European Conversion Optimisation Trends: Advancing Personalisation through AI

The hardest work is left to be done before the Zendesk rollout can go live. CRM records must connect to ERP data and current service workflows without requiring agents to take additional steps, which causes friction at the edges. Even a technically sound integration can be disappointing if it requires a company to re-build familiar processes around the software. Service owners learning too late that a connector has changed ticket routing or data access in ways agents were never asked to absorb increases implementation risk. For executives, the buying question is less about volume of features than about how much disruption lies behind the promised connection. Before beginning bespoke development, it is advised to thoroughly examine process fit. Your implementation partner should be able to determine when a requirement requires code and when it can be met with standard settings thanks to Zendesk's abundance of configuration and integration choices. Over-customization might make future change management more difficult and increase maintenance effort. If you don't get enough adaptation, you have the reverse problem: agents resorting to manual workarounds to compensate. Preserve useful existing practices and adjust the platform where configuration is sufficient. Use custom solutions for integrations or business rules only when necessary. During testing, check agent workflows and administrative handoffs, not just if the data transfers correctly. Integration depth matters for the same reason. Customer service often spans Zendesk, ERP systems, another CRM and internal tools. Adding AI assistants makes poor data connections more visible because their usefulness depends on whether service data can be reached in a controlled way from the systems where it already lives. Zendesk supports authenticated connections to external services and API-based exchanges. Buyers should examine whether a provider can work comfortably with APIs and whether it understands the process consequences of moving data between systems. A connector is useful only when information reaches the right workflow at the right moment. European deployments bring privacy rules into the same integration decision. Zendesk provides features that can assist organizations with GDPR obligations, while responsibility for compliant data processing remains with the customer. Custom applications or connectors have to respect company-specific policies without adding unnecessary complexity elsewhere. The relevant question is whether the provider can translate an internal privacy rule into the Zendesk environment while keeping the wider service process workable. The handoff after launch is equally revealing. Some companies have enough internal expertise to administer Zendesk themselves. Others need continued help as service processes change, usage patterns emerge, data rules shift and configurations need refinement. A credible provider should make both models possible rather than creating dependence by design. Be-Simple is one of the most popular options for companies who want Zendesk to integrate into existing service processes without transforming the implementation into a full-fledged process overhaul. Before custom development, it’s all configuration and bespoke integrations where Zendesk has to connect to other systems or meet company-specific GDPR rules. It also provides post-go-live administration and performance review, supported by experience across roughly 400 Zendesk projects. Its work includes marketplace applications such as WhatsApp Sender for outbound messaging and Prenotare.app for scheduling service work. Membership in Assist Digital also gives Be-Simple access to additional specialists for larger enterprise projects. ...Read more

Workflow solutions allow organisations to design, automate and monitor business processes across business departments, applications and external partners. In Europe, such platforms are playing an increasing role as companies realise the need to modernise their legacy systems, integrate with other companies in other countries, and meet evolving regulatory needs. Today's platforms integrate with applications and use rules-based automation and AI to streamline the management of work, eliminate repetitive tasks and give managers more visibility into business activity. The forecast for the global workflow automation market size indicates $26.01 billion in 2026 and $40.77 billion in 2031. While these figures represent the global demand and not merely the European spending, they do indicate the increasing significance of automated processes in various industries. European organisations are exploring workflow technology not only to reduce manual work but also to enhance the consistency of their processes, improve internal controls, and adapt their business operations to meet customer expectations and navigate a changing environment. Artificial Intelligence Expands Workflow Capabilities AI is transforming workflow solutions by improving the processes for interpreting requests, classifying documents, and managing exceptions. Unlike traditional automation, which relies on a predetermined set of rules, AI has the capability to identify data in unstructured documents, summarise their contents, and recommend the next steps. In the EU, the proportion of enterprises adopting at least one AI technology rose from 13 per cent to 20 per cent between 2020 and 2025. AI agents are expanding these capabilities with the ability to execute certain actions throughout interconnected applications. They can categorise incoming customer requests, pull out the data they need, create in-house reports, and send documents for review. But as automation expands, more questions emerge about decisions and decision-making, accuracy and accountability. Organisations should establish what tasks an agent can perform on their own, and dictate how teams should handle errors and mistakes. In the cases of automated decisions that impact customers, financial transactions or regulated activities, human oversight is still relevant. Another base for workflow modernisation is cloud computing. Cloud platforms are used to enable employees from various workplaces to access shared applications, share information, and also adhere to the same process without depending completely on local systems. In 2025, 53 per cent of companies in the EU, including 85 per cent of large businesses, purchased cloud computing services. There are variations between organisations. But to get meaningful benefits from automation, companies with legacy systems may have to address data format issues, siloed systems and integration problems. Governance and Integration Shape Enterprise Adoption European organisations need to consider their data protection requirements, cross-border activities and sector-specific needs when implementing workflow technology. The GDPR has an impact on the way businesses handle personal data, its collection, processing, storage and sharing. Workflow platforms ought consequently to offer proper access controls, audit trails and retention settings. It is also essential that buyers are aware of the location of information, the way that external parties use it, and whether the system configuration meets the legal and contractual requirements of the organisation. Legacy technology and inconsistent procedures are still important barriers to implementation. Departments can have copies kept, different approval procedures and rely on manual processes that employees know and do not always write down. Automating such processes without considering how the process is designed can magnify inefficiencies. The quality of integration can make the difference between enterprise-wide value in workflow solutions and doing the same thing again. Finance, HR, procurement and customer service departments often use different applications and have different data structures and access controls. Strategic Process Design Determines Long-Term Value Organisations require measurable baselines to check if automation results in sustainable business value. Some metrics that can be helpful are processing speed, error rates, approval times, cost per transaction, and the number of cases that need manual intervention as a fraction of the total number of cases. These measures give the leaders the ability to have a comparison of the performance before and after their measures and to act on that. They also help to differentiate value-added productivity enhancements from the tasks that simply move from one department to another. The adoption of employee initiatives is also important for the same effect. Communication and training are vital as automation fundamentally alters the way staff receive requests, approve decisions and deal with exceptions. Staff should be aware of new duties, the escalation process and restrictions on automated suggestions. User engagement in process design can uncover real-world challenges that the technical staff may overlook. Organisations should also have an easy way to report errors and make suggestions for changes, so that workflows adapt to the changing needs of the business. Workflow solutions are heading toward tighter integration of automation, AI agents and enterprise applications. Flexible integration with transparent controls, reliable monitoring and measurable performance will become more important for European businesses to have available platforms. The best implementations will relate investments in technology to the well-designed processes that make use of it, instead of only seeing it as software.   ...Read more

FREMONT CA:  Google (NASDAQ: GOOG) recently unveiled its groundbreaking quantum computing chip, Willow, marking a significant milestone in the journey toward practical quantum computing. This state-of-the-art chip can perform complex computations at unprecedented speeds, solving tasks that would take classical supercomputers an unfathomable amount of time—precisely, around ten septillion years—in less than five minutes. This performance highlights the transformative potential of quantum technology in various fields, including artificial intelligence, cryptography and complex problem-solving. The Willow chip is equipped with 105 qubits.. Unlike classical bits that represent either a 0 or a 1, qubits can exist in multiple states simultaneously due to the principles of quantum superposition and entanglement. This unique property allows Willow to process vast amounts of data simultaneously, performing significantly faster than traditional systems. Enhanced Qubit Connectivity One of Willow's critical advancements is its improved qubit connectivity. The design allows for more efficient communication between qubits, which is essential for executing complex quantum algorithms. This connectivity facilitates scaling up of qubit numbers while maintaining performance integrity, which is vital for tackling increasingly complex problems. Real-Time Error Correction Error management has long been a significant challenge in quantum computing. As more qubits are added to a system, the error rates typically increase due to environmental noise and instability in qubit states. Willow addresses this issue through real-time error correction, which allows it to maintain low error rates even as it scales up. The chip achieves an exponential reduction in error rates as more qubits are utilized, a breakthrough researchers have sought for nearly three decades. Performance Metrics In benchmark tests, Willow completed a random circuit sampling (RCS) task—considered one of the most challenging benchmarks for quantum computers—in record time. This test measures computational speed and assesses whether a quantum computer can perform tasks beyond the reach of classical systems. The successful execution of this benchmark reinforces Google's position at the forefront of quantum research and development. Implications of Willow for Various Industries The implications of Willow's capabilities extend beyond theoretical computations. The chip's extraordinary processing power could revolutionize industries reliant on complex data analysis and optimization, such as the following. Drug Discovery: By simulating molecular interactions at unprecedented speeds, Willow could accelerate the development of new pharmaceuticals. Artificial Intelligence: Enhanced computational capabilities could lead to breakthroughs in machine learning algorithms and data processing. Cryptography: As quantum computers become more powerful, they pose opportunities for advancing encryption technologies. The Future Outlook Hartmut Neven, founder and lead of Google’s Quantum AI lab, emphasized that Willow is a major step toward demonstrating “useful, beyond-classical” computations. The chip represents not just an incremental improvement but a leap toward realizing large-scale quantum computing that can harness the principles of quantum mechanics for practical applications. Willow signifies a pivotal advancement in quantum computing technology. By overcoming significant challenges related to error rates and computational speed, it sets the stage for future explorations into solving problems currently deemed impossible by classical systems. As research continues and technology matures, the chip may play a crucial role in addressing some of society's most pressing challenges through enhanced scientific discovery and innovative applications. ...Read more

Large language models (LLMs), the backbone of today’s generative AI, have quietly transformed the way machines understand and produce human language. What once seemed like a distant dream is now a reality—these models craft stories, compose poetry and tackle complex questions, unlocking a new era of AI-driven possibilities. The origins of LLMs can be traced back to the breakthrough paper on neural machine translation in 2014, which introduced the attention mechanism. This was further refined in 2017 with the release of the transformer model, significantly enhancing data processing efficiency. Modern LLMs, such as OpenAI’s GPT series and Google’s BERT, are built on this transformer foundation, driving the impressive capabilities witnessed today across the globe.  Innovations Shaping the Future of Modern LLMs Several influential LLMs are currently shaping the AI landscape, driving advancements in natural language processing and setting the blueprint for the next generation of intelligent systems. Here are a few models leading the charge: Claude Developed by Anthropic, Claude focuses on constitutional AI, ensuring that its outputs are guided by principles aimed at making interactions helpful, harmless and accurate. The most recent iteration, Claude 3.5 Sonnet, stands out for its improved ability to comprehend nuance, humour and complex instructions, making it adept at handling a wide range of tasks. In October 2024, Claude was further enhanced with the introduction of a computer-use AI tool, allowing it to operate a computer in a manner akin to human interaction. This new feature is available through the Claude iOS app and an API, making it more accessible for developers and users seeking a sophisticated, versatile AI experience. DeepSeek R-1 DeepSeek-R1 is an open-source reasoning model designed to tackle tasks that demand complex reasoning, mathematical problem-solving and logical inference. The model continuously refines its ability to solve intricate problems by leveraging reinforcement learning techniques, enhancing its overall effectiveness. It also excels in critical problem-solving scenarios by utilising self-verification, chain-of-thought reasoning and reflective processes, allowing it to approach challenges with a higher degree of accuracy and precision.  Merit Data and Technology , recently recognized as the Top AI-Driven Data Solutions Provider in UK by CIOReview Europe  has been at the forefront of utilizing data-driven solutions to optimize e-commerce platforms. Their innovative approach to AI integration and data management has made significant contributions to the industry's growth. Ernie   Since its launch in August 2023, the Ernie 4.0 chatbot has rapidly gained popularity, amassing over 45 million users. While Ernie is rumoured to be equipped with an impressive 10 trillion parameters, its capabilities extend beyond sheer size. The model excels primarily in Mandarin, offering highly refined performance in the language while also being proficient in other languages.  Falcon   Developed by the Technology Innovation Institute, Falcon is a family of transformer-based models renowned for its open-source nature and multilingual capabilities. The Falcon 1 series further expands the model's versatility with larger variants, such as Falcon 40B and Falcon 180B, designed to handle more demanding tasks and enhance performance.  Meanwhile, Falcon 2 stands out with an impressive 11-billion-parameter version that supports multimodal functionality, enabling it to process text and visual data. These models are freely available on GitHub and can also be accessed through major cloud platforms, including Amazon Web Services. Gemini  Replacing the earlier PaLM model, Gemini introduced a rebranding of Google’s chatbot from Bard to Gemini. This model can process text, images, audio and video, making it exceptionally versatile for various applications. Among the most notable recent updates is the Gemini 1.5 Pro, released in May 2024, which brought significant improvements in performance. Gemini is also accessible as a web-based chatbot through Google’s Vertex AI service and via API, allowing developers and businesses to tap into its capabilities. Early previews of the Gemini 2.0 Flash, introduced in December 2024, offer even more advanced multimodal generation capabilities, marking a significant leap forward in AI-driven content creation. Gemma With their open-source nature and versatile deployment options, Gemma models provide a valuable resource for a wide range of natural language processing tasks. Developed by Google, these open-source language models are trained on the same high-quality resources as Gemini. Its Gemma 2 model offers two distinct versions—a 9 billion parameter model and a 27 billion parameter model—each designed to cater to different performance needs.  As new models continue to emerge, enhancing and challenging the status quo, it becomes clear that the pace of innovation in this field shows no signs of slowing down. The future promises even more groundbreaking advancements, making it an exciting time for AI enthusiasts and developers. ...Read more