Modern healthcare innovations span AI, devices, software, images, and regulatory frameworks, all requiring stringent coordination. Generative AI arguably has the strongest transformative potential in healthcare technology programmes, with it already being applied across various domains, such as R&D, commercial operations, and supply chain management.Traditional models for medical appointments, like face-to-face appointments, and paper-based processes may not be sufficient to meet the fast-paced, data-driven medical landscape of today. Therefore, healthcare professionals and patients are seeking more convenient and efficient ways to access and share information, meeting the complex standards of modern medical science. According to McKinsey, Medtech companies are at the forefront of healthcare innovation, estimating they could capture between billion and billion annually in productivity gains. Through GenAI adoption, an additional billion plus in revenue is estimated from products and service innovations. A McKinsey 2024 survey revealed around two thirds of Medtech executives have already implemented Gen AI, with approximately 20% scaling their solutions up and reporting substantial benefits to productivity.  While advanced technology implementation is growing across the medical industry, challenges persist. Organisations face hurdles like data integration issues, decentralised strategies, and skill gaps. Together, these highlight a need for a more streamlined approach to Gen AI deployment. Of all the Medtech domains, R&D is leading the way in Gen AI adoption. Being the most comfortable with new technologies, R&D departments use Gen AI tools to streamline work processes, such as summarising research papers or scientific articles, highlighting a grassroots adoption trend. Individual researchers are using AI to enhance productivity, even when no formal company-wide strategies are in place.While AI tools automate and accelerate R&D tasks, human review is still required to ensure final submissions are correct and satisfactory. Gen AI is proving to reduce time spent on administrative tasks for teams and improve research accuracy and depth, with some companies experiencing 20% to 30% gains in research productivity. KPIs for success in healthcare product programmesMeasuring business performance is essential in the healthcare sector. The number one goal is, of course, to deliver high-quality care, yet simultaneously maintain efficient operations. By measuring and analysing KPIs, healthcare providers are in a better position to improve patient outcomes through their data-based considerations. KPIs can also improve resource allocation, and encourage continuous improvement in all areas of care. In terms of healthcare product programmes, these structured initiatives prioritise the development, delivery, and continual optimisation of medical products. But to be a success, they require cross-functional coordination of clinical, technical, regulatory, and business teams. Time to market is critical, ensuring a product moves from the concept stage to launch as quickly as possible.Of particular note is the emphasis needing to be placed on labelling and documentation. McKinsey notes that AI-assisted labelling has resulted in a 20%-30% improvement in operational efficiency. Resource utilisation rates are also important, showing how efficiently time, budget, and/or headcount are used during the developmental stage of products. In the healthcare sector, KPIs ought to focus on several factors, including operational efficiency, patient outcomes, financial health of the business, and patient satisfaction. To achieve a comprehensive view of performance, these can be categorised into financial, operational, clinical quality, and patient experience.Bridging user experience with technical precision – design awardsInnovation is no longer solely judged by technical performance with user experiencebeing equally important. Some of the latest innovations in healthcare are recognised at the UX Design Awards, products that exemplify the best in user experience as well as technical precision. Top products prioritise the needs and experiences of both patients and healthcare professionals, also ensuring each product meets the rigorous clinical and regulatory standards of the sector. One example is the CIARTIC Move by Siemens Healthineers, a self-driving 3D C-arm imaging system that lets surgeons operate, controlling the device wirelessly in a sterile field. Computer hardware company ASUS has also received accolades for its HealthConnect App and VivoWatch Series, showcasing the fusion of AIoT-driven smart healthcare solutions with user-friendly interfaces – sometimes in what are essentially consumer devices. This demonstrates how technical innovation is being made accessible and becoming increasingly intuitive as patients gain technical fluency.  Navigating regulatory and product development pathways simultaneously The establishing of clinical and regulatory paths is important, as this enables healthcare teams to feed a twin stream of findings back into development. Gen AI adoption has become a transformative approach, automating the production and refining of complex documents, mixed data sets, and structured and unstructured data. By integrating regulatory considerations early and adopting technologies like Gen AI as part of agile practices, healthcare product programmes help teams navigate a regulatory landscape that can often shift. Baking a regulatory mindset into a team early helps ensure compliance and continued innovation. Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.Explore other upcoming enterprise technology events and webinars powered by TechForge here. #medtech #hardware #clinical #application #programmes

Germany and NVIDIA are building possibly the most ambitious European tech project of the decade: the continent’s first industrial AI cloud.NVIDIA has been on a European tour over the past month with CEO Jensen Huang charming audiences at London Tech Week before dazzling the crowds at Paris’s VivaTech. But it was his meeting with German Chancellor Friedrich Merz that might prove the most consequential stop.The resulting partnership between NVIDIA and Deutsche Telekom isn’t just another corporate handshake; it’s potentially a turning point for European technological sovereignty.An “AI factory”will be created with a focus on manufacturing, which is hardly surprising given Germany’s renowned industrial heritage. The facility aims to give European industrial players the computational firepower to revolutionise everything from design to robotics.“In the era of AI, every manufacturer needs two factories: one for making things, and one for creating the intelligence that powers them,” said Huang. “By building Europe’s first industrial AI infrastructure, we’re enabling the region’s leading industrial companies to advance simulation-first, AI-driven manufacturing.”It’s rare to hear such urgency from a telecoms CEO, but Deutsche Telekom’s Timotheus Höttges added: “Europe’s technological future needs a sprint, not a stroll. We must seize the opportunities of artificial intelligence now, revolutionise our industry, and secure a leading position in the global technology competition. Our economic success depends on quick decisions and collaborative innovations.”The first phase alone will deploy 10,000 NVIDIA Blackwell GPUs spread across various high-performance systems. That makes this Germany’s largest AI deployment ever; a statement the country isn’t content to watch from the sidelines as AI transforms global industry.A Deloitte study recently highlighted the critical importance of AI technology development to Germany’s future competitiveness, particularly noting the need for expanded data centre capacity. When you consider that demand is expected to triple within just five years, this investment seems less like ambition and more like necessity.Robots teaching robotsOne of the early adopters is NEURA Robotics, a German firm that specialises in cognitive robotics. They’re using this computational muscle to power something called the Neuraverse which is essentially a connected network where robots can learn from each other.Think of it as a robotic hive mind for skills ranging from precision welding to household ironing, with each machine contributing its learnings to a collective intelligence.“Physical AI is the electricity of the future—it will power every machine on the planet,” said David Reger, Founder and CEO of NEURA Robotics. “Through this initiative, we’re helping build the sovereign infrastructure Europe needs to lead in intelligent robotics and stay in control of its future.”The implications of this AI project for manufacturing in Germany could be profound. This isn’t just about making existing factories slightly more efficient; it’s about reimagining what manufacturing can be in an age of intelligent machines.AI for more than just Germany’s industrial titansWhat’s particularly promising about this project is its potential reach beyond Germany’s industrial titans. The famed Mittelstand – the network of specialised small and medium-sized businesses that forms the backbone of the German economy – stands to benefit.These companies often lack the resources to build their own AI infrastructure but possess the specialised knowledge that makes them perfect candidates for AI-enhanced innovation. Democratising access to cutting-edge AI could help preserve their competitive edge in a challenging global market.Academic and research institutions will also gain access, potentially accelerating innovation across numerous fields. The approximately 900 Germany-based startups in NVIDIA’s Inception program will be eligible to use these resources, potentially unleashing a wave of entrepreneurial AI applications.However impressive this massive project is, it’s viewed merely as a stepping stone towards something even more ambitious: Europe’s AI gigafactory. This planned 100,000 GPU-powered initiative backed by the EU and Germany won’t come online until 2027, but it represents Europe’s determination to carve out its own technological future.As other European telecom providers follow suit with their own AI infrastructure projects, we may be witnessing the beginning of a concerted effort to establish technological sovereignty across the continent.For a region that has often found itself caught between American tech dominance and Chinese ambitions, building indigenous AI capability represents more than economic opportunity. Whether this bold project in Germany will succeed remains to be seen, but one thing is clear: Europe is no longer content to be a passive consumer of AI technology developed elsewhere.Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.Explore other upcoming enterprise technology events and webinars powered by TechForge here. #nvidia #helps #germany #lead #europes

Anthropic has unveiled a custom collection of Claude AI models designed for US national security customers. The announcement represents a potential milestone in the application of AI within classified government environments. The ‘Claude Gov’ models have already been deployed by agencies operating at the highest levels of US national security, with access strictly limited to those working within such classified environments. Anthropic says these Claude Gov models emerged from extensive collaboration with government customers to address real-world operational requirements. Despite being tailored for national security applications, Anthropic maintains that these models underwent the same rigorous safety testing as other Claude models in their portfolio. Specialised AI capabilities for national security The specialised models deliver improved performance across several critical areas for government operations. They feature enhanced handling of classified materials, with fewer instances where the AI refuses to engage with sensitive information—a common frustration in secure environments. Additional improvements include better comprehension of documents within intelligence and defence contexts, enhanced proficiency in languages crucial to national security operations, and superior interpretation of complex cybersecurity data for intelligence analysis. However, this announcement arrives amid ongoing debates about AI regulation in the US. Anthropic CEO Dario Amodei recently expressed concerns about proposed legislation that would grant a decade-long freeze on state regulation of AI. Balancing innovation with regulation In a guest essay published in The New York Times this week, Amodei advocated for transparency rules rather than regulatory moratoriums. He detailed internal evaluations revealing concerning behaviours in advanced AI models, including an instance where Anthropic’s newest model threatened to expose a user’s private emails unless a shutdown plan was cancelled. Amodei compared AI safety testing to wind tunnel trials for aircraft designed to expose defects before public release, emphasising that safety teams must detect and block risks proactively. Anthropic has positioned itself as an advocate for responsible AI development. Under its Responsible Scaling Policy, the company already shares details about testing methods, risk-mitigation steps, and release criteria—practices Amodei believes should become standard across the industry. He suggests that formalising similar practices industry-wide would enable both the public and legislators to monitor capability improvements and determine whether additional regulatory action becomes necessary. Implications of AI in national security The deployment of advanced models within national security contexts raises important questions about the role of AI in intelligence gathering, strategic planning, and defence operations. Amodei has expressed support for export controls on advanced chips and the military adoption of trusted systems to counter rivals like China, indicating Anthropic’s awareness of the geopolitical implications of AI technology. The Claude Gov models could potentially serve numerous applications for national security, from strategic planning and operational support to intelligence analysis and threat assessment—all within the framework of Anthropic’s stated commitment to responsible AI development. Regulatory landscape As Anthropic rolls out these specialised models for government use, the broader regulatory environment for AI remains in flux. The Senate is currently considering language that would institute a moratorium on state-level AI regulation, with hearings planned before voting on the broader technology measure. Amodei has suggested that states could adopt narrow disclosure rules that defer to a future federal framework, with a supremacy clause eventually preempting state measures to preserve uniformity without halting near-term local action. This approach would allow for some immediate regulatory protection while working toward a comprehensive national standard. As these technologies become more deeply integrated into national security operations, questions of safety, oversight, and appropriate use will remain at the forefront of both policy discussions and public debate. For Anthropic, the challenge will be maintaining its commitment to responsible AI development while meeting the specialised needs of government customers for crtitical applications such as national security.See also: Reddit sues Anthropic over AI data scraping Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo. Explore other upcoming enterprise technology events and webinars powered by TechForge here. The post Anthropic launches Claude AI models for US national security appeared first on AI News. #anthropic #launches #claude #models #national

Reddit is accusing Anthropic of building its Claude AI models on the back of Reddit’s users, without permission and without paying for it.Anyone who uses Reddit, even a web-crawling bot, agrees to the site’s user agreement. That agreement is clear: you cannot just take content from the site and use it for your own commercial products without a written deal. Reddit claims Anthropic’s bots have been doing exactly that for years, scraping massive amounts of conversations and posts to train and improve Claude.What makes this lawsuit particularly spicy is the way it goes after Anthropic’s reputation. Anthropic has worked hard to brand itself as the ethical, trustworthy AI company, the “white knight” of the industry. The lawsuit, however, calls these claims nothing more than “empty marketing gimmicks”.For instance, Reddit points to a statement from July 2024 where Anthropic claimed it had stopped its bots from crawling Reddit. The lawsuit says this was “false”, alleging that its logs caught Anthropic’s bots trying to access the site more than one hundred thousand times in the following months.But this isn’t just about corporate squabbles; it directly involves user privacy. When you delete a post or a comment on Reddit, you expect it to be gone. Reddit has official licensing deals with other big AI players like Google and OpenAI, and these deals include technical measures to ensure that when a user deletes content, the AI company does too.According to Reddit’s lawsuit, Anthropic has no such deal and has refused to enter one. This means if their AI was trained on a post you later deleted, that content could still be baked into Claude’s knowledge base, effectively ignoring your choice to remove it. The lawsuit even includes a screenshot where Claude itself admits it has no real way of knowing if the Reddit data it was trained on was later deleted by a user:So, what does Reddit want? It’s not just about money, although they are asking for damages for things like increased server costs and lost licensing fees. They are asking the court for an injunction to force Anthropic to stop using any Reddit data immediately.Furthermore, Reddit wants to prohibit Anthropic from selling or licensing any product that was built using that data. That means they’re asking a judge to effectively take Claude off the market.This case forces a tough question: Does being “publicly available” on the internet mean content is free for any corporation to take and monetise? Reddit is arguing a firm “no,” and the outcome could change the rules for how AI is developed from here on out.Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.Explore other upcoming enterprise technology events and webinars powered by TechForge here. #reddit #sues #anthropic #over #data

John Edwards, Technology Journalist & AuthorJune 5, 20254 Min ReadSailGP Australia, USA, and Great Britain racing on San Francisco Bay, CaliforniaDannaphotos via Alamy StockWarren Jones is CTO at SailGP, the organizer of what he describes as the world's most exciting race on water. The event features high-tech F50 boats that speed across the waves at 100 kilometers-per-hour.  Working in cooperation with Oracle, Jones focuses on innovative solutions for remote broadcast production, data management and distribution, and a newly introduced fan engagement platform. He also leads the team that has won an IBC Innovation Awards for its ambitious and ground-breaking remote production strategy. Among the races Jones organizes is the Rolex SailGP Championship, a global competition featuring national teams battling each other in identical high-tech, high-speed 50-foot foiling catamarans at celebrated venues around the world. The event attracts the sport's top athletes, with national pride, personal glory, and bonus prize money of million at stake. Jones also supports event and office infrastructures in London and New York, and at each of the global grand prix events over the course of the season. Prior to joining SailGP, he was IT leader at the America's Cup Event Authority and Oracle Racing. In an online interview, Jones discusses the challenges he faces in bringing reliable data services to event vessels, as well as onshore officials and fans. Related:Warren JonesWhat's the biggest challenge you've faced during your tenure? One of the biggest challenges I faced was ensuring real-time data transmission from our high-performance F50 foiling catamarans to teams, broadcasters, and fans worldwide. SailGP relies heavily on technology to deliver high-speed racing insights, but ensuring seamless connectivity across different venues with variable conditions was a significant hurdle. What caused the problem? The challenge arose due to a combination of factors. The high speeds and dynamic nature of the boats made data capture and transmission difficult. Varying network infrastructure at different race locations created connectivity issues. The need to process and visualize massive amounts of data in real time placed immense pressure on our systems. How did you resolve the problem? We tackled the issue by working with T-Mobile and Ericsson in a robust and adaptive telemetry system capable of transmitting data with minimal latency over 5G. Deploying custom-built race management software that could process and distribute data efficiently. Working closely with our global partner Oracle, we optimized Cloud Compute with the Oracle Cloud.  Related:What would have happened if the problem wasn't quickly resolved? Spectator experience would have suffered. Teams rely on real-time analytics for performance optimization, and broadcasters need accurate telemetry for storytelling. A failure here could have resulted in delays, miscommunication, and a diminished fan experience. How long did it take to resolve the problem? It was an ongoing challenge that required continuous innovation. The initial solution took several months to implement, but we’ve refined and improved it over multiple seasons as technology advances and new challenges emerge. Who supported you during this challenge? This was a team effort -- with our partners Oracle, T-Mobile, and Ericsson with our in-house engineers, data scientists, and IT specialists all working closely. The support from SailGP's leadership was also crucial in securing the necessary resources. Did anyone let you down? Rather than seeing it as being let down, I'd say there were unexpected challenges with some technology providers who underestimated the complexity of what we needed. However, we adapted by seeking alternative solutions and working collaboratively to overcome the hurdles. What advice do you have for other leaders who may face a similar challenge? Related:Embrace adaptability. No matter how well you plan, unforeseen challenges will arise, so build flexible solutions. Leverage partnerships. Collaborate with the best in the industry to ensure you have the expertise needed. Stay ahead of technology trends. The landscape is constantly evolving; being proactive rather than reactive is key. Prioritize resilience. Build redundancy into critical systems to ensure continuity even in the face of disruptions. Is there anything else you would like to add? SailGP is as much a technology company as it is a sports league. The intersection of innovation and competition drives us forward and solving challenges like these is what makes this role both demanding and incredibly rewarding. About the AuthorJohn EdwardsTechnology Journalist & AuthorJohn Edwards is a veteran business technology journalist. His work has appeared in The New York Times, The Washington Post, and numerous business and technology publications, including Computerworld, CFO Magazine, IBM Data Management Magazine, RFID Journal, and Electronic Design. He has also written columns for The Economist's Business Intelligence Unit and PricewaterhouseCoopers' Communications Direct. John has authored several books on business technology topics. His work began appearing online as early as 1983. Throughout the 1980s and 90s, he wrote daily news and feature articles for both the CompuServe and Prodigy online services. His "Behind the Screens" commentaries made him the world's first known professional blogger.See more from John EdwardsWebinarsMore WebinarsReportsMore ReportsNever Miss a Beat: Get a snapshot of the issues affecting the IT industry straight to your inbox.SIGN-UPYou May Also Like #racing #yacht #cto #sails #success

Reading Time: 3 minutes In a recent webinar hosted by MoEngage, QSR marketing experts from Radar and Bottle Rocket came together to unpack the findings of the 2025 State of Cross-Channel Marketing for QSRs report.  With more than 800 total survey responses, including 70 from QSR marketers, this report revealed where quick-service restaurants are focusing their energy, what is holding them back, and the emerging strategies reshaping the guest experience. Here is a recap of the key takeaways, expert insights, and actionable advice shared by our panelists.   Where QSRs Are Focused in 2025: Loyalty, Personalization & Speed The webinar kicked off with a deep dive into shifting priorities. Customer engagement and loyalty emerged as the top focus for QSR marketers in 2025, with 80% of respondents increasing investment in customer experience technology. Mobile-first experiences and real-time personalization are no longer optional; they are essential for effective QSR marketing. Nick Patrick, CEO of Radar, put it simply: “Mobile has become the primary interface between QSRs and their customers. Real-time context is what makes that interface intelligent.” Challenges: Disconnected Data, Tech Silos, and Execution Speed While QSRs have the vision, many struggle with execution.  The report found that 60% of QSR leaders still struggle with personalization, and more than a quarter cited siloed data as a top challenge. The panelists echoed these findings, pointing to fragmented systems and misaligned teams as major hurdles. Brendan shared: “The POS system was designed for speed and accuracy, not personalization. But when you can use even a basic signal, like a loyalty status, to prompt a more human, high-touch experience, it makes a real difference.” Strategies That Work: Start Small, Focus Deep, Earn Trust Panelists emphasized the value of starting small with high-impact initiatives like curbside pickup or loyalty nudges. Cross-functional alignment and choosing scalable tech partners were key themes. “Don’t boil the ocean,” Brendan advised. “Start with one moment in the journey that can be improved and work cross-functionally to get it right.” Earning Location Opt-ins the Right Way Location data is a powerful lever for marketing and operations, but only if opt-ins are earned with care. Nick shared a practical framework: “It comes down to three things: transparency, value, and timing. You can’t just ask up front with no context and expect users to say yes.” He pointed to Outback Steakhouse as a standout example: “They clearly explain the value, guide users through branded screens, and then request OS-level permissions. It’s thoughtful, and it works.” AI in Action: Real Business Impact Artificial intelligence was another hot topic. Brendan shared two areas where AI is delivering real value: smarter cross-sell/upsell and feedback intelligence. “Even simple segmentation by daypart or region can lift basket size. Some tools report a 10% increase just by turning it on,” he said. “It doesn’t need to be complex to be effective.” QSR Webinar Recap: Closing Thoughts Whether optimizing app experiences, trying to unify your tech stack, automating manual processes, or building stronger loyalty loops, the advice was clear: start small, stay focused, and partner with tools and teams that can scale with you. Watch the full webinar on demand to explore these examples further and learn how MoEngage, Radar, and Bottle Rocket can help your team accelerate QSR engagement.   The post Digital Meets Dine-In: 5 Expert QSR Engagement Strategies appeared first on MoEngage. #digital #meets #dinein #expert #qsr

John Edwards, Technology Journalist & AuthorJune 4, 20254 Min ReadRancz Andrei via Alamy Stock PhotoIt may be hard to believe, but some senior executives actually believe that AI's arrival isn't a ground-shaking event. These individuals tend to be convinced that while AI may be a useful tool in certain situations, it's not going to change business in any truly meaningful way. Call them skeptics or call them realists, but such individuals really do exist, and it's the enterprise's CIOs and other IT leaders who need to gently guide them into reality. AI adoption tends to fall into three mindsets: early adopters who recognize its benefits, skeptics who fear its risks, and a large middle group -- those who are curious, but uncertain, observes Dave McQuarrie, HP's chief commercial officer in an online interview. "The key to closing the AI adoption gap lies in engaging this middle group, equipping them with knowledge, and guiding them through practical implementation." Effective Approaches The most important move is simply getting started. Establish a group of advocates in your company to serve as your early AI adopters, McQuarrie says. "Pick two or three processes to completely automate rather than casting a wide net, and use these as case studies to learn from," he advises. "By beginning with a subset of users, leaders can develop a solid foundation as they roll out the tool more widely across their business." Related:Start small, gather data, and present your use case, demonstrating how AI can support you and your colleagues to do your jobs better and faster, recommends Nicola Cain, CEO and principal consultant at Handley Gill Limited, a UK-based legal, regulatory and compliance consultancy. "This could be by analyzing customer interactions to demonstrate how the introduction of a chatbot to give customers prompt answers to easily addressed questions ... or showing how vast volumes of network log data could be analyzed by AI to identify potentially malign incidents that warrant further investigation," she says in an email interview. Changing Mindsets Question the skeptical leader about their biggest business bottleneck, suggests Jeff Mains, CEO of business consulting firm Champion Leadership Group. "Whether it’s slow decision-making, inconsistent customer experiences, or operational inefficiencies, there's a strategic AI-driven solution for nearly every major business challenge," he explains in an online interview. "The key is showing leaders how AI directly solves their most pressing problems today." When dealing with a reluctant executive, start by identifying an AI use case, Cain says. "AI functionality already performs strongly in areas like forecasting, recognition, event detection, personalization, interaction support, recommendations, and goal-driven optimization," she states. "Good business areas to identify a potential use case could therefore be in finance, customer service, marketing, cyber security, or stock control." Related:Strengthening Your Case Executives respond to proof, not promises, Mains says. "Instead of leading with research reports, I’ve found that real, industry-specific case studies are far more impactful," he observes. "If a direct competitor has successfully integrated AI into sales, marketing, or operations, use that example, because it creates urgency." Instead of just citing AI-driven efficiency gains, Mains recommends framing AI as a way to free-up leadership to focus on high-level strategy rather than day-to-day operations. Instead of trying to pitch AI in broad terms, Mains advises aligning the technology to the company's stated goals. "If the company is struggling with customer retention, talk about how AI can improve personalization," he suggests. "If operational inefficiencies are a problem, highlight AI-driven automation." The moment AI is framed as a business enabler rather than a technology trend, the conversation shifts from resistance to curiosity. Related:When All Else Fails If leadership refuses to embrace AI, it’s important to document the cost of inaction, Mains says. "Keep track of inefficiencies, missed opportunities, and competitor advancements," he recommends. Sometimes, leadership only shifts when management’s view of the risks of staying stagnant outweigh the risks of change. "If a company refuses to innovate despite clear benefits, that’s a red flag for long-term growth." Final Thoughts For enterprises that have so far done little or nothing in the way of AI deployment, the technology may appear optional, McQuarrie observes. Yet soon, operating without AI will become as unthinkable as running a business without the internet. Enterprise leaders who delay AI adoption risk falling behind the competition. "The best approach is to embrace a mindset of humility and curiosity -- actively seek out knowledge, ask questions, and learn from peers who are already seeing AI’s impact," he says. "To stay competitive in this rapidly evolving landscape, leaders should start now." The best companies aren't just using AI to improve; they're using the technology to redefine how they do business, Mains says. Leaders who recognize AI as a business accelerator will be the ones leading their industries in the next decade. "Those who hesitate? They’ll be playing catch-up." he concludes. About the AuthorJohn EdwardsTechnology Journalist & AuthorJohn Edwards is a veteran business technology journalist. His work has appeared in The New York Times, The Washington Post, and numerous business and technology publications, including Computerworld, CFO Magazine, IBM Data Management Magazine, RFID Journal, and Electronic Design. He has also written columns for The Economist's Business Intelligence Unit and PricewaterhouseCoopers' Communications Direct. John has authored several books on business technology topics. His work began appearing online as early as 1983. Throughout the 1980s and 90s, he wrote daily news and feature articles for both the CompuServe and Prodigy online services. His "Behind the Screens" commentaries made him the world's first known professional blogger.See more from John EdwardsWebinarsMore WebinarsReportsMore ReportsNever Miss a Beat: Get a snapshot of the issues affecting the IT industry straight to your inbox.SIGN-UPYou May Also Like #how #convince #management #colleagues #that