Image: Bill Gates/YouTube Fifty years ago, Bill Gates and his childhood friend Paul Allen founded a company called “Micro-Soft” in a strip mall in Albuquerque, New Mexico. Half a century later, the company has cemented its place among tech giants and ranks as the world’s second-largest company. Currently, the only company with a higher market cap is Apple, maker of the ubiquitous iPhone. While Microsoft is reflecting on its past successes in honor of its 50-year anniversary this April, including Gates sharing the company’s original source code, the tech giant is also hustling to secure its place among the leaders of the artificial intelligence revolution. After largely missing the boat on smartphones and the shift to mobile devices, Microsoft is hoping to avoid a repeat of this mistake and instead dominate the fields of cloud computing and generative AI. Microsoft still dominates office software and operating systems Microsoft’s Windows operating system — originally called MS-DOS — runs the majority of the world’s computers. The company also made a name for itself by dominating the office software market, and it still reigns supreme today. Once available on floppy disks and then CDs, the software can now be downloaded on any device thanks to the power of cloud computing. Even though its main competitor Google Docs is free for many to use, Microsoft Office products remain the standard for most offices around the world. Attempts to diversify weren’t always home runs The company has made moves to diversify beyond office software and operating systems throughout the years. For example, Microsoft launched Xbox consoles in 2001, introduced the Bing search engine in 2009, and acquired the LinkedIn social media website in 2016. Despite these moves, Microsoft’s products and services often lag behind its competitors. PlayStations outnumber Xboxes almost two to one, Google Search continues to dominate Bing, and other social media websites like Facebook, Instagram, and YouTube outperform LinkedIn. Must-read developer coverage Microsoft now bids on AI and cloud computing Microsoft is now working hard to future-proof its tech legacy through cloud computing and artificial intelligence. Its cloud platform Microsoft Azure is currently the second-largest by market share, though Amazon Web Services (AWS) still leads by a wide margin, and Google Cloud is also gaining in popularity. The company has also made significant moves to strengthen its AI offerings. It invested its first $1 billion in OpenAI back in 2019, and over the years has invested a total of $14 billion. Microsoft has also developed its own in-house AI tools, such as Microsoft 365 Copilot. However, the tech giant lacks its own proprietary silicon chips and relies on other companies to produce these essential AI model components. It remains to be seen whether Microsoft’s other AI investments will be enough to uphold its legacy for another 50 years — or if it will fall behind other AI companies. source

The promised land of AI transformation poses a dilemma for security teams as the new technology brings both opportunities and yet more threat. Threat actors are already using AI to write malware, to find vulnerabilities, and to breach defences faster than ever. At the same time, machine learning is playing an ever-more important role in helping enterprises combat hackers and similar. According to Palo Alto Networks, its systems are detecting 11.3bn alerts every day, including 2.3m new and unique attacks.[1] It is beyond human capabilities to monitor and respond to these attacks; it is also putting immense stress on security teams. How, then, can CISOs and CSOs build resilient security teams that can defend their organisations, and continue to innovate? Arms race Cybersecurity teams are in an “arms race” with attackers, as threat groups use AI to increase both the volume and speed of attacks. “AI has created a powerful toolkit for threat actors, and it has changed the way that we’re seeing attacks,” warns Nick Calver, VP for Financial Services at Palo Alto Networks. “Two or three years ago a ransomware attack would typically take 44 days before they could extract data or cause your systems a problem. Now we’re seeing that exact same attack happening in a number of hours,” he says. This acceleration is happening even as businesses struggle with visibility of how AI is being used in their own organisations, and as regulators struggle to keep up with a fast-changing landscape. “Everybody needs to be aware of AI,” says Calver. “Threat-based assessment is incredibly powerful, and I’ve seen it put to good use. It’s immediately helped improve organisations’ protection,” says Calver. Threat assessment is just one area where AI can also play a positive role in security. AI has been in use in cyber defence for over 10 years. “When you consider those attack volumes, it is not possible for humans to actually keep up and respond effectively,” says Calver. “Security technicians need to harness the power of AI.” Resilience, and human factors However, there is also a different side to an increasingly hostile security environment. Increasing threats are challenging organisations’ abilities to recover from attacks. This is changing how security leaders think. Focus remains on preventing a breach, but increasing attention is being given to how to respond and recover from attacks. Regulations are helping ensure consistency in this area with DORA being just one example. “Historically, we’d try to build a moat around the technology, and just stop anybody crossing in. But people do come in,” says Calver. “How do we actually segment and protect systems and provide a level of resilience?” Architectures such as zero trust will also play a role in building resilience, he says. But it is people who will ultimately secure an organisation. Even with automation and AI tools, businesses will only survive cyber attacks if their security teams can function under pressure. This means bringing together technical tools, training, testing and above all support for those in the front line. “Without people, we are nothing,” warns Calver. “Ultimately, the team, the people, that’s what actually makes an organisation successful, and that’s what protects the organisation too.” Watch the full interview below. CIO’s interview with Nick Calver of Palo Alto Networks For more information, please visit Palo Alto Networks’ Precision AI page. [1] Foundry Interview with PAN’s Nick Calver source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI announced plans to release its first “open-weight” language model since 2019, marking a dramatic strategic shift for the company that built its business on proprietary AI systems. Sam Altman, OpenAI’s chief executive, revealed the news in a post on X on Monday. “We are excited to release a powerful new open-weight language model with reasoning in the coming months,” Altman wrote. The model would allow developers to run it on their own hardware, departing from OpenAI’s cloud-based subscription approach that has driven its revenue. “We’ve been thinking about this for a long time but other priorities took precedence. Now it feels important to do,” Altman added. The announcement coincided with OpenAI securing $40 billion in new funding at a $300 billion valuation — the largest fundraise in the company’s history. These major developments follow Altman’s admission during a February Reddit Q&A that OpenAI had been “on the wrong side of history” regarding open-source AI — a statement prompted by January’s release of DeepSeek R1, an open-source model from China that reportedly matches OpenAI’s performance at just 5-10% of the operating cost. TL;DR: we are excited to release a powerful new open-weight language model with reasoning in the coming months, and we want to talk to devs about how to make it maximally useful: https://t.co/XKB4XxjREV we are excited to make this a very, very good model! __ we are planning to… — Sam Altman (@sama) March 31, 2025 OpenAI faces mounting economic pressure in a marketplace increasingly dominated by efficient open-source alternatives. The company reportedly spends $7-8 billion annually on operations, according to AI scholar Kai-Fu Lee, who recently questioned OpenAI’s sustainability against competitors with fundamentally different cost structures. “You’re spending $7 billion or $8 billion a year, making a massive loss, and here you have a competitor coming in with an open-source model that’s for free,” Lee said in a Bloomberg Television interview last week, comparing OpenAI’s finances with DeepSeek AI. Meta’s Llama models have established formidable market presence since their 2023 debut, surpassing one billion downloads as of this March. This widespread adoption demonstrates how quickly the field has shifted toward open models that can be deployed without the recurring costs of API-based services. Clement Delangue, CEO of Hugging Face, celebrated the announcement, writing: “Amazing news for the field and the world. Everyone benefits from open-source AI!” Amazing news for the field and the world. Everyone benefits from open-source AI! @elonmusk where’s open groq? https://t.co/ATThJQKIUH — clem ? (@ClementDelangue) March 31, 2025 The billion-dollar gamble: Why OpenAI is risking its primary revenue stream OpenAI’s move represents a high-stakes bet that could either secure its future relevance or accelerate its financial challenges. By releasing an open model, the company implicitly acknowledges that foundation models are becoming commoditized — an extraordinary concession from a company that has raised billions on the premise that its proprietary technology would remain superior and exclusive. The economics of AI have shifted dramatically since OpenAI’s founding. Training costs have fallen precipitously as hardware efficiency improves and algorithmic innovations like DeepSeek’s approach demonstrate that state-of-the-art performance no longer requires Google-scale infrastructure investments. For OpenAI, this creates an existential dilemma: maintain course with increasingly expensive proprietary models or adapt to a market that increasingly views base models as utilities rather than premium products. Their choice to release an open model suggests they’ve concluded that relevance and ecosystem influence may ultimately prove more valuable than short-term subscription revenue. This decision also reflects the company’s growing realization that competitive moats in AI may not lie in the base models themselves, but in the specialized fine-tuning, domain expertise, and application development that build upon them. Balancing openness with responsibility: How OpenAI plans to control what it can’t contain OpenAI emphasizes that safety remains central to its approach despite embracing greater openness. “Before release, we will evaluate this model according to our preparedness framework, like we would for any other model. And we will do extra work given that we know this model will be modified post-release,” Altman wrote. This represents the fundamental tension in open-weight releases: once published, these models can be modified, fine-tuned, and deployed in ways the original creators never intended. OpenAI’s challenge lies in creating guardrails that maintain reasonable safety without undermining the very openness they’ve promised. The company plans to host developer events to gather feedback and showcase early prototypes, beginning in San Francisco in the coming weeks before expanding to Europe and Asia-Pacific regions. These sessions may provide insight into how OpenAI plans to balance openness with responsibility. Enterprise impact: What CIOs and technical decision makers need to know about OpenAI’s strategic shift For enterprise customers, OpenAI’s move could significantly reshape AI implementation strategies. Organizations that have hesitated to build critical infrastructure atop subscription-based models now have reason to reconsider their approach. The ability to run models locally addresses persistent concerns around data sovereignty, vendor lock-in, and long-term cost management. This shift particularly matters for regulated industries like healthcare, finance, and government, where data privacy requirements have limited cloud-based AI adoption. Self-hosted models potentially enable these sectors to implement AI in previously restricted contexts, though questions around compute requirements and operational complexity remain unanswered. For existing OpenAI enterprise customers, the announcement creates uncertainty about long-term investment strategies. Those who have built systems atop GPT-4 or o1 APIs must now evaluate whether to maintain that approach or begin planning migrations to self-hosted alternatives — a decision complicated by the lack of specific details about the forthcoming model’s capabilities. Beyond base models: How the AI industry’s competitive landscape is fundamentally changing OpenAI’s pivot highlights a broader industry trend: the commoditization of foundation models and the shifting focus toward specialized applications. As base models become increasingly accessible, differentiation increasingly happens at the application layer — creating opportunities for startups and established players alike to build domain-specific solutions. This doesn’t mean the race to build better base models has

But analysts have questioned whether the Microsoft move truly addresses those European business concerns. Phil Brunkard, executive counselor at Info-Tech Research Group UK, said, commenting on last month’s announcement of the EU Data Boundary for the Microsoft Cloud,  “Microsoft says that customer data will remain stored and processed in the EU and EFTA, but doesn’t guarantee true data sovereignty.” And European companies are now rethinking what data sovereignty means to them. They are moving beyond having it refer to where the data sits to focusing on which vendors control it, and who controls them. Responding to the new Euro cloud plan, another analyst, IDC VP Dave McCarthy, saw the effort as “signaling a growing European push for data control and independence.” “US providers could face tougher competition from EU companies that leverage this tech to offer sovereignty-friendly alternatives. Although €1 million isn’t a game-changer on its own, it’s a clear sign Europe wants to build its own cloud ecosystem—potentially at the expense of US market share,” McCarthy said. “For US providers, this could mean investing in more EU-based data centers or reconfiguring systems to ensure European customers’ data stays within the region. This isn’t just a compliance checkbox. It’s a shift that could hike operational costs and complexity, especially for companies used to running centralized setups.” Adding to the potential bad news for US hyperscalers, McCarthy said that there was little reason to believe that this trend would be limited to Europe. “If Europe pulls this off, other regions might take note and push for similar sovereignty rules. US providers could find themselves adapting to a patchwork of regulations worldwide, forcing a rethink of their global strategies,” McCarthy said. “This isn’t just a European headache, it’s a preview of what could become a broader challenge.” source

By Patrick Hoff ( April 3, 2025, 10:54 AM EDT) — The Second Circuit on Thursday reopened a proposed class action accusing IBM of shorting retirees on pension payments by using outdated mortality data, saying the trial court should’ve sought clarity about certain documents before tossing the case…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

By Tom Zanki ( March 31, 2025, 9:43 PM EDT) — Nasdaq is seeking to weed out volatile stocks by tightening listing standards for small companies conducting initial public offerings or uplistings, although lawyers caution that new rules could prompt capital-hungry companies to pursue other listing strategies, including reverse mergers…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

OCBC has emerged as a leader in enterprise AI adoption, seamlessly integrating generative AI (genAI) across its operations. I recently spoke with Donald MacDonald, head of OCBC’s Group Data Office, about the bank’s AI journey. Q: Donald, OCBC’s success with AI seems to stem from a long-term vision rather than a sudden pivot. How did this foundation come about? Donald MacDonald: One of our core principles has always been “achieve greater results with the same resources.” We never had multiple data platforms or multiple teams — it was always going to be one central team taking the lead on analytics for OCBC. This became a strength. Two decades ago, we made a strategic bet on a single, centralized data platform rather than fragmented solutions across business units. That decision drove us to integrate and scale our analytics consistently over the years. When generative AI came along, we already had the necessary foundations — clean, structured data; robust deployment processes; and a strong AI team — to move fast. If you’re spending your time fixing data pipelines while trying to innovate, you’re already behind. Q: OCBC’s approach to generative AI is notably pragmatic. How do you balance rapid deployment with regulatory constraints? Donald MacDonald: We’ve built a governance-first approach without letting it become a bottleneck. Our model management platform (MMP) and Hydra framework ensure that AI models are rigorously monitored, but they also streamline deployment. We don’t wait for perfection; we roll out solutions incrementally while keeping a close eye on performance and risk. Take OCBC GPT, for example. This is the bank’s internal enterprisewide generative AI assistant, which helps our employees create content and generate ideas. This application is freely available to every employee within the bank, used around 250,000 times a month. At the same time, because it was built within our secure on-premises environment, we could iterate safely and improve it in real time. Regulation isn’t a barrier when you bake it into your AI strategy from day one. This creates the trust that’s essential to high-performance IT. Q: One of OCBC’s strengths has been democratizing AI access within the organization. How do you ensure AI adoption at scale? Donald MacDonald: AI adoption isn’t about flashy demos; it’s about usable tools that add business value. We’ve made AI tools like Buddy and OCBC GPT available to all employees, not just data scientists. But access alone isn’t enough; you need an open culture where people aren’t afraid to experiment. Our AI team operates more like an internal open-source hub, where employees can experiment and build on existing tools rather than waiting for centralized IT to do everything. The result? We see genAI adoption spread organically, often in ways we wouldn’t have anticipated. This flexibility enhances our organizational adaptivity, allowing us to respond quickly to emerging opportunities. Q: OCBC has also developed AI copilots tailored to specific roles. What has been the impact of these specialized tools? Donald MacDonald: The real magic happens when AI goes beyond generic use cases and starts solving deeper role-specific problems: Take HOLMES AI, our relationship manager (RM) copilot that generates curated talking points based on investment research, saving the front line hours of prep work, or our compliance copilot, which reduces some customer onboarding tasks from days to potentially just minutes. These AI copilots aren’t gimmicks; they tangibly improve workflow efficiency and decision-making. We’re now exploring multiagent AI systems that can automate even more complex processes, like customer onboarding in private banking. Strategic alignment between business needs and technology is key here. Q: Looking ahead, how do you envision AI transforming the banking industry? Donald MacDonald: The future of banking will be fundamentally altered by AI, changing how we operate and engage with customers. At OCBC, we’ve already seen how generative AI enhances efficiency and personalization. For instance, our Buddy chatbot helps employees navigate over 400,000 internal documents, while our agentic AI systems significantly reduce lengthy private banking onboarding times. Looking forward, I see banking becoming more predictive and personalized, with AI enabling us to anticipate customer needs before they even realize them. This will free our staff from routine tasks, allowing them to focus on more complex, value-added activities that require human judgment. Of course, we’ll continue to approach this transformation responsibly, maintaining robust data privacy protections and governance frameworks. The AI-powered bank of the future will blend advanced technology with human expertise, delivering services that are both efficient and deeply personalized. Conclusion OCBC’s journey demonstrates how a thoughtful approach to AI can deliver significant business value while managing risk effectively. By building strong foundations, aligning technology with business objectives, and creating a culture of innovation, the financial services group has positioned itself at the forefront of AI adoption in financial services. Forrester clients can read our complete case study to explore how OCBC exemplifies high-performance IT through its strategic alignment, trust-building governance, and adaptive capabilities in AI implementation. source

Through its RISE with SAP offering, SAP gives customers a rapid transition to the cloud. This offering combines SAP S/4HANA Cloud with complementary SAP services to support companies in their digital transformation. This transformation takes place in three steps: redesigning processes, technical migration, and building the intelligent enterprise. S/4HANA On-Premise With SAP S/4HANA On-Premise, the customer manages the S/4HANA system, the HANA database, applications, data center, operating systems, middleware, and network on-premises. The customer is also responsible for maintenance and development. This allows for maximum flexibility. In this variant, the customer is responsible for adapting the software to company-specific requirements, controlling the frequency and scheduling of upgrades, and installing support packages. S/4HANA Hybrid In principle, a combination of S/4HANA Cloud and S/4HANA On-Premise is also possible. This is referred to as a hybrid approach. Companies can run selected processes, such as core processes, locally on their own servers with SAP S/4HANA On-Premise. Other processes that do not require individual adaptation can be outsourced to the cloud. source

Last week’s mega deal of Google acquiring CNAPP provider Wiz for $32 billion has some lamenting the future of IPOs in the cybersecurity space. Wiz was on a high growth trajectory, and given that Wiz had previously rebuffed Google’s interest in the summer of 2024, many assumed Wiz was on target for a 2025 IPO, the success of which was meant to serve as a bellwether for the overall health of the cybersecurity market. With Wiz no longer an IPO candidate, has momentum for cybersecurity IPOs stalled? In the short term, the answer is yes, but that is more to do with the health of the overall tech IPO market, not just cybersecurity. Genesys, a provider of AI-driven call center software, recently postponed its planned spring 2025 IPO, citing market uncertainty, with plans to revisit an IPO in the second half of 2025. And despite last year’s uneven macroeconomic environment, there were still over 220 IPOs in the US stock markets last year, up from 150 in 2023. While approximately 10% of the 2024 IPOs were of the SPAC (special-purpose acquisition company) variety, there were still several significant tech IPOs in 2024, including Reddit, OneStream, Ingram Micro, and ServiceTitan, to name a few. Rubrik’s April 2024 IPO also marked the first cybersecurity-related IPO in two years. While the cybersecurity IPO market may be muted right now, there are still several possible cybersecurity IPO candidates for 2025. While there is a lot of discussion on tariffs and the current market volatility hindering IPOs, indexes such as the Cboe’s VIX Index (which analyzes S&P 500 index options to derive a forward-looking projection of volatility) have not moved as much as the overall market indices. Some have suggested that this is because much of current volatility is derived from policy decisions (like tariffs), meaning they can be quickly reversed and are also not tied as directly to structural economic factors. Despite this current uncertainty, the resilience of the US stock market, and the fact that there are still several cybersecurity companies seeking a liquidity event, mean that cybersecurity IPOs could still happen in 2025, especially in the second half of the year. The current tech IPO bellwether is AI darling CoreWeave. Despite a tepid initial trading day, CoreWeave has since rebounded and its shares are up. This current (but by no means comprehensive) list of potential cybersecurity IPO candidates for the fall of 2025 can be put into two distinct categories: Category one: venture-backed, with $500 million or more in VC funding and high annual recurring revenue (ARR) growth of over 40% Netskope: In October 2024, Netskope CEO Sanjay Beri indicated plans to proceed with an IPO in the second half of 2025, depending on market conditions and investor appetite. Netskope has raised over $1 billion in venture capital, reported over $500 million in ARR, and competes in the high demand Zero Trust edge network security segment. While Netskope has not filed an S-1 form with the SEC yet, it is a vendor to watch in 2025 as a strong contender for an IPO. Snyk: Like Netskope, application security developer Snyk has raised over $1 billion in venture capital, hit $300 million in ARR last year, and is growing ARR 40% annually. While Snyk has not filed an S-1 with the SEC, it is long rumored to be an IPO candidate and fits the criteria for this category. Application security remains a high growth area. OneTrust: This privacy management company has raised over $1 billion and is exceeding $500 million in ARR. While the firm has been mum on any IPO plans, it meets the size, valuation, and growth metrics for an IPO. Armis has not reached the $500 million ARR milestone yet but is growing rapidly and has raised over $800 million in venture capital. According to Bloomberg, it is looking at 2026 for an IPO, so continued success and growth in 2025 will position the company for an IPO next year. Category two: established cybersecurity firm owned by private equity (PE) firms for two or more years and seeking exit This category already has a successful 2025 IPO: identity management and governance vendor SailPoint, which PE investor Thoma Bravo took public in February, raising $1.4 billion in the IPO at a $12 billion valuation. Some other IPO candidates in this category include: Proofpoint: Email and data security vendor Proofpoint was taken private by Thoma Bravo for $12 billion in 2021. Last fall, Proofpoint indicated plans to return to public markets within 12 to 18 months. Thoma Bravo has held Proofpoint for five years; this would be a good IPO candidate once market conditions improve. Illumio: Also owned by Thoma Bravo since 2021, Illumio has raised more than $500 million in funding, is growing fast, and had a $2 billion-plus valuation when acquired by Thoma Bravo. Illumio was a Leader in The Forrester Wave™: Microsegmentation Solutions, Q3 2024, last year and competes in the high-demand cloud security and Zero Trust segments. Delinea: Last week, the PE owner of privileged identity management vendor Delinea indicated that it’s considering IPO plans. TPG has owned Delinea since 2021 when it merged Thycotic and Centrify and renamed the new entity Delinea. With Delinea’s ARR at almost $400 million, it fits the criteria, especially if the PE owner is looking to exit this investment in 2025. While macroeconomic factors or geopolitical events could affect the public market’s appetite for tech IPOs, this post has hopefully shown that there are plenty of well-funded and capitalized cybersecurity companies capable of going public in the next 12 months based on market conditions. And seeing as all these companies are growing and investing in their product offerings, security professionals should view pending cybersecurity IPOs as a positive validation of the overall cybersecurity market and their supplier’s position within that market. source

Let’s face it: As a US government leader, you are facing unprecedented change. The speed, depth, and widespread nature of the current change in the US government is dramatic, and you are uniquely challenged at this moment in helping your people stay engaged to deliver great outcomes while under significant mental stress. Our research shows that government workers who see how their work contributes to their organization’s overall success are four times more likely to put effort into their growth and development. They’re also three times more likely to be productive at work than those who do not see how their work contributes. Consider further that 41% of employees who remain after a layoff experience a 36% decline in organizational commitment and a 20% decline in job performance. When you pair both factors together, it is easy to see the importance of keeping your employees engaged and connected to their work after a layoff, when motivation is a challenge. Even if it is a deliberate strategy by executive leadership to further reduce headcount in response to continued uncertainty, you, as the directly aligned manager of your people, need to assume that they will stay for the long term. Forrester can help you navigate this uncertainty with some practical guidance. Go Beyond Surface-Level Conversations And Tactics This is not a time for leaning too heavily on team lunches, “How are you?” statements in a one-on-one, or other surface-level approaches. Colleagues losing their jobs around you can trigger deeply rooted psychological fears and emotions. Consider that many of your employees might be the sole or primary earner of a household. Many may be concerned about feeding their family if they are “next.” How do you, as their leader, respond to this moment of strong emotion? You respond by leaning into it and doing the uncomfortable work of connecting with your people on a deeper level: Open the door to deep sharing of how your people are feeling by telling a relatable story from your past (along with how you currently feel about the environment). Share the immense impact it had on you, how you navigated it, and what the end result was. If you do not have this story or are uncomfortable with this approach, find someone in your network who is willing and ensure that this employee is fully open to this type of dialogue before beginning. Focus on what you can control by continuing the dialog. Stay committed to it; not every conversation needs to be deep and painful, but your employees should know that this door is always open to them. Check in on them regularly after your initial deep work to let them know that you are there for them any time they need. As with any organizational change, you cannot control every variable. Focus on what you can control with your teams to stay focused on the task at hand. Delivering outcomes while change swirls around you will build resilience both within your team and in yourself as a leader. After you cover the personal side of the conversation, keep a sharp focus on how their work drives meaningful outcomes for overall goals. Make a plan that establishes a strong and purposeful line of communication between you and your team. Employees who have a strong understanding of how their work ties to organizational goals are much more likely to deliver strong results because they can see a tangible impact. Failure to establish this linkage will result in a precipitous decline in results. Do Not Forget To Practice Self-Care It is imperative as a leader to routinely practice self-care. If you are not in the best mental state, you will not be able to optimally support your employees and the aforementioned tactics will not succeed at achieving high engagement in your teams. Self-care takes many forms and is highly individualized. Some people exercise, read, or sit on a riding lawn mower for 3 hours. Our guidance is to deploy any techniques that bring about mental calm and help reduce stressors so that you can bring absolute clarity to supporting your teams. Effective self-care mindfulness routines have been found to moderately reduce symptoms of anxiety and depression, two very common conditions experienced during times of stress such as a period of layoffs. Don’t make the mistake of believing that the highly productive people you are relying on to steer your ship in a new direction aren’t also burning out. Our research finds that employees can be both burning out and highly engaged — and this includes you. As a leader, practice what you preach. Leverage the power of storytelling to share what you are doing to manage your emotions during these difficult times. It is easy to avoid these difficult topics and stay focused on the day’s tasks. To be truly effective as a change leader, we urge you to lean heavily into the challenge — for yourself and your people. If you want to dig deeper into managing change in these unprecedented times, please reach out to us by scheduling a guidance session or an inquiry via email: . source