text: Jason Morris, SEO Marketing Manager, Brew Digital
  Content creation has been fundamentally and forever changed by GenAI tools. They offer a fast and efficient way of building content, in a matter of seconds, and a majority of businesses have been deploying it in some capacity for the last two years.
  In particular, tools like ChatGPT, Copy.ai and Jasper have transformed copywriting, allowing businesses to quickly turn around high-quality content, social media content, blog posts and marketing copy, in a matter of minutes. It has also revolutionised our approach to SEO, helping with everything from optimising keywords to generating content that meets search engine standards.
  However, the same capability that makes AI attractive – its speed in generating content – can result in copy that feel generic and impersonal, risking the intuition and creativity that are essential for compelling brand messaging. This is because AI, by nature, operates on probabilistic models that predict the most likely next word or phrase based on patterns in the data it has been trained on.
  Large language models (LLMs), are excellent at identifying and replicating patterns, but lack the creativity and intuition that come from human experience. While this can be useful for producing factual or structured content, it often results in formulaic writing that lacks originality and depth. In areas like storytelling or thought leadership, where original insights are key, AI struggles to deliver the same level of nuance and emotional connection that human writers can achieve.
  Inauthentic writing does not just risk disengaging brand audiences, either, and many search algorithms are now designed to detect web pages with AI-generated content and diminish its domain authority. This will inevitably lead to diminished visibility and a drop in organic traffic, and is exemplified by Google removing a staggering 40% of indexed web pages for failing to meet its E-E-A-T (Expertise, Experience, Authoritativeness, Trustworthiness) standards. This is a trend we can expect to see continue as GenAI created content becomes even more prevalent.
  Thus, the challenge for brands in the age of AI is to harness the power of efficiency without losing the unique voice that sets them apart.
  ‘Bans’ are counter-intuitive
  The first step in striking the balance between efficiency and authenticity is to establish blueprints for how AI can and should be used within a business setting. For companies keen to retain that human flair, limiting AI usage may seem attractive. However, staff will use GenAI for content creation regardless of whether or not their employers tell them not to. In fact, a blanket ban on GenAI will only lead to the bigger challenge of ‘Shadow AI’.
  Recent research shows that 78% of knowledge workers use their own AI tools to complete work, yet 52% don’t disclose this to employers. This not only poses significant organisational risks like data breaches, compliance violations, and security threats, but it also means business leaders will never have full transparency over how much, and how often, GenAI is being used to create the messaging that’s being published for customer or public consumption.
  So how should GenAI be used?
  For businesses that regularly need to create high-volumes of detailed content, GenAI tools like Jasper can provide crucial support for building first drafts, generating ideas, and providing digestible summaries or even detecting mistakes across existing copy. This process of leaving the more time-consuming content creation tasks to AI frees up considerable time for content creators and enables them to focus on strategy and creativity.
  Likewise, tools such as MarketMuse can analyse audience behaviour and preferences, audit existing content, suggest SEO optimisation, and identify gaps in coverage. These data-driven insights enable brands to create more tailored and engaging content, improving both reach and conversion rates.
  Another significant advantage is consistency. Ensuring a uniform tone and style across various platforms is essential for cultivating a cohesive brand identity. AI tools play a crucial role here, by supporting content so that it aligns with established brand guidelines. By maintaining consistent messaging, businesses can strengthen their connection with their audience and foster long-term trust.
  For businesses and content creators looking to use GenAI to create content which will be made public, it’s essential for them to review, refine and personalise outputs extremely carefully. This ensures that the final output remains true to the brand’s voice and offers genuine value to the audience.
  GenAI is no substitute for human storytelling
  While AI can support efficiency, human insight is essential for crafting content that truly resonates. This is especially true in storytelling and in complex strategies like seo consultancy, where aligning with audience expectations requires a thoughtful, hands-on approach.
  Humans are still best equipped to connect with audiences on an emotional level. Storytelling, creativity, and real-life experience are areas where human writers hold a distinct advantage.
  By blending AI’s efficiency with the creativity and empathy that only humans can bring, brands can produce content that is not only high-quality but also deeply engaging. AI tools, therefore, should complement human creativity, not replace it. Striking this balance allows businesses to maximise efficiency without sacrificing authenticity, creating content that resonates and builds long-term connections with audiences.
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  Artificial intelligence will revolutionise the manufacturing industry: here’s how
  By Grace Nam, Strategic Solutions Manager at Laserfiche
  Much of the manufacturing industry continues to rely on traditional ways of handling data and documentation, with scattered technology and complex processes acting as barriers to progress and hybrid working. This siloed information coupled with rising costs, logistical uncertainty, and doubts over the supply of essential raw materials is making manufacturing stagnant.
  The answer is digitalised smart systems and processes. Innovations such as AI-powered cloud document management systems (DMS) can control and organise critical data, creating a single source of truth to streamline processes and provide multi-layer protection against missing documentation to comply with regulations. Let’s look at some of the core ways that AI will revolutionise manufacturing: minimising errors, empowering employees, and attracting talent.
  Harmonising processes, freeing up time
  First, by digitalising data and investing in AI integrations, manufacturing leaders can reduce margin for error. While the integration of IOT may appear to garner similar results, the real impact of AI is incredibly different.
  IOT focuses on supporting machines and network enablement. On the other hand, AI supports the functionalities that are traditionally confined to the realm of human responsibility and intelligence. While IOT connects physical objects to manufacturing networks, AI harmonises the entire process and structures unstructured data.
  The value of AI lies in its scalability. AI can reduce tedious data entry for employees and eliminate delays, having the potential to transform employees’ lives by reclaiming time for valuable projects and saving resources.
  Creating more efficient process cycles
  AI also enables manufacturing leaders to enhance process automation, making the more time-consuming elements of the manufacturing process rule-based and decision-driven. For instance, in a recent SME study that surveyed over 300 manufacturing professionals, one-third of respondents reported experiencing work delays a few times a week across various operational processes.
  As is the case with minimising errors, the role of AI is to free up human employees for other tasks, not replace them. AI and ML are invaluable tools to save countless hours wasted by employees fulfilling administrative tasks, reclaiming significant amounts of time that can be redirected to supporting customers. Manufacturing companies can implement processes driven by AI and ML to streamline areas of the business and integrate processes that often experience delays, as well as augmenting high-cost processes involved with compliance documentation to create better process cycles.
  AI-powered decision making
  With endless data at their fingertips, leaders can utilise AI to gain insights and power decision-making. This technology gives organisations the ability to centralise customer information and order history and gather product information that involves hundreds or even thousands of parts, where each part carries its own unique identification number.
  Another example could be analysing the data in relation to how suppliers perform, enabling the manufacturing organisation to better understand what to expect and prepare for potential pitfalls in advance.
  AI-powered technologies are also being implemented to resolve interoperability issues, enabling computer systems and software to exchange and make use of information across platforms. Allowing data to be shared between different software and technologies will help in streamlining processes.
  By utilising process automation and enhancing data processing speed, we can expect organisations to see an increase in operational efficiency. These improved systems will reduce costs while improving scalability and flexibility, enabling the streamlined sharing of data across the business.
  Looking ahead: the future of manufacturing
  AI-powered solutions improve the working lives of almost everyone in the sector, helping them make better informed decisions at speed, while process automation solutions empower remote employees who are integral to the success of future workforces. This innovation is vital for businesses to attract, and retain, new generations of talent.
  AI and machine learning are offering the manufacturing industry the opportunity to unlock new levels of efficiency and create a solid foundation for future growth and innovation. From sales and supply chain management to quality checks and inventory control, AI is streamlining complex processes, predicting potential issues, and ensuring timely delivery of projects.
  It’s not just about keeping up with technological trends and jumping on the bandwagon. AI proves its worth for manufacturers aiming to make critical decisions promptly, effectively address high-cost functionalities, streamline operations, ensure accuracy of compliance documentation, enhance scope for innovation, boost ROI, and improve sustainability. By bringing together human intuition with the speed and scale of AI technologies, manufacturers can remain competitive – and continue to evolve – for years to come.
  The role of leadership during digital transformation projects in the financial services sector
  Stephen Foreshew-Cain is CEO of Scott Logic, the specialist software consultancy
  Until around a decade ago, the UK financial services and banking industries were dominated by a core group of major players who controlled almost the entire market. They built up their offering over a period of decades – even centuries in some cases – and invested in robust, weighty technology platforms pre-millennium that were seemingly built to last and which most hoped could be relied on for years to come.
  These businesses still hold significant market share, but in recent years their position has come under attack from the emergence of several notable challenger banks. This change has slowly but surely driven the need for digital transformation within the established financial services market and seen many firms consider modernising their offering. However, the success of these programmes depends on much more than buying the latest off-the-shelf package, and there are several elements that dictate how effective this change really is. Leadership is one of those factors; but what’s driving this need for digital transformation and why is having the right management in place so critical to success?
  Perhaps no other sector has felt the pressure to modernise itself as much as financial services, where growing security challenges – and the rise of the aforementioned emerging brands – have made this a priority. These newer digitally native organisations are setting the standards for customer experiences and operational efficiency, and the more long-standing institutions are battling to keep up. The challengers have demonstrated how a digital-first approach can offer a superior customer experience and deliver greater operational efficiency. These banks have built their infrastructure from the ground up and have leveraged big data, embraced cloud-native architectures, and developed a focus on user-centric design and experiences. And their streamlined processes, fast decision-making, and commitment to innovation have won them a growing share of the market.
  In contrast, many of the more established financial groups are built on platforms that were developed decades ago. While this may sound staggering, it’s understandable. It’s only in recent years that the cloud has matured sufficiently to offer a viable migration path to these institutions, with more benefits than were previously available.
  In addition, there is an inherent resistance to change amongst more long-standing institutions – again, for understandable reasons; much of their success is based on caution and reducing risk. One wrong move or misstep can naturally set off a domino effect that has ramifications around the world, as seen in the global financial crisis in 2008. Convincing these organisations to go through the process of upgrading their systems to replace a platform with another more modern one, that does something very similar, is challenging. Hierarchical organisational structures can often hinder rapid decision-making and the swift adoption of innovative and emerging technologies.
  Despite these challenges, now is the time for change. External market forces, improved customer experience, cyber-resiliency, and the ability to meet evolving regulatory requirements means that migration from legacy is no longer a ‘nice to have’. The pace of evolution in modern technology means that the longer organisations wait, the rate at which challengers outstrip them will only accelerate. Migration projects are as challenging as they ever were; however, it is now possible to employ migration strategies that allow a phased process, making them easier to manage from a risk mitigation perspective. Any remaining reasons to delay are now massively outweighed by the reasons to proceed with migration – frankly, it’s now imperative.
  The pivotal role of leadership
  That’s not to say that all modernisation programmes are a success; research from McKinsey indicates that over 70% of digital transformation projects fall short of their goals. There is a range of factors to this, but a common recurring theme is ineffective leadership. Without leaders capable of reimagining these structures and securing buy-in from the workforce, digital transformation initiatives are always likely to encounter issues.
  However, in the context of digital transformation, leadership is more than just guiding an organisation through technology adoption. These individuals are responsible for setting the tone and direction for change, building a sense of urgency, and ensuring that the everyone understands the purpose behind the transformation. Remembering the quote attributed to Peter Drucker, culture eats strategy for lunch. The best managers will be able to set a clear vision for change and convey palpably how it will benefit customers and colleagues. They will also be adept at fostering a more agile culture, encouraging collaboration and a sense of continuous learning across teams. To horribly torture Drucker’s famous statement, if strategy is lunch, leaders need to make sure culture is the breakfast of champions.
  There’s a form of culture that’s more than “the way we do things here”. It’s something that’s baked in; the structural risk aversion that often critically undermines the change programmes organisations set out to deliver. It doesn’t matter how many digital experts an institution hires to lead the way into a digital future if outdated governance approaches fetter initiatives before they can get out of the starting blocks. Leaders need to be given the scope to propose new approaches to investing in infrastructure – ones that not only balance investment in legacy modernisation with investment in innovation, but which allow the two to work in symbiosis.
  Fundamentally, culture should not be allowed to eat innovation for lunch; leaders need to be free to keep pace with innovation beyond the ‘walls’ of their institution – free indeed to conceive of those walls breaking down as the banking ecosystem of the future takes shape.
  What’s next for EAM? Get ready for an AI-inspired future
  By Berend Booms, Head of Enterprise Asset Management Insights at IFS Ultimo
  More and more organisations in every sector are investing in artificial intelligence (AI) technologies that will enable them to automate and transform their operations. Generative AI (GenAI) is the latest innovation to drive the current wave of AI adoption and, according to Gartner, by 2027 more than 50 percent of the GenAI tools enterprises deploy will be utilised for a specific industry task or business function.
  In 2024, the global AI market jumped beyond $184 billion – a growth of nearly 50 billion US dollars compared to 2023 as businesses put AI to work to deliver new business value. Top use cases include chatbots and voice assistants, IT operations management, process automation, financial reporting and analysis, and scheduling optimisation to name but a few. However, according to tech industry analysts, AI is now set to revolutionise how industrial businesses boost productivity and performance while reducing operational risk.
  The big challenge for today’s industrial firms is understanding how to best unlock this potential and maximise value from their AI investments from Day One. That’s because the integration of AI in Enterprise Asset Management (EAM) platforms is set to radically enhance how organisations operating in industrial sectors such as manufacturing, logistics and food production improve reliability and streamline their operations.
  Let’s look at how these firms can expect to derive measurable benefits and value from their AI-infused EAM investments.
  A world of opportunity
  Indications are that AI will help break the productivity impasse that hampers many industrial organisations from achieving measurable gains and bedding in sustainable growth. For example, deploying AI and automation technologies to address workforce scarcity issues and reduce the amount of time that workers spend on mundane repetitive tasks. But that’s not all.
  Research by McKinsey has found that following the successful implementation of AI technologies, companies were able to cut logistics costs by 15 percent, achieve a 35 percent improvement in inventory management and realise a 65 percent increase in service level agreements met. Meanwhile, AI-supported maintenance is set to maximise operational productivity by using machine learning to predict and prevent equipment failures. AI enabled predictive maintenance can extend asset lifespans by 20-40 percent.
  By leveraging AI, future EAM toolsets will become more intuitive, accessible and predictive. All of which will deliver a comprehensive and data rich overview of asset productivity, uptime and cost alongside new and highly coordinated maintenance management capabilities.
  Be that as it may, AI will not leapfrog organisations’ journey towards predictive maintenance instantaneously. Significant investments are imperative for many of the new gen AI functionalities – an expenditure many organisations are not able to commit to. Organisations starting out on their AI journey should seek out ways to generate incremental gains fast while retailing complete control over their AI implementation and outcomes.
  With reactive maintenance being the reality of the situation for most industrial organisations, companies should instead focus their attention on EAM solutions capable of realising fast time to value without any additional investment requirements. The good news is that AI-infused EAM solutions are now making it easy to pursue a step-by-step approach to improving how organisations manage and maintain their physical assets.
  Elevating the effectiveness of reactive maintenance
  For many industrial organisations, predictive maintenance is a long-term goal rather than a short-term reality. That said, today’s AI-enhanced EAM solutions enable industrial firms to significantly reduce the time they currently spend undertaking reactive maintenance. By realising these benefits today, they will be able to start working towards their future predictive maintenance vision.
  For example, with the average cost of downtime in manufacturing often exceeding $100K an hour, maintenance and operational leaders know that any reduction in Mean Time to Repair (MTTR) translates into increased asset productivity and thousands of dollars in downtime saved. However, 80 percent of MTTR is typically wastage that arises from poor inter-team communications and a lack of detailed failure reports that make the diagnosis process more time-consuming and cumbersome.
  Traditional asset failure reports often contain too few details, which frustrates the efforts of technicians tasked with pinpointing issues. Yet the use of AI embedded in the latest iterations of EAM software can reduce diagnosis time significantly, with every percent reduction in MTTR equating to cost savings of thousands of dollars. This is on top of the boost in productivity for everyone involved in the maintenance process.
  For organisations this means they can utilise AI functionality to not only improve fault reporting but enable better inter-team collaboration and more hands-on ‘wrench time’ for engineers. All of which helps to maximise the productivity of highly skilled employees, so they can increase asset availability in a more streamlined and efficient way.
  Frontline workers spend most their day working with critical assets. They know these assets inside and out. They know what the assets smell like, what they sound like and what they are supposed to look like. Any irregularities will not pass them by. Capturing these sensory observations in their failure reports will greatly expedite the MTTR on any incident raised. But that’s not the only real-world benefit that’s up for grabs.
  Harnessing the power of data
  Besides slashing MTTR times, today’s AI-powered EAM tools enable organisations to elevate their data management capabilities from good to great.
  Alongside automating data collection and delivering access to better quality data that adds value instantly, these systems provide powerful reporting and dashboarding tools that enable users to make sense of and operationalise data. When combined with machine learning, this will significantly enhance the error detection and prevention capabilities of organisations looking to move the dial where preventative and predictive maintenance is concerned.
  Similarly, these systems make it possible to accurately capture every change, move and repair in a truly user-friendly way. For example, using AI-powered tools to take photo-based meter readings, autogenerate image captions and auto-translate multilingual data.
  Working towards an AI-inspired EAM future
  Today’s industrial firms want to overcome the multiple challenges that can get in the way of improving uptime, optimising maintenance schedules and elevating asset performance. Today’s AI-powered EAM systems are capable of analysing asset information and suggesting diagnostic and mitigation actions that enhance troubleshooting processes and reduce unplanned interruptions.
  One thing is for sure: AI will take EAM to the next level and here at IFS Ultimo we’re focused on the addition of AI features that make it easy for organisations to incrementally apply AI to real-world use cases and generate measurable value – at a pace that works best for them.