Challenges Facing Work Technology

Our new study highlights a growing challenge for the #digitalworkplace industry: an increasing level of technology intensity for employees, revealed in experiences of hyperconnectivity, overload and Fear of Missing Out. 𝗞𝗲𝘆 f𝗶𝗻𝗱𝗶𝗻𝗴𝘀: • 𝗛𝘆𝗽𝗲𝗿𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝘃𝗶𝘁𝘆 𝗻𝗼𝗿𝗺: A sense of pressure to be available and erosion of work-life boundaries is becoming normalised, making it harder to psychologically detach from work. • 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗼𝘃𝗲𝗿𝗹𝗼𝗮𝗱: The proliferation of apps, messages, and video calls is levying a mental and emotional toll, contributing to attentional conflict and overwhelm for workers. • 𝗧𝗲𝗰𝗵𝗻𝗼-𝘀𝘁𝗿𝗮𝗶𝗻: Employees report impacts to both physical and mental health as a result of excessive demands in the digital workplace. 𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 ��𝗵𝗶𝘀 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝘄𝗼𝗿𝗸𝗽𝗹𝗮𝗰𝗲 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝘆? While the digital workplace is a central feature of modern organisations - enabling productivity, collaboration and flexibility - it can also create unsustainable demands on employees if not designed and managed thoughtfully. 𝗜𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝘄𝗼𝗿𝗸𝗽𝗹𝗮𝗰𝗲 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗮𝗻𝗱 𝘁𝗲𝗮𝗺𝘀: • 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝘀𝗸𝗶𝗹𝗹𝘀: Equip employees with the skills and mindset to navigate the digital workplace with confidence, fostering productivity while protecting wellbeing. • 𝗦𝘁𝗿𝗲𝗮𝗺���𝗶𝗻𝗲 𝘁𝗼𝗼𝗹𝘀 𝗮𝗻𝗱 𝗺𝗲𝘀𝘀𝗮𝗴𝗲𝘀: Prioritise usability and accessibility, including reducing application proliferation and communication overload. • 𝗕𝗮𝗹𝗮𝗻𝗰𝗲 𝗶𝘀 𝗸𝗲𝘆: Help employees establish boundaries to enable positive work and life outcomes. The findings highlight a critical need for organisations to rethink their digital workplace strategies to protect wellbeing as well as enhance productivity.  The full research paper is available Open Access. It is published with my Phd supervisors at University of Nottingham, the wonderful Professors Alexa Spence and Elvira Perez Vallejos, and funded by the Midlands Graduate School ESRC DTP. #DigitalWorkplace #EmployeeWellbeing #TechnoStress #FutureOfWork #InternalCommunication #Intranet

𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞’𝐬 𝐭𝐚𝐥𝐤𝐢𝐧𝐠 𝐚𝐛𝐨𝐮𝐭 𝐚𝐩𝐩𝐥𝐲𝐢𝐧𝐠 #𝐀𝐈 𝐭𝐨 #𝐇𝐑... 𝐛𝐮𝐭 𝐰𝐡𝐲 𝐜𝐚𝐧'𝐭 𝐰𝐞 𝐞𝐯𝐞𝐧 𝐠𝐞𝐭 𝐛𝐚𝐬𝐢𝐜 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐫𝐢𝐠𝐡𝐭? AI promises to revolutionize HR—but before we dive headfirst into generative and agentic AI, it’s worth asking: Why have foundational technologies like Predictive #Modeling, Robotic Process Automation (#RPA), and even REST API #integrations struggled to take hold in HR over the last decade+? These are tools that have powered consumer grade applications and have advanced marketing, sales, and finance capabilities across industries for 10+ years. So what’s stopping HR from achieving the same? Here are some of the biggest blockers: 1️⃣ 𝐋𝐚𝐜𝐤 𝐨𝐟 𝐅𝐮𝐧𝐝𝐢𝐧𝐠 HR is often seen as a cost center, with budgets flowing to functions that show faster ROI, like sales or product. Limited resources and unclear financial returns mean HR tech is often deprioritized. 2️⃣ 𝐒𝐤𝐢𝐥𝐥 𝐆𝐚𝐩𝐬 HR teams lack technical expertise, while IT teams often don’t understand HR’s purpose. Traditional project and product methods fail to address compliance-driven, people-first challenges in HR. 3️⃣ 𝐏𝐨𝐨𝐫 𝐃𝐚𝐭𝐚 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 Fragmented, inaccurate, and siloed data hinder tech adoption. HR systems built for compliance or architected specific to one persona's needs often create disjointed solutions that block integration, analytics, and automation. 4️⃣ 𝐁𝐫𝐨𝐤𝐞𝐧 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬 Automation can’t fix what’s already broken. Undocumented or inefficient workflows lead to tech implementations that pile on complexity without addressing root problems. 5️⃣ 𝐂𝐮𝐥𝐭𝐮𝐫𝐚𝐥 𝐑𝐞𝐬𝐢𝐬𝐭𝐚𝐧𝐜𝐞 Fear of change and ROI doubts can stall tech initiatives. Leadership often hesitates, worrying that HR tech introduces risks without clear, immediate rewards. This is exacerbated by a lack of clear ownership and accountability for key HR workstreams like employee onboarding, internal mobility, and offboarding. If these foundational barriers have held us back for years, what makes us think we can integrate AI effectively now? The good news? These obstacles aren’t insurmountable. With the right strategies—like better process mapping, cross-functional collaboration, and targeted upskilling—we can set HR up for real success. Work like Dirk Jonker's efforts to better build comprehensive, explainable models tying human employee value to organizational balance sheets provides significant promise as well in creating compelling cases for better investment into people functions. What do you think? What’s holding HR back from adopting advanced tech at your organization or in your industry—and how do we fix it? Let’s discuss! ⬇️

It's always a privilege to connect with safety professionals across borders. While I couldn't make it to France for this team health and safety forum, technology allowed me to contribute remotely from Perth to colleagues in Reims. After sharing an educational overview of emerging technologies in workplace safety, the team ultimately synthesized their own insights. These four key takeaways reflect common challenges and opportunities that safety professionals worldwide are grappling with: 1. Problem-Led, Not Solution-Led Approach Define your challenge before exploring technology. Ask "How might we solve this problem?" rather than "How can we use AI?" Clear problem statements prevent solutions searching for problems. 2. Early Involvement is Essential Safety professionals must join technology conversations from the start, not after decisions are made. Challenge your suppliers about their innovation roadmaps and collaborate on solutions that fit your operational context. 3. Trust and Culture Come First Technology adoption depends on organisational readiness and worker trust. People need to understand how technology will be used and feel confident it won't be used against them. Start with trust-building use cases before moving to more 'sensitive' implementations. 4. Human Oversight Remains Critical AI can reduce administrative burden and support better decisions, but humans must maintain critical oversight. Technology should augment human capability, not replace human judgment. The goal is making people more effective, not removing them from safety-critical decisions. The key lesson: technology experimentation in safety must be strategic and human-centered. Success should be about thoughtful integration that serves people and improves work design that in turn creates business value. #safetytech #safetyinnovation

“The biggest error leaders make is when they apply technical means to solve adaptive challenges.” It’s an apt quote from Ronald Heifetz. Technical challenges are problems where the skills required to solve them are already known. They may be hard. They may matter a great deal. But we know what the solution looks like. Adaptive challenges are different – they can’t be solved by adding skills alone. They require people to make sense of new situations, question old assumptions, and shift how they see their role and value. AI adoption is largely an adaptive challenge: we’re not just asking people to learn how to use Copilot. We’re asking them to rethink their role. Yet many organisations are treating this as a technical problem: here’s the tool. Here’s the tutorial. Off you go. Don’t get me wrong, fluent use of AI tools matters. Technical challenges aren’t trivial, and they’re certainly not unimportant. But adaptive challenges ask something very different of people. They ask them to: • Exercise judgment where there is no single right answer, often under heavy pressure of performance expectations • Work productively with ambiguity rather than waiting for instructions • Revisit mental models that used to work and recognise when they no longer do • And in some cases, reconceive their entire work identity, not just their task list This is a big ask, especially when the “box” people need to step outside of has been invisible to them until now. These kinds of challenges can’t be solved with technical means alone. They demand changes in the environment people work in, support for people to make sense of the change, and leadership signals that make clear what’s rewarded – and what quietly isn’t. When adaptive work is treated as technical, adoption stalls and frustration grows. Don’t ask for transformation and only offer instructions. Design for adaptive work, not just technical skill. --- ♻️ If you found this post useful, share it to help more people think about AI adoption. 📩 If you're exploring the people side of AI adoption and where to go from here, that's exactly the kind of work I do with clients at Nodes. Let's chat!

HR leaders are being asked to “implement AI”. But in most organisations, the real challenge isn’t the technology. It’s the way work is structured. Because AI doesn’t actually change organisations by itself. Work does. And many organisations are introducing AI into work systems that were designed for a pre‑AI world. The result? A familiar pattern is emerging: • HR chatbots answering policy questions • copilots helping draft job descriptions or performance reviews • experimentation with automation in recruitment or HR operations But the underlying work remains largely unchanged. Manual processes still exist. Decision bottlenecks remain. Roles and accountabilities are the same. So the technology improves tasks, but rarely improves productivity. Real productivity gains and benefits only appear when organisations redesign: • workflows • decision rights • roles and responsibilities • how work moves between humans and systems In other words, the challenge isn’t just AI adoption. It’s work redesign.

🤖 Algorithmic Management in the EU Workplace: Risks, Gaps & Policy Paths A new study commissioned by the European Parliamentary Research Service sheds light on the growing use of #algorithmic management (#AM) and artificial intelligence across EU workplaces, extending far beyond the gig economy. 📌 Key Insights: 📈 Rising AM Exposure An estimated 55.5% of the EU workforce could soon be exposed to AM tools, up from 42.3%, especially in logistics, telecoms, automotive, healthcare, and manufacturing. ⚠️ Challenges for Workers While AM technologies can boost productivity, they also raise significant concerns: * Erosion of autonomy * Decline in job quality * Data misuse and lack of transparency * Impact on well-being and employment relations 🕳️ Regulatory Gaps Identified The current EU legal framework lacks specific provisions to ensure transparency, accountability, and worker protection in the use of AM at work. 🧭 Policy Options Considered The study outlines three EU-level responses: 1. A non-binding recommendation 2. Amendments to existing labour and data protection legislation 3. A dedicated new legislative instrument on AM at work 🇪🇺 Why EU Action Matters A harmonised approach would: * Ensure equal protection for all EU workers * Safeguard fundamental rights and working conditions * Provide legal certainty and a level playing field for businesses 💡 The study highlights the urgency of balancing innovation with social responsibility in Europe’s digital transformation of work. 📄 Full report: https://lnkd.in/dvg4C5FW #AlgorithmicManagement #AIAtWork #LabourRights #DigitalWorkplace #FutureOfWork #EULegislation #FairWork #WorkerProtection #HRTech #ArtificialIntelligence #EmploymentPolicy #DigitalEurope #PlatformWork #WorkplaceWellbeing

“Most organizations don’t actually know how work gets done. Roles are vaguely defined, responsibilities sprawl, and performance is often measured by presence rather than outcomes. When AI tools are introduced into this environment, the confusion becomes impossible to ignore." Sophie Wade’s point goes beyond AI. It gets at a deeper employability challenge. If organizations cannot clearly explain how work gets done today, or imagine how it could be done differently tomorrow, then it becomes nearly impossible to prepare people with the skills, attributes, and professional habits they need to remain employable as work itself evolves. Employability depends on more than tool training. It requires clarity about tasks, judgment about where human insight adds value, and ensuring people can adapt as roles are redesigned rather than simply automated. The adoption of AI into the workplace is exposing vague roles, unclear accountability, and work designed around activity instead of outcomes. Addressing this confusion is not just an operational issue. It is central to developing people who can grow, pivot, and contribute as work continues to change. The future of employability starts with understanding how work happens now, and the different ways it might change over time. #FutureOfWork #FutureOfEmployability #AI https://lnkd.in/eYi2kfm4 

The harsh reality about deploying agentic AI Everyone talks about what AI can do. But few talk about the roadblocks companies actually face when trying to make it work inside their business. After working with leaders across industries, I see the same 7 challenges come up again and again: 1. Integration complexity – Legacy IT + outdated APIs = slow, painful rollouts. 2. Messy data – Biased or inconsistent data = unreliable AI. 3. Security & compliance – Regulations (GDPR, HIPAA) can’t be an afterthought. 4. High costs – Infrastructure + licensing + talent can crush SMBs. 5. Talent gap – Few people know how to build, deploy, and maintain agentic AI. 6. Reliability & control – Agents can act in ways you didn’t intend. 7. Change resistance – Teams fear disruption, slowing adoption. The way forward is not to avoid these challenges but to anticipate and design around them. Start small. Scale responsibly. Build trust inside the org, not just in the tech. Companies that nail this balance will see the fastest ROI. Which of these 7 challenges do you think is the biggest blocker for enterprises right now? Let me know in the comments ! Follow Avani Rajput For More Such AI Insights !

Across many conversations with clients and partners, the challenge of actual adoption of GenAI has frequently come up. While the potential of the technology is well understood, businesses struggle with how to drive adoption at scale.    Research estimates that Gen AI could add up to $20 trillion to global GDP by 2030 and save 300 billion work hours a year. Yet the technology continues to be treated with equal parts excitement and apprehension - apprehension around impacting employment and jobs, aggravating existing inequalities in the society and possibly being deployed without the necessary ethical guardrails. I strongly believe that the adoption of GenAI is not a pure tech challenge- but a skills, culture and leadership challenge. The journey is not simply about more tools and platforms being rolled out – but is dependent on how these technologies can be integrated into the workflow, how these can help re-wire work and how mindsets can be shifted along with skill sets. To me, it begins by developing a workforce that is both knowledgeable about and confident in using the technology responsibly; and providing them the opportunity to explore its potential to augment and amplify their abilities to accelerate productivity and enhance experience. I’m curious to hear from my network on the practical approach you are taking to drive the adoption of GenAI in your organisations? #GenAI #futureofwork #skills

Over the past 2 years, the Workato Business Technology team has been implementing AI and Agentic solutions … Here are our team’s top 5 lessons learned based on 3 categories (People, Technology, and Prompting): 1. There’s a strong readiness for adoption of new AI technology. The primary challenge lies in setting the right expectations about what the solution is designed to do. 2. On the flip side, blind reliance on AI shouldn’t be encouraged. Verification is especially needed when rolling out new AI solutions. 3. Integrating new tech into someone’s everyday workflow & routine. Habit is one of the biggest hurdles to adoption. 4. Set clear expectations about what the AI will be able to do and not do. 5. There’s a learning curve when it comes to talking to bots or AI. Sometimes, new users have to be reminded to think outside the box and ask open-ended questions like “What can you help me with today?” The solutions? Start small, prove value internally, and build as you go. #BusinessTechnology #BT #AI