them.

The post Why we can’t talk about AI without talking about trust appeared first on TechInformed.

The post Cloud, code, and control: what AI can do for enterprise data pipelines appeared first on TechInformed.

For the patient, this means prolonged call times, high volumes of repeat calls and heightened anxiety whilst they wait for a response.

What’s more, the disconnect between different systems, Trusts and other organisations involved in the healthcare journey (such as social care), continues to drive inefficiency and unnecessary waste. All too often we are seeing patients not attending appointments due to ineffective communication and not turning up, or patient transport still turning up when an appointment has been cancelled.

Events such as these are not only costing the NHS financially and in terms of resource, but it is also having a damaging impact on the patient experience, and ultimately – patient outcome.

Breaking the cycle

Over time a vicious cycle of inconvenience and inefficiency has been created, leaving patients feeling frustrated by having to wait to have their enquiries resolved. Often, additional stress is placed on service centre agents who are facing the brunt of this. Patients are frequently passed around in the process fuelling this frustration further, whilst other requests end up in an operational black hole – remaining unresolved.

So, what is the answer?

By bringing all relevant sources of patient data together into one single view, NHS Trusts and their service centre agents can be equipped with the information they need to transform experience and efficiency within the NHS.

Known as a Patient Relationship Management (PRM) system, solutions of this kind present the appropriate patient information in a meaningful way to assist with outpatient administration.

Faster access to information means better response times, and a better experience for both patients and NHS staff, offering an opportunity to break the vicious cycle of patient frustration, stress for agents and operational inefficiencies.

One view to rule them all

Having an immediate contextually relevant view of the patient when receiving an inbound enquiry can save 60 or more seconds per call – a significant time saving for outpatient service centres grappling with the waiting list backlog.

This will not only create additional capacity in service centres but can also provide a consolidation opportunity to support further service improvement and transformation initiatives in the future.

Having the right knowledge in the right place at the right time can enable better in-call decision-making meaning that outcomes and solutions can be reached quicker and easier – removing the need for follow-up calls and providing relief in what can often be a stressful scenario.

It can also support the identification and eradication of operational bottlenecks and inefficiencies caused by gaps in information and communication – such as follow-up appointments not being cancelled if the original appointment or diagnostics has not taken place. Every day a growing number of appointments are going ahead unnecessarily, wasting both consultant and patient time.

Forming a more holistic view of the patient can also ensure better attendance rates for appointments that are necessary. This can be achieved by taking a closer look at patient demographics and being aware of personal situations when arranging/setting appointments initially.

Insight into action

Knowledge is key when it comes to dealing with patient enquiries, however, there must also be workflows in place to turn that knowledge into action. Effectively configured workflows can enable follow-up tasks to be completed in just a few clicks, whilst the consolidation of these activities into a central view removes the need to rely on ad-hoc emails.

By adding this layer of structure, service agents – and patients – can be confident that next steps are followed up on and actioned in a timely manner.

That’s where PRM systems built via low-code platforms that easily integrate with other tools and technologies such as RPA can be particularly effective. Integration of this kind can allow the automation of onward tasks for further quality, performance and efficiency improvements.

Unlike traditional Enterprise CRM systems which require a long program of analysis, design, build and deployment, PRM systems built via low-code platforms, that accelerate development, can deliver immediate benefits as well as long-term improvements.

A streamlined future

With patient referrals continuing to run significantly higher than pre-pandemic levels, the intense pressure felt by NHS service centres is unlikely to slow down anytime soon. Especially given that the NHS’s Elective Recovery Priorities focus on better patient engagement and re-focusing capacity toward new patients.

This will certainly have an impact on the agents operating on the frontline – many of whom are already at risk of burnout and dissatisfaction. Equipping these agents with the right tools and platforms to work smarter and more successfully, therefore, must be a priority.

The same can be said for patient experience, despite efforts to reduce current NHS waiting list, it is likely to be a reality we will live in for some time. Focus should be placed on ensuring that patients are waiting well, and that when they reach out they are left reassured, rather than being faced with additional stress as a result of broken and disconnected systems.

NHS Trusts must work towards a streamlined future focused on improving the patient and employee experience. Technology will act as the foundation of this future, with PRM systems at the very heart.

Read more here: Postcards from the edge: NHS tech challenges and how industry can help

The post Can Patient Relationship Management platforms transform the NHS? appeared first on TechInformed.

“AI’s greatest potential lies in its ability to reshape workplace culture; by prioritising results over hours worked”

As summer comes to a close, many face the challenge of getting back into the swing of things after a seasonal productivity dip.

Nearly half of UK employees report being less productive during summer months — and this trend is only worsened by digital fatigue. Today’s working environment can be overwhelming, and the constant stream of notifications, emails, and meeting alerts pulls people’s attention in multiple directions, making it harder to focus.

This dip in productivity has real consequences. According to the Dropbox Economist Impact Survey, the average worker loses 122 hours a year just trying to refocus after distractions, with UK employees losing as much as 131 hours a year.

Attached to this loss of time is a significant economic cost. The UK alone is losing nearly $188 billion in potential productivity due to digital distractions.

And it’s not just about numbers — this constant barrage also takes a toll on employees’ well-being. A study by HR software provider Ciphr found that 50% of UK employees regularly work overtime, pushing them towards burnout and risking their mental and physical health.

As a result, businesses face higher turnover, lower job satisfaction, and a lack of overall efficiency, which impacts bottom-line performance.

But how much of this problem is caused by outdated work processes? Artificial Intelligence (AI) offers a powerful solution to tackle inefficiencies and help workers regain their time.

By automating repetitive tasks and synthesising data, AI can enable employees to focus on higher-value work and, crucially, overcome the mid-year productivity slump.

Focusing on results over hours

More companies are moving away from tracking hours worked and instead emphasising the quality and impact of the work produced.

Cal Newport, productivity expert and author, calls this phenomenon ‘pseudo-productivity’, where busywork is confused with true efficiency. He notes that peeling back the surface often reveals a few critical actions that genuinely drive value.

This is where AI can make a profound difference. By taking over routine tasks such as sorting emails, summarising documents, or even preparing targeted reports, AI frees workers to focus on more important and meaningful activities.

These AI-powered tools allow employees to devote their time to deep, high-impact work rather than bogged down by administrative duties.

This cultural shift from ‘busyness’ to purposeful productivity can reshape the modern workplace, especially during the end of summer when productivity can be at its lowest.

The productivity gains from AI go beyond simple time-saving. By relieving workers from low-value tasks, AI encourages creative problem-solving and innovation, which drive business success.

Research shows that employees who engage in meaningful work are happier, more motivated, and ultimately more productive.

Read more: Five ways AI will be used to turbocharge organisations in 2024

Harnessing the power of extra time

Imagine the possibilities if every employee gained an extra hour each day.

Take, for example, Myth Studio, a UK-based animation company. Their adoption of AI and cloud-based collaboration tools has significantly reduced time-consuming tasks, such as managing feedback and revisions across dispersed teams.

As a result, their staff have more time to focus on strategic and creative projects, leading to better work outcomes.

This transition hasn’t just boosted individual productivity — it’s fostered a more collaborative and efficient working environment.

With less time spent on repetitive tasks, Myth Studio’s employees have found more opportunities for innovation, professional growth, and personal development.

For the UK’s five million small businesses, AI could be the key to unlocking new growth and ensuring that even the smallest teams can thrive despite digital distractions.

From digital overload to digital empowerment

While digital distractions can weigh heavily on productivity and employee morale, AI offers a way to transform this challenge into an opportunity.

By integrating AI tools into daily workflows, businesses can turn the tide, helping employees refocus and spend more time on impactful tasks.

As Myth Studio has shown, AI can free up time for creative and strategic work, driving greater engagement and innovation.

AI’s greatest potential lies in its ability to reshape workplace culture. By prioritising results over hours worked, businesses can create environments where employees are empowered to focus on what truly matters — both in their professional and personal lives.

This can lead to healthier, happier teams and more resilient, successful businesses ready to meet the challenges of the summer slump and beyond.

The post How AI helps businesses overcome digital overload and boost productivity appeared first on TechInformed.

Before a business plunges headfirst into AI, it is important to identify the use case – the exact problem you are trying to solve. In my experience, it is vital to be as specific as you can. At Mallowstreet, our use case was the meeting.

We all have lots of meetings – too many. So, the Mallowstreet team started by asking ourselves the simple question: how can we help make meetings, and the necessary follow-up, more efficient and effective? As a result, we built our own AI tool SOFI to analyse a meeting.

We used our industry-specific knowledge to train SOFI to fluently summarise financial meetings and discussions, surfacing key actions and takeaways in the process. We also built the capability to analyse a meeting through critical lenses – a dashboard of how the meeting deals with risk, return, liquidity profile, investment time horizon, growth versus matching investment characteristics, and ESG.

The result was amazing. As a meeting attendee, you could now actively listen and truly pay attention – feeling 100% present, knowing that SOFI is in the background ensuring no discussion points are missed, and instantly see if the salient lines of enquiry are being attended to.

Expanding the use case

Then came our second round of development – again leaning on the use case approach. This time, we asked: can we apply SOFI analysis to a whole series of related meetings to highlight key themes, identify discrepancies and surface common questions across sessions? This was the genesis of Multi-Vertical Analysis.

Multi-Vertical Analysis unlocks our trend analysis tools, paving the way for the development of ‘SOFI scores’ which quantify vital meeting dynamics. For example, how much time does each participant speak (soapbox score) or how many ‘ums’ and ‘ahs’ do you say (disfluency score)? With this added layer, SOFI has also become a pitch/ presentation practice tool – providing feedback that is objective, consistent, and transparent.

It’s clear that, when implemented in a thoughtful and structured way, AI does deliver on specific use cases. But what I’ve found more interesting is the concept of the ‘impact coefficient’: how an individual can achieve uplift in delivery of their core role.

Very few people go into a job to become, for example, a financial adviser who says: ‘wow, I really cannot wait to write up the reports for my clients, fill in the KYC forms, and update the CRM with all of the required points to satisfy an increasingly more regulated world’.

On the contrary, people become financial advisers because they want to help people on their journey. They want to make a difference, to spend time with their clients and get to know them, so they can provide the best advice to allow each person to achieve their respective goals.

I repeatedly hear the above tension of how people want to spend time vs where time actually gets spent by wealth advisers and financial advisers. And this is where the impact coefficient comes into its own. If an adviser is freed from the responsibility of capturing and analysing all the information from a conversation, they can spend their time focusing on asking the right questions and really getting to the heart of what a client needs. They can pay far more attention to body language and tone, helping to understand the ultimate driver of a client’s concerns or decision making.

Allowing the adviser to be truly present in a conversation with their clients allows them to help provide better advice, allowing the client to make better decisions, and ultimately having a fundamental positive impact on their long-term trajectory.

Imagine a world where all the advice being offered was elevated in this way. The long-term impact on the UK wouldn’t just be significant – it could be transformational.

A tool, not a replacement

People often ask me if AI is going to ultimately make a huge amount of the work force redundant. The honest answer is I don’t think anyone knows. But what I am sure of is this: the people integrating AI into their daily workflow are not only becoming more efficient, but also more effective. Those who are leveraging the impact coefficient are gaining ground on all of us.

Stepping back, when you are approaching AI, it is incredibly important to nurture curiosity – ask the challenging questions, and push for how things can be done differently and, even, better?

You aren’t looking for a quantum leap. Think about the Tour de France. Better handwashing to avoid germs, improved pillows for better sleep, tiny aerodynamic redesigns for faster results. Alone these tweaks are helpful – together they are transformational.

And make sure you have a growth mindset – the belief that abilities, intelligence, and talents can be developed over time through dedication, hard work, and learning. This contrasts with a fixed mindset, where someone believes their abilities and intelligence are static and cannot change.

What excites me is the fact that we now have a new set of tools which can transform the way we work together and leverage the impact coefficient. Be specific in the issues you’re trying to solve – find your use case – and automate the tasks that can be.

Because the more time we can save, the more intellectual firepower that can be deployed to help ensure we all achieve our respective goals. By putting financial advisers and the financial services at the forefront of innovation and driving adoption of AI to benefit from its advantages, we can achieve great things.

Read more: How to launch a dating app in 8 days

The post Art of the meeting: Transforming client relationships in finance appeared first on TechInformed.

Schmidt’s ‘AI versus climate change’ comments obviously align with his major business investments in AI companies and his belief in AI’s potential is understandable. AI is already proving valuable in so many areas of our lives and, as far as the climate is concerned, specifically in areas like energy and process optimisation, climate modelling such as predicting droughts in Africa, data analysis and supporting smarter decision-making. There’s no doubt AI will be a massive help in the fight against climate change, but framing AI as a solution that should take precedence over climate action is misguided.

AI is part of the problem

AI can help us tackle environmental challenges, but it is not a silver bullet. It cannot address the root causes of climate change—like industrial emissions, deforestation, and overconsumption without broader systemic and policy changes. With regards to overconsumption, people will buy more stuff when prompted by the AI driven algorithms of Amazon and other ecommerce platforms.

In addition, AI itself is energy intensive. Large-scale data centres and AI model training consume significant energy, contributing to the carbon footprint. For example, doubling AI data centres, as Schmidt suggests, could lead to a sharp rise in global emissions. According to the Climate Action against Disinformation coalition, this could result in an 80% increase in global carbon emissions.

Additional research suggests that AI use in data centres could use as much electricity as a small country such as the Netherlands or Sweden by 2027.

In the US, there is already evidence that the life of coal-fired power plants is being prolonged to meet the rising energy demands of AI. Much of this increased energy demand comes from the added complexity of AI operations – generating AI queries could require as much as 10 times the computing power as a regular online search. Training ChatGPT, the OpenAI system, can use as much energy as 120 US households over the course of a year, the report claims.

If AI is allowed to proliferate at this rate without action being taken to protect the environment, it would be unequivocally disastrous for the world.

Big Tech’s nuclear plans

Schmidt’s former company Google, along with other Big Tech including Amazon and Microsoft, are fast exploring options to accommodate AI’s vast energy needs and have landed on advanced nuclear power as the best option. Although this progressive energy source is being touted as safer and more sustainable than traditional nuclear power plants, it also has its disadvantages. Nuclear energy may be clean from carbon emissions, but it uses highly toxic chemicals and has the potential to be utilised in weapons, which has led some environmentalists to take a strong stance against it.

At 51toCarbonZero, we can see the benefits of nuclear – it’s more reliable than solar or wind, it can be produced onshore meaning less reliance on overseas deals, and it’s relatively cheap in comparison to other energy options. However, nuclear power should not be seen as the future of energy; it has its place, but only as part of a balanced energy economy alongside green energy sources. The drive to power AI must not ignore the investment, development and improvement of green energy technology. And this is what we fear Big Tech is overlooking.

Ignoring climate crisis will affect the vulnerable

Prioritising AI advancement and delaying climate action has broad repercussions, and it’s essential to consider the significant societal impact of postponing climate action. Ignoring the urgent need to cut emissions now would disproportionately affect vulnerable populations and low-income countries, many of which are already facing severe climate impacts. Expecting AI to eventually solve these issues overlooks the moral imperative to protect these communities today.

While AI’s potential to contribute to sustainability is significant, prioritising its evolution over immediate climate action could increase risks to humanity. As Schmidt himself has noted, AI can pose an “existential risk” if left unregulated. A measured approach to AI development is therefore essential, but the climate crisis poses an even greater existential threat, demanding urgent action now.

In summary, we view AI as a super-powerful ‘tool’ in the fight against climate change, but it cannot replace the multifaceted approach needed to solve the climate crises. Climate action requires not only technological innovation and process optimisation but also policy reform, economic restructuring, behavioural change and international cooperation. AI is a critical part of the solution, probably contributing in ways we cannot yet imagine, but it cannot take precedence over the climate crisis that demands our immediate attention.

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