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Although many people would think of phishing as a malicious email containing a suspicious link or attachment, hackers have now moved far beyond this to target trusted business tools including calendars and meeting invites.
We spoke to Soundharya Bharani Poomalai, Associate Threat Analyst, Barracuda, to find out more.
- Why are attackers increasingly turning to calendar invites and .ics files as phishing vehicles, and what has changed in the threat landscape to make this an attractive attack surface now?
Calendars have become part of daily business admin and are used far beyond meeting scheduling. People now get invites for things like policy acknowledgements, handbook reviews, benefits enrolment windows and compliance training reminders. A calendar invite referencing an HR update or a payroll action doesn't look out of place, and this allows attackers to blend in more easily than a suspicious email ever could.
There's also a structural advantage. Calendar entries get added automatically with little or no interaction from the recipient, and they tend to persist even if the original email is deleted or quarantined. Mobile adds another layer to this. A lot of calendar notifications get handled on phones, which often sit outside the reach of desktop-focused security tools.
- How significant is the visibility gap between what an email security system can inspect and what is rendered by a calendar application?
It is significant, and it’s something many organisations haven't accounted for. Traditional email security is built to scan the message body, subject line and attachments, whereas an .ics file often slips through as a calendar object and doesn’t receive the same level of inspection.
The problem is that .ics files carry much more than a date and time. They can include event descriptions, organiser details, locations, attachments, URLs and custom metadata fields, and any of these can be used to hide phishing content. Once the calendar app renders that content, the recipient sees corporate branding, instructions or a QR code that looks entirely legitimate. If the victim then enters their credentials and completes MFA, the attackers can intercept the username, password and session data, giving them full access to the account.
That mismatch between what security tools check and what the user sees is why these attacks succeed.
- Are calendar phishing attacks fundamentally a new technique, or are they an evolution of the same social-engineering and evasion techniques we've seen in email phishing – such as QR codes, impersonation and adversary-in-the-middle attacks?
They’re an evolution rather than an invention. The building blocks are familiar. QR codes hide a destination, brand impersonation builds trust, and adversary-in-the-middle platforms intercept credentials and session data in real time once someone signs in. None of this is new.
What's changed is the delivery container as they’re wrapped inside a calendar invite instead of an email. A QR code buried in an .ics file benefits from all the same advantages it has in a PDF or email body because it avoids text-based detection. It also gets the added benefit of sitting somewhere security tools are paying less attention.
- What does the rise of .ics phishing tell us about how organisations need to redefine what constitutes ‘malicious content’?
It tells us that malicious content is no longer confined to a bad link or attachment. Security teams have traditionally focused on email bodies and file attachments because that's historically where the risk sat. Calendar invites increasingly need to be factored into this as a phishing vehicle. The format of the content is not what determines the risk now, it’s what the content does when it reaches the recipient.
All of this means organisations need to start thinking about any workflow that can display or trigger content on a user's behalf.
What should organisations be inspecting inside a calendar file, and what are the technical challenges involved in doing that effectively at scale?Calendar files need the same level of scrutiny as a traditional attachment. In practice, this involves parsing the metadata fields, analysing any embedded links or attachments, inspecting HTML-rendered content, and decoding QR codes to check where they lead to.
The technical security challenge is that .ics files were built for interoperability. The format allows an event title, description or organiser field to carry rich content across Outlook, Google Calendar and Apple Calendar, and that flexibility is what makes it hard to inspect consistently at scale.
There's also the matter of what happens after delivery. A calendar entry can sit in someone's diary for days or weeks before a link becomes relevant, so inspection can't just happen once at the point of delivery. It needs to hold up over time.
If a malicious calendar invite gets through, what should the incident-response process look like? Is deleting or quarantining the original email enough?Deleting or quarantining the original email doesn't remove the calendar entry itself. Because invites are typically added automatically, the event can remain live in someone's calendar even after the source email is long gone, which means the malicious link or QR code is still sitting there and waiting to be clicked on.
A response will only be effective if it removes both the delivery message and the associated calendar entry from every affected mailbox. Teams also need to check identity activity around the time the invite landed and look for things like sign-ins from unfamiliar devices, unexpected MFA prompts, new session creation or OAuth consent activity. If someone did interact with the invite, credentials or session tokens may already be compromised, so containment can't stop at cleaning up the calendar.
(Image credit: Getty Images / Westend61)What are the three most practical things security and business teams can do today to reduce their exposure to calendar-based phishing without disrupting legitimate use of calendars?First, treat calendar invites as active content rather than passive scheduling data. Apply the same inspection standards to .ics files that already exist for attachments, including checking embedded links, attachments and any QR codes inside the event itself.
Second, strengthen identity security so a successful phishing attempt doesn't automatically become a successful breach. Phishing-resistant MFA such as FIDO2 or WebAuthn, conditional access policies, and the ability to monitor sessions and revoke them quickly all reduce the impact if someone does click through.
Third, update user awareness so people know calendar invites can be malicious too. Employees should be wary of QR codes inside calendar events and treat unexpected HR, payroll or policy notifications arriving as .ics files with the same suspicion as an unusual email.
Are we reaching a point where organisations need to stop thinking of email as the attack surface, and instead think about all the trusted applications and automated workflows that email can trigger?Yes, and calendar phishing is a good example of why. An email triggers a calendar entry, a calendar entry contains content, and that content leads somewhere else entirely, whether that's a fake sign-in page or a malicious QR code.
Every one of those steps happens in a different application, often with a different set of security controls, or none at all. Attackers are simply following that chain to find the weakest link, and the weak link tends to sit wherever inspection stops.
Organisations that only defend the inbox are defending one part of a much longer chain.
Across organizations, AI adoption is entering a new phase. What began as experimentation and isolated use cases is rapidly evolving into enterprise-wide deployment, with AI becoming embedded across operations, customer experiences, decision-making and business strategy.
This shift is creating significant opportunities for growth, productivity and innovation, while also presenting a growing challenge for business and technology leaders: ensuring governance, oversight and operating models keep pace. As organizations scale AI, the question is no longer just what the technology can do, but whether the structures, processes and controls are in place to manage it effectively.
The growing governance gapIn many organizations, AI adoption is expanding beyond the direct oversight of central technology teams. Business units are deploying AI-powered tools to improve efficiency, streamline workflows and accelerate decision-making.
While this democratization of technology can unlock innovation, it can also create complexity. Leaders may find themselves accountable for outcomes generated by systems distributed across multiple teams, platforms and environments. The pace of adoption only intensifies this challenge.
Organizations are under pressure to move quickly as competitors invest in AI capabilities and employees increasingly expect access to AI-powered tools. The urgency is reflected in the UK government's AI Opportunities Action Plan, which highlights IMF estimates that AI could boost UK productivity by up to 1.5 percentage points annually, potentially generating £47 billion in economic gains each year.
As a result, deployment often progresses faster than governance frameworks can evolve.
This creates a fundamental tension between speed and control. Businesses want to capture the benefits of AI quickly, but moving too fast without appropriate safeguards can introduce operational, security and compliance risks. The challenge is not simply deploying AI at scale, but ensuring it can be managed responsibly once deployed.
As investment accelerates, expectations riseAs AI becomes more deeply integrated into business operations, its influence extends beyond execution. AI is increasingly shaping how work gets done, how decisions are made and how organizations allocate resources. In some cases, it is helping leaders identify opportunities and risks that may not have been visible through traditional approaches.
This growing influence means AI is no longer just a technology initiative. It has become an organizational capability that touches every part of the business. Decisions about AI deployment are therefore also decisions about governance, accountability and risk management.
The scale of momentum behind AI is clear. Earlier this year, the UK government highlighted £14 billion in private-sector AI investment commitments and more than 13,000 planned jobs as part of its ambition to establish the UK as a global leader in artificial intelligence.
This reflects a broader shift in how organizations view AI: no longer as an experimental technology, but as a strategic capability expected to drive growth, productivity and competitive advantage.
As investment accelerates, so too does the pressure to deliver measurable outcomes. Yet many leaders are discovering that success depends on more than deploying new AI tools. Without clear accountability, visibility and governance, the benefits of AI can be undermined by operational complexity, fragmented decision-making and increased risk.
The organizations that realize the greatest value from AI are likely to be those that invest as heavily in governance and oversight as they do in the technology itself.
Security, data and trust at scaleSecurity remains a critical consideration. As organizations integrate AI into business-critical processes, they must address issues such as data protection, model integrity and regulatory compliance. A single failure can have consequences that extend beyond technical disruption, affecting customer trust, brand reputation and regulatory standing.
Public expectations for responsible AI are high, with 72% of the British public saying that laws and regulation would make them more comfortable with the use of AI, underlining the importance of strong governance and oversight.
The challenge is heightened by AI's dependence on large volumes of data drawn from multiple environments and applications. Without visibility into how data flows through these systems, organizations may struggle to assess risk or respond effectively when issues arise. Robust governance and transparency therefore become essential components of any AI strategy.
Alongside security concerns, organizations are also facing greater financial scrutiny. Unlike traditional technology projects, AI programs often evolve rapidly, with new models, services and use cases introduced continuously. This can make it difficult to maintain oversight of spending, performance and risk, particularly as AI becomes embedded across multiple business functions.
These pressures are driving a reassessment of operating models. Traditional approaches to governance were largely built around systems that changed predictably and remained relatively static once deployed. AI introduces a different dynamic: models evolve, outputs vary and operating environments can change rapidly.
Building adaptable AI governanceAs a result, organizations are increasingly recognizing the need to build adaptability into their AI strategies. Governance cannot be treated as a one-time exercise. It must become an ongoing capability supported by continuous monitoring, clear accountability and the ability to respond quickly to emerging risks and opportunities.
The organizations seeing the greatest success with AI typically view governance and innovation as complementary objectives rather than competing priorities. They focus not only on deployment, but also on visibility, control and resilience. By establishing clear frameworks from the outset, they create an environment where AI can scale responsibly and deliver sustainable business value.
Infrastructure strategy also plays a key role. Many organizations operate across multiple cloud environments and technology platforms, creating challenges around integration, portability and control. As AI workloads increase, flexibility becomes increasingly important.
Businesses need the ability to deploy, move and manage workloads efficiently without becoming constrained by fragmented architectures.
Scaling AI with confidenceUltimately, the challenge facing leaders is not whether AI should scale, but how it scales. As adoption accelerates, organizations must look beyond deployment and focus on creating the conditions for sustainable success.
That means investing in governance, strengthening visibility across increasingly complex environments, improving financial accountability and ensuring organizational structures evolve alongside technological capabilities.
AI has the potential to transform how organizations operate, compete and create value. However, realizing that potential requires more than implementing new tools. It requires building the frameworks that allow innovation and control to coexist.
As AI becomes more deeply embedded across the enterprise, the organizations that achieve the greatest success will be those that can balance agility with accountability, enabling them to innovate confidently while maintaining oversight, resilience and trust.
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Now is the time for nonprofits to embrace artificial intelligence (AI).
There is a distinct opportunity to significantly improve their operations using the latest developments in AI and automation.
We are seeing confidence grow in the third sector as organizations come to understand the potential of AI, but now we are entering a crucial phase as they look to implement it.
Given the complex technology involved, it may appear daunting to get the right strategy in place, but a disciplined approach to adoption that is focused on incremental gains - not radical transformation - will ensure a positive outcome.
This will mitigate significant disruption by avoiding large number of components being upgraded at the same time, as that increases the risk of something going wrong.
A pragmatic approach combined with thinking bigGetting to this point is perhaps easier said than done, as many nonprofits rely on purpose-built applications and manual processes. It is incumbent on vendors to articulate a pragmatic approach to integrating AI with these existing systems, focusing on the idea of incremental improvements to processes and ways of working.
In parallel, nonprofits must think big about the potential for positive change. Vendors must work with their customers to help them understand that transformation entails more than just a chatbot or another point solution being added to the technology stack.
Done right, AI will reshape how workflows are triggered, data is interpreted and insights are generated. It will become an intelligence layer acting autonomously to interrogate data and provide teams with cognitive support to make more effective and real-time decisions.
Therefore, if nonprofits are to maximize their AI investments, they must ask themselves big questions: how does AI change the ecosystem the organization operates in? And how can it be used to improve the nonprofit’s mission?
If nonprofit leaders can think ambitiously about these answers, it will empower teams to become true knowledge workers. AI will help them uncover information to inform actions or give workers more time to focus on problem-solving, as the technology is designed to automate repetitive tasks that traditionally consume cognitive bandwidth.
The right data strategy is crucialA key challenge for nonprofits is data governance, as the AI tools will only be effective if they have access to the right data. In a recent study we conducted called “Closing the Gap: Nonprofit Finance Software’s Crucial Role in Today’s Landscape”, 61% of finance professionals in US nonprofits admitted they still rely on generic spreadsheets for core financial management.
The combination of specialist applications and manual processes makes it difficult to connect information across different applications and workflows. Data must be extracted, transformed and loaded so that it can be analyzed. The extraction process is also challenging because the data is often not in the same format and must be loaded in a particular format for ease of use by the finance team.
To address the data challenge, nonprofits must decide how to make the right data accessible to the AI. The data must be set up in a way that can be interrogated in plain language, and it must be based on good data inputs. Aside from reliability, the data also requires a common analysis framework so that users can understand its origins and who is accountable for it. Then, the organizations must be able to analyze the data chain and how AI is interacting with the data to make decisions.
This requires a detailed appreciation of the semantics of data. For example, the term “project” means something different in the world of nonprofits compared a professional services company or a public sector organizations . The AI must be able to interpret the word correctly to avoid hallucinations.
Equally important is the ability to monitor, evaluate and adjust data as it goes through the data supply chain to avoid inaccuracies in areas such as when it moves from the record to report phase. Nonprofits must work closely with their technology suppliers to ensure everyone in the organization, whether at the leadership, program or project level, can monitor the data for compliance with expectations.
Incrementalism is better than quantum leapsThe concept of incrementalism, rather than the “Big Bang” or quantum leap approach to transformation is the best way to address these fundamental questions.
Most successful IT implementations follow a similar incremental path. For organizations embracing this approach and wanting to ensure operational integrity while transforming IT systems, it is important to start with an evaluation of where the organization is relative to the desired end state. The planning objective is to get to the end state through a process of continuous, incremental adoption of new functionality and applications.
Most importantly, this approach allows staff to build their confidence in collaborating with AI tools. Imagine a team operating in a remote location providing medical assistance and the head of the team realizes they need to order supplies. Using incremental innovation an organization could use a combination of AI and automation to alleviate the burden of replacing missing stock.
In the first phase, an AI tool could monitor existing stocks and prompt staff when it is running low. This will give employees the chance to train the AI tool when it is urgent to replenish stocks. The AI could also use contextual information such as historical usage data and information such as weather to identify likely peak demand. As the AI becomes more autonomous, it could make recommendations to a human co-worker suggesting it completes an order form for new supplies which is reviewed by an employee.
Again, this acts as a training opportunity for AI so that in time employees will have confidence it can autonomously complete such tasks in the background. In time, the whole process will minimize interruption for employees and reduce the burden on them to complete such administrative tasks.
What this example shows is that nonprofits do not have to achieve fully autonomous systems straight away. At each stage, the AI is providing workflow improvements that empower knowledge workers by saving them time to focus on what is important to deliver their missions.
Incremental change will see nonprofits delivering value to their organizations very quickly, if they are able to address fundamental questions around their approach to data, while also thinking big about what AI can enable them to do better.
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- HP and Huawei enter patent-sharing deal, with Huawei's Wi-Fi 7 patents central to the deal
- Huawei is still under the microscope by the US Department of Commerce
- The companies have had a previous run-in about Wi-Fi patents
Huawei has confirmed a multi-year global patent cross-licensing agreement with HP in relation to its Wi-Fi technology, ultimately granting the latter permission to use certain Huawei Wi-Fi patents. In return, Huawei will also receive rights to some of HP's patents.
Central to the agreement is Huawei's Wi-Fi portfolio, including Wi-Fi 7 technology, however neither company has actually disclosed the exact patents that are covered. Further details, like the financial terms or the deal's duration, are also under wraps.
The deal is of great geopolitical significance, with California-based HP striking up a deal with Shenzhen-based Huawei – a Chinese company that remains on the US Department of Commerce's Entity List.
HP and Huawei dealAlthough US restrictions have severely restricted Huawei's ability to buy American chips, software and other technology, and other international restrictions including in the UK have sought to remove Huawei hardware from critical national infrastructure, this new deal is proof that the company still has major influence outside of China.
Importantly, an HP spokesperson (via Reuters) stressed that this is merely a patent licensing agreement: "It is not new, and does not represent a broader strategic or commercial relationship, partnership, or collaboration with Huawei." The company noted that such licences are normal because manufacturers need access to patented technology for certain standards, like Wi-Fi.
"This milestone agreement not only reflects the companies’ cooperation in the field of intellectual property licensing but also recognizes Huawei's innovation capabilities and core technological strength as well as HP’s position as a global leader in computers and peripheral equipment," Huawei wrote.
Ultimately, being on the Entity List doesn't represent an outright ban for American companies to transact with Huawei, but it does place the company under US scrutiny. Neither does it mean that HP has been given special treatment to bypass US restrictions to open this deal.
A work in progressInterestingly, the deal didn't just come from nowhere. HP and Huawei have been involved in a dispute over Wi-Fi intellectual property for a while. Huawei accused HP's products of implementing its Wi-Fi 6 patents without a relevant licence in August 2025.
Standard-essential patent owners are generally expected to make their patents available under FRAND terms to grant manufacturers access to the technology, but cross-licence deals like this latest one are often seen as the most efficient and cost-effective way to open up the technology.
Rather than both companies paying full royalties to each other, HP gets Huawei rights and Huawei gets HP rights in return. It's unclear if one of the companies has also made a balancing payment.
In other circumstances, Huawei's current rates for consumer products are $0.50 per device for both Wi-Fi 6 and Wi-Fi 7.
For Huawei, it's just another opportunity to turn years of R&D into important revenue. "By the end of 2025, Huawei held a total of 165,000 active patents and had signed over 260 patent licensing and cross-license agreements with many of the world’s largest patent holders," the company shared in a 2026 update.
Artificial intelligence has transformed how software is built. Tasks that once took software developers days, if not weeks, to finalize can now be completed in hours with the assistance of generative AI tools.
The promise is compelling, offering faster innovation, increased productivity, and the ability to bring new applications to life at an unprecedented speed. But what is the impact on security?
AI coding has simultaneously put software risk on steroids. This is not because AI-generated code is uniquely flawed; it’s because it enables organizations to build and deploy software faster than any existing security, governance, or risk management process.
Development velocity has accelerated toward machine speed, while governance remains largely human-driven. That gap is now one of the defining software security challenges of the AI era.
Software is moving at machine speed. Security isn't.AI is not only changing how code is written; it is changing how software is assembled. Developers can now assemble applications using open-source components, APIs, and third-party services faster than ever before. Every new application, integration, and dependency expands the attack surface that organizations must inventory, monitor, and secure.
The result is now a growing imbalance between software creation and software remediation. As the pace of software creation increases, remediation must keep up.
Veracode's 2026 State of Software Security report found 82% of organizations now carry security debt—vulnerabilities that remain unresolved over time — and 60% carry critical security debt, meaning flaws that are severe enough to cause significant damage if exploited.
Third-party code continues to be an especially stubborn source of risk, representing 66% of the most dangerous, long-lived vulnerabilities. The data reveals a simple reality: AI doesn't just generate more first-party code—it is increasing software complexity.
Organizations have always dealt with flawed code. The difference now is the speed and scale at which that code can be created, accepted, and deployed. AI doesn't just introduce risk, it amplifies the challenge of managing risk by enabling teams to generate exponentially more software than traditional security processes were designed to govern.
Traditional security governance assumes humans remain the bottleneck in software creation. Reviews, approvals, audits, and remediation workflows were designed for development cycles measured in weeks or months. AI-assisted development compresses those timelines dramatically.
When software can be generated, modified, and deployed at machine speed, governance models that depend on human intervention alone are no longer sustainable.
AI can help plant a seed, but that does not mean the garden will thrive. A seed needs the right soil, climate, and care. Software is no different. Organizations can generate applications overnight, but without the right security frameworks, operational support, and governance structures, those applications can quickly become liabilities rather than assets.
This is why security leaders must rethink governance for the AI era. The goal can’t be to inspect every line of code or eliminate every vulnerability before deployment; that approach was already becoming unsustainable before generative AI entered the picture. Instead, organizations need governance systems capable of operating at the same pace as software creation.
That means automating risk analysis, continuously evaluating dependencies, enforcing policies through pipelines, and prioritizing remediation based on business risk rather than relying on manual review alone.
Governance becomes the new trust layerThe need for machine-speed governance extends beyond operational efficiency. As AI accelerates software creation, governance becomes the mechanism through which organizations maintain visibility, demonstrate control, and establish trust across an increasingly complex software ecosystem.
Ultimately, this isn't just about scaling security. It's about ensuring software can be trusted and held accountable, regardless of how it's built.
AI can generate software, but it cannot assume responsibility for it. Boards will still hold executive leadership accountable for cyber risk. Regulators will still expect organizations to demonstrate that the software they deploy is secure and resilient. Customers will still expect software they can trust, regardless of how it was built.
AI may change how software is created, but it does not change who is accountable for its consequences.
That shift requires organizations to rethink governance as a strategic capability, not a compliance exercise. Success will depend less on preventing every vulnerability and more on demonstrating that software can be continuously evaluated, understood, and trusted as it evolves. In the AI era, the winners will not simply be those that build software fastest, but those that can govern it most effectively.
AI can help plant the seed, but it cannot tend to the garden. The organizations that lead today will not necessarily be those that generate the most software. They'll be the ones that can confidently answer the question every stakeholder will eventually ask: Can we trust what we've built?
AI has accelerated software creation beyond anything the industry has experienced before. If software risk is now on steroids, governance must be too. Otherwise, the gap between what organizations can build and what they can securely manage will continue to widen.
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This summer's unexpected heatwave across the UK and Europe has caught retailers flat-footed.
Go into any London Oxford Street store this week and you’ll find the last remains of the mid-summer sale, while “new in” rails are covered in chocolate brown trouser suits ready for Autumn.
The trouble is, it’s still 28 degrees and sunny with another heatwave expected this week. Shoppers aren’t looking for jumpers, yet the shelves are stocked for a forecast made half a year earlier, meaning many retailers have missed the immediate shift in consumer demand.
It’s hard to not feel the disconnect. Somewhere back in January, a planning team sat in a meeting room and decided, with the best information they had at the time, that by early August the nation would be ready to shop for knitwear.
It’s a process that retailers have used for decades, and the problem isn’t that they planned-ahead or got it wrong, it’s that the infrastructure behind the decision has no mechanism for course correcting once new information arrives.
That's not a forecasting failure. It's a technology and process failure, and it's one that's becoming impossible to ignore.
Six-month planning cycles are no longer viableThe heatwave is just one example of why rigid six-month planning cycles in retail are no longer commercially viable. Designed in an era that was steady and predictable, they assume stable supply chains, formulaic seasons, and shoppers who wait patiently for the "right" moment to buy. That world simply doesn’t exist today.
Global supply chains have become fragile and prone to disruption at any point in the chain, while erratic weather patterns can change trading conditions overnight, and consumer demand is shaped as much by a TikTok trend that lands on a Tuesday, as it is by a season on a calendar. This has left retailers trying to run a business that demands agility on an operating system designed for a much slower rhythm.
The result is the disconnect we're seeing on the shop floor right now. Having worked in retail for more than 20 years, I know first-hand that most planning systems are still built around static reports, disconnected spreadsheets and manual range-building processes that take weeks (sometimes months) to turn around.
This creates a structural lag between changes in demand and when the business is able to respond. It is this lag that is quickly becoming the single biggest driver of markdowns, stockouts and wasted inventory that costs the global retail sector more than $1.7 trillion per year, according to analysts IHL Group.
Rethinking planningIt’s clear to me that for retailers looking to achieve growth, they must rethink how planning, buying and merchandising get done.
This starts with moving away from rigid, twice-a-year buying cycles built on legacy systems to data infrastructure that supports micro-season planning. This is shorter, more frequent windows where decisions about what to buy, when to reorder and how to promote in-store are made continuously and based on current data, not locked in months in advance based on a forecast made months earlier.
Practically, this means implementing a few core capabilities, the first being real-time data visibility that allows teams to see live sell-through, stock position and intake at SKU level, rather than via a report that lands the following Monday describing what already happened.
The second is more connected forecasting, where demand models can analyze external factors such as a heatwave, local events or social media trends as a trading signal rather than an anomaly discovered on the shop floor.
This leads to the third capability, which is faster execution once a shift in demand is identified. The system needs to support quick decision making and action, whether that's an automated reorder, a reallocation of stock between stores and channels, or a change to in-store and online merchandising.
Maintaining a live modelNone of this replaces long-range strategy. Retailers still need a financial plan, a range architecture and a clear vision. What changes is how that happens in practice. Planning becomes less about producing a fixed document twice a year and more about maintaining a live model of the business that can be interrogated and acted on continuously.
That's a fundamentally different technology requirement than most legacy planning and merchandising systems were built to support, and it's why so many retailers are still reacting to demand shifts weeks after they've already cost them sales.
For retail leaders, the practical takeaway is to start auditing where your planning and merchandising decisions get made, and how long it takes for a real-world signal, such as a heatwave, a stockout, a viral product, to translate into a change on the shop floor or the website. If that gap is measured in weeks rather than days, the constraint isn't your team's judgement, it's the infrastructure they're working with.
This heatwave is just one visible example of a much broader technological and operational shift that retailers need to make. The businesses that treat it as a prompt to modernize, rather than a one-off weather event, are the ones that will be more commercially resilient the next time conditions change without warning.
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It's not a problem that Taylor Sheridan has often had to contend with, but some fans are worried that Lioness season 3 is about to enter a mid-season slump.
At the halfway point of the political drama, there's a lot going on, with few answers in sight. This season's drama revolves around two simultaneous timelines: Joe being kidnapped by unknown Russian operatives, and, the six months leading up to her capture. Do we know who is responsible? Not exactly.
But still, let's press on in the face of potential adversity — when does Lioness season 3 episode 5 arrive on Paramount+?
What time can I watch Lioness season 3 episode 5 on Paramount+?Lioness season 3 episode 5 will drop on one of the world's best streaming services in the US and Canada on Sunday, August 30 at 12am PT / 3am ET.
Here's when it will be released in other nations globally:
- US – 12am PT / 3am ET
- Canada – 12am PT / 3am ET
- UK – 8am BST
- India – 12:30pm IST
- Singapore – 3pm SGT
- Australia – 5pm AEST
- New Zealand – 7pm NZST
Lioness season 3 will have a total of eight episodes, with new entries airing weekly. That gives us the following schedule:
- Episode 1: out now
- Episode 2: out now
- Episode 3: out now
- Episode 4: out now
- Episode 5: August 30
- Episode 6: September 6
- Episode 7: September 13
- Episode 8: September 20
- OM System has shared a YouTube teaser for upcoming camera launch
- The viewfinder experience is the teaser's key focus, and the camera's form is revealed
- The launch is set for September 9
Since Japan Industrial Partners acquired Olympus in 2021, creating OM Digital Solutions and rebranding new cameras as OM System, we've seen upgrades for every Olympus camera series except one: the PEN-F / PEN E-P7 line of digital rangefinders.
The Olympus PEN-F is a camera that's much admired for its stunning retro looks, described by some as the 'best looking camera ever made'. Olympus fans have been calling for a successor to the series for years — and they might finally be about to get their wish.
A YouTube video from OM System (below) just teased what could be the long-awaited successor to the PEN-F, and it's coming on September 9.
The teaser focuses on the experience of using the built-in 'rangefinder'-style viewfinder — a hallmark of PEN cameras — and then reveals the camera's form factor, with some detailing of the top plate.
Based on these low-key visuals, we could be looking at the spiritual successor to the beloved Olympus PEN-F — which inspired the design of the stunning OM-3 — or perhaps a re-imagining of the low-cost Olympus PEN EP-7.
Either way, the rangefinder-style camera is getting Olympus fans excited, if the comments section of the YouTube video are anything to go by. "Let's go!" chime several viewers.
Given the length of time between the original Olympus models — the Pen-F is 10 years old, and the Pen EP-7 five — there are a few upgrades that could find their way into this new camera, including a speedier stacked sensor, improved phase-detection autofocus, and additional computational photography modes.
All will be revealed on September 9, and you can follow the live event on the OM System site, which is advertising a five-day program of live discussions, all seemingly centered on the new camera.
I played an hour of the new Dragon's Dogma 2: Dark Arisen expansion and walked away impressed by its gameplay loop and improved performance (on PlayStation 5), and instantly saw what Capcom is aiming for.
In Dragon's Dogma 2: Dark Arisen, players must journey through Norgan, an abandoned region in the north, and join forces with a mysterious new character to uncover the secrets of an undying Fallen Dragon.
In that process, you must find and appraise gear, skills, and weapons known as 'spoils' or 'relics' obtained through exploration and by defeating new, formidable foes to stand a chance in the unforgiving region.
If this sounds a lot like the mechanics of Dragon's Dogma: Dark Arisen, you're on to something because the appraisal system is effectively identical to Dragon's Dogma: Dark Arisen's purification of cursed items, which provided some of the most powerful gear available in the game.
That same concept is applied in Dragon's Dogma 2: Dark Arisen's Norgan region, but with a bigger explorable map instead of a dungeon-only layout and presumably even more spoils to be found.
Better than its predecessor?It's only natural to compare Capcom's two Dragon's Dogma expansions, considering the similarities. However, after my time with its latest offering, it's hard (and frankly too soon) to conclude which holds the best experience.
Fortunately, some of the pain points from the base game experience in Dragon's Dogma 2 have been addressed, as the major patch (arriving at the end of August) was included in the hour preview I played.
Finally, there's enough time to explore and loot areas without being bombarded by enemies every minute — enemy encounters are still frequent, but during my journey to the first major boss, it felt good not to have hordes of goblins constantly spoiling the fun.
Performance is also significantly improved on the PS5. Bear in mind, I didn't have access to any frame rate overlay and couldn't visit the base game's regions (and I originally played the base game on PC), but it definitely felt like a constant 60fps, even in the most intense battles.
I didn't have plenty of time to explore many of Norgan's points of interest, but from acquiring a pet dire wolf, stumbling upon a frost-breathing giant, and finding level 3 skill spoils for the new Trickster and Magick Archer vocations, it's clear that Dragon's Dogma 2: Dark Arisen is the bigger expansion.
For example, Magick Archer now has an enhanced version of Arctic Bolt that becomes available by appraising one of the skill spoils, known as Glacial Bolt. This works wonders against bosses, with increased damage and the ability to freeze them in place for pawns to rally and attack.
Harpies are also heavily present in Norgan, but with new designs and attack patterns, notably a more aggressive attempt to grab the player or their pawns. Some of the enemies I spotted in the preview were essentially reskins of those from the base game, but like the harpies, new maneuvers and attacks set them apart.
Fighting the Fallen Dragon was a surprise too, an epic main boss encounter that I wish didn't end so soon — especially with one surprising moment I didn't see coming (which I won't spoil), but left me wanting more and curious to see just how many formidable foes await the Arisen in the full experience.
It's worth noting that there are also twelve new dungeon challenges to be found across the base game in addition to Norgan's content. Again, I didn't get to see them, but if the caves and smaller explorable areas I found in Norgan are anything to go by, with lootable chests and some new foes to fight, there'll be a lot to look forward to in those dungeons.
While I did have fun exploring Norgan, I'm cautiously optimistic about what the full experience has to offer. Bitterblack Isle's dungeon may have offered little in terms of exploration in a smaller map, but the sole focus on combat allowed vocations (or, in other words, classes) to work best in closed spaces in the first Dark Arisen, particularly Magick Archer and its Ricochet Hunter skill.
My main concern is that neither Norgan nor the additional twelve dungeon challenges will double down on those indoor and closed-space environments from Dragon's Dogma: Dark Arisen, and the new enemy and boss types may not be diverse enough to keep combat encounters entertaining.
Nonetheless, I'm very much looking forward to getting my hands on the full expansion, especially since Capcom is seemingly fixated on delivering a vastly improved version of Dragon's Dogma 2 with the highly anticipated pre-expansion patch.
We won't have to wait long, as the expansion is set for launch on October 9, 2026, on PS5, Xbox Series X, Xbox Series S, Nintendo Switch 2, and PC.