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National Post
3 days ago
- Business
- National Post
Latest OAG Report: High-Quality Data and AI Transformation Are Critical to Building Resilience Across Airline Operations
Article content LONDON — OAG, the global leader in aviation data and analytics, is proud to release its latest industry report in collaboration with Microsoft: ' Can AI and the Right Data Rewrite the Rules of Airline Performance? '. Article content Article content From minimizing delays and turnaround bottlenecks to forecasting maintenance needs and enhancing stakeholder decision-making, AI is already delivering tangible impact—but only when powered by accurate, complete, and well-structured data. Article content The report explores how trusted data is enabling AI to address critical operational challenges in aviation today. From minimizing delays and turnaround bottlenecks to forecasting maintenance needs and enhancing stakeholder decision-making, AI is already delivering tangible impact—but only when powered by accurate, complete, and well-structured data. Article content Drawing on real-world case studies, robust research, and expert insight, the report illustrates how data readiness and AI transformation are ushering in a new era of operational resilience across the airline industry. Article content A key highlight of the report is a comprehensive visual mapping of nine of the airline industry's most persistent operational challenges, structured along the end-to-end operational journey from pre-flight to post-flight. For each, the report showcases one real-world AI use case already delivering tangible results, offering airline leaders and innovators a clear path from problem to solution. Article content Filip Filipov, OAG's Chief Operating Officer explained 'AI is already transforming airline operations but to truly scale its impact, the industry must prioritize data readiness. Scalable transformation is only possible with intelligent, high-quality data at the core of every AI solution.' Article content Article content Article content Article content Article content
News.com.au
3 days ago
- Business
- News.com.au
'The Beautiful Game' falls for AI's charms
Sport has been unable to resist the surge of artificial intelligence and the biggest one of them all, football, is benefitting from data that AI can supply and the human eye cannot. Warsaw-based Vision, which says it is unique in gathering data by using AI, has two immediate goals -- women's football and re-igniting Generation Z's interest in watching sports, their co-founder Pawel Osterreicher told AFP. The company -- which numbers the South American football body CONMEBOL and their Central American counterparts CONCACAF among their clients -- are able to capture data from matches from just a single camera angle. This makes gathering data much cheaper, as players do not need to wear any technology, and there is no need for multiple cameras to capture the data, thanks to AI. Vision's programme -- which was used at last year's Copa America -- was recently awarded FIFA certification. Osterreicher says AI can provide data on aspects of football that humans cannot, such as acceleration, passing lanes, heat maps and zones of control. He said the data can help the 'Goliaths' as well as the 'Davids', just as it did by assisting in second-tier side Wisla Krakow's giantkilling exploits on their way to lifting the Polish Cup in 2024. However, despite this run of success the 36-year-old says he and his colleagues are not aiming for the men's World Cup or this year's men's World Club Cup. Instead they are targeting covering the inaugural women's World Club Cup in 2028, which fits in nicely with another of their aims, to halt the haemorrhage of Generation Z -- people born from 1997 to 2012 -- watching sports. "What we see right now in the sports market in general is that women's sport grows at a much faster pace," he said. "Of course, from a lower base, but a much faster pace than men's sport. "You can argue that men's is saturated. But one of the best investment opportunities and development opportunities in sports are currently women's franchises, women's sport and all the media around it." - 'More with less' - Osterreicher says this could be a way to reboot the interest of younger viewers "who are flocking away". The young "expect to get excited immediately... I have five seconds and if not, I'm swiping away. "So women's sport is also potentially an opportunity for sport to attract younger audiences because maybe it's just too boring just to watch all the same setups, all the same guys," he said. "So lots of investment is being directed in women's sports and from our perspective as well. "We're agnostic. Human is a human. We capture data on humans, not on particular genders. "But definitely, more and more customers are asking us to just cover women's leagues." Osterreicher -- who along with his colleagues set up the company five years ago -- says he is a "realist", adding not everyone should use the technology as it is a "complex thing, it requires certain resources." Nevertheless Wisla's cup victory showed that you "can do more with less". "You can have a smaller team wisely using technology and then beating the big guys," he said. He added, though, that it is not a "silver bullet" as human frailties can come into play. "A player might have had a row with his wife and be off his game," he said. While this technology is already tried and tested, Osterreicher and his team are months away from dealing another card to try and claw back the young audience, whose loyalty has switched to TikTok, Netflix and other platforms. "The way for sports to address it is to create content which is much more to their liking," he said. "So you can recreate a game in 3D, which is what we are planning to do. "So imagine a legendary goal being scored, or any goal being scored, and you switch to a replay from player perspective. "So we are potentially entering the world where sport needs to reinvent itself a little bit, change the way it's being served, in order to not lose those people to TikToks and the video games and mobile games of the world." pi/gj
Forbes
7 days ago
- Business
- Forbes
The Future Is Explainability – Why AI Must Earn Our Trust
As enterprises shift from AI experimentation to scaled implementation, one principle will separate hype from impact: explainability. This evolution requires implementing 'responsible AI' frameworks that effectively manage deployment while minimizing associated risks. The responsible AI approach, termed in the industry as 'explainability,' creates a balanced methodology that is ethical, pragmatic, and deliberate when integrating AI technologies into core business functions. Responsible AI shifts past generative AI's buzz (LLMs, voice/image generators) by harmonizing AI applications with corporate objectives, values, and risk tolerance. This approach typically features purpose-built systems with clearly defined outcomes. Forward-thinking companies making sustained investments prioritize automating routine tasks to decrease human dependency while enabling AI to manage repetitive processes. However, they maintain a balance where humans remain informed of system changes and actively oversee them. And in my view, this is the key to maturing AI. Explainability helps business leaders overseeing data analytics better interpret decisioning as concerns have become essential as businesses pursue AI's promised cost savings and increased automation. Explainability helps demystify AI decision-making. Business leaders overseeing analytics need visibility into why an AI system makes certain recommendations. This transparency is key as organizations scale their AI deployments and seek to build internal trust. According to McKinsey & Company, explainability increases user engagement and confidence, which are vital ingredients for successful, enterprise-wide adoption. As businesses embrace automation to drive efficiency and cost savings, interpretability becomes essential for governance, compliance, and decision support. Explainability agents are a new class of AI models designed to interpret and communicate the reasoning behind complex AI decisions, particularly in black-box systems such as deep neural networks. These agentic AI assistants are autonomous, goal-driven, and capable of adapting to changing conditions in real-time. Take, for example, a manufacturer managing MRO (maintenance, repair, and operations) inventory. An explainability agent can continuously reassess stocking levels by analyzing supply, demand, asset usage, and work orders. It can then suggest dynamic adjustments and explain the rationale behind each one. This improves efficiency and empowers supply chain leaders to make informed, confident decisions. As enterprises grow more sophisticated in their AI adoption, they recognize the limits of generic, pre-trained models. Instead, they're embracing purpose-built AI that: The goal is to improve timelines, cut costs, and increase productivity, responsibly and at scale. Responsible AI also involves rigorous risk management. A recent National Institute of Standards & Technology (NIST) report highlights how AI systems trained on evolving data can behave unpredictably, creating legal, reputational, or operational vulnerabilities. Responsible AI means designing systems that are explainable, testable, and aligned with human oversight, not just accurate. For example, responsible AI systems can segment sensitive data to prevent it from being processed by third-party large language models (LLMs). In another case, a supply chain AI platform might explain every recommendation with data-backed context, allowing users to see what the AI suggests and why it matters. This transparency builds user trust, facilitates informed decision-making, and accelerates execution by ensuring stakeholders align with AI-driven strategies. Ultimately, it empowers organizations to unlock AI's full potential, without losing control. AI doesn't need to be mysterious. With explainability agents and purpose-built systems, businesses can harness the power of AI in a transparent, ethical, and results-driven way. Enterprise users shouldn't just use AI—they should be able to understand and trust it. In the next phase of AI adoption, companies that prioritize responsible, agentic AI will reap long-term value while remaining resilient, agile, and accountable.
TechCrunch
29-05-2025
- Automotive
- TechCrunch
From Insights to Action: Advancing Agentic AI
The frontier of AI is rapidly advancing, but among the public and even in the enterprise, an understanding of its capabilities hasn't always kept pace. The widely held view of AI often gets stuck on the image of a sophisticated chatbot, capable of engaging in a conversation with a user and providing a response to prompts. But this way of thinking is also a limitation, boxing the technology into something as simple as trading messages and images. It misses both the nuance and full potential of state-of-the-art, real-world applications. As businesses, societies, and technical practitioners alike seek to unlock the value of AI, tapping an expanded set of capabilities has become a top priority. Capital One has delivered a recent breakthrough by building a new multi-agentic conversational AI assistant for car buyers. Capital One has a long history of using data, technology, and analytics to deliver superior financial services products and services for millions of customers. For over a decade, the business has been on a technology transformation journey to rebuild its tech stack, scale its technology workforce, and extend machine learning across the business. This dedication to innovation has positioned the company at the forefront of enterprises creating industry-leading AI advances today. 'We are continually exploring ways to enhance the customer experience at the frontier of AI. As we dug into new ways to improve the shopping experience with AI, we were looking at how to provide natural and satisfying interactions based on the way humans interact and reason,' says Dr. Milind Naphade, SVP of Technology, AI Foundations at Capital One. 'We wanted to transform the customer experience by replacing the previous generation of conversational AI technology with an agentic approach that leverages large language models (LLMs). We knew we needed to build a solution that would be able to really interact with a customer, understand their needs, and take actions on their behalf while they searched for a new vehicle.' The result: Chat Concierge from Capital One. The proprietary multi-agentic conversational AI assistant is custom-built to enhance the experience for car buyers and dealers alike. But answering questions and organizing information is only one part of what Chat Concierge can do. Model advances have enabled the dawn of AI agents that are trained to work together and tackle a series of complex tasks. Each AI agent performs a specific duty based on the user's request. Breaking a given workflow into discrete tasks and assigning each task to an AI agent can help ease the cognitive load of the user and create a more streamlined, satisfying experience. It's almost like building a dream team where each member is assigned to a role fitting their strengths. With Chat Concierge, multiple AI agents work together to not only provide information to the customer, but to take specific actions based on the customer's preferences and needs. For example, one agent communicates with the customer. Another creates an action plan based on business rules and the tools it is allowed to use. A third agent evaluates the accuracy of the first two, and a fourth agent that explains and validates the action plan with the user. In a single conversation, Chat Concierge can present information like vehicle comparisons and specifications, then take the next step by scheduling appointments and test drives with a sales team. 'There is a complex workflow that is getting executed behind the scenes, but it's all happening behind the scenes,' Naphade explains. These advances come as a logical progression from generative AI to AI agents that understand their environment, make decisions, and take actions. This requires an underlying infrastructure where the data and application programming interfaces (APIs) are AI-ready. 'We are standing on the shoulders of all the giant systems Capital One has built so far,' Naphade says. 'For example, we are one of the only banks that has fully committed to a public cloud. The data-driven, machine learning heritage of Capital One precedes us.' The possibilities for agentic AI–and future advances in the field–continue to evolve at a rapid clip. State of the art reasoning models are now designed to handle complex tasks by thinking through multiple steps and reasoning logically. Using these models to create AI agents brings the potential to help people turn insights into action for a range of sophisticated tasks that were never possible before. For instance, they have the potential to help solve real-world challenges like working together to tackle complex, PhD-level research problems; work with a company's developers to autonomously support the entire software development lifecycle, from planning to deployment; or even help a new business create a business plan and financial models along with a logo, website, and marketing plans. While the pace of AI innovation excels, so does the need for thoughtful approaches that balance speed with risk management. Capital One has tested, learned, and adapted its multi-agentic conversational AI workflow to create a great customer experience, in real-time, while also mitigating hallucination and errors through strong guardrails. In continuing to advance the state of the art in AI, Capital One isn't just inventing new tools and technology. It's delivering the right help at the right time—with intelligent, dynamically adaptive approaches—for more than 100 million customers. Learn more about AI at Capital One here.
CNA
20-05-2025
- Business
- CNA
Thoma Bravo's Nearmap to buy insurance tech firm itel for over $1.3 billion
Thoma Bravo-backed Nearmap has agreed to buy insurance technology provider itel from private equity firm GTCR, the companies said on Tuesday, as it looks to expand its offerings across property portfolios. The deal values itel at over $1.3 billion, including debt, a source familiar with the matter told Reuters. GTCR declined a comment on the deal value. The deal highlights the revived deployment of dry powder by buyout firms as the industry's recovery from high interest rates was disrupted by tariff-driven turbulence. The exchange of private assets in the secondaries market has also come to the forefront, with the freeze in IPO market, the traditional liquidity source for private equity, forcing many managers to sell their holdings at a discount. Founded in 1993, itel uses its proprietary database and technology to reduce costs for insurance companies in the property and casualty segment. The company also assists policyholders with damage assessments through its mobile platform. Jacksonville, Florida-based itel provides its services to all of the top 100 insurance carriers in North America. GTCR, which manages over $45 billion in capital, bought itel in August 2021 from PNC Riverarch Capital. The firm, in a release on Tuesday, said that the insurance tech firm had doubled its revenue over the past three years. "GTCR has been a great partner to us as we have built itel into a leading data and analytics company in the property claims ecosystem," said itel CEO Brian Matthews in a statement. The sale to Thoma Bravo comes just over a month after GTCR achieved a rare, significant return by selling its 55 per cent stake in payments processor Worldpay to Global Payments in a $24.25 billion three-way deal. In an environment where private equity firms have been compelled to hold onto their investments for longer periods, Thoma Bravo has also been active, liquidating its holding in exchange operator Nasdaq in two separate block trades earlier in the month. Thoma Bravo had acquired Australia-based insurance technology firm Nearmap in December, 2022.
