Latest news with #C-suite
Yahoo
12 hours ago
- Business
- Yahoo
For CEOs, AI tech literacy is no longer optional
Artificial intelligence has been the subject of unprecedented levels of investment and enthusiasm over the past three years, driven by a tide of hype that promises revolutionary transformation across every business function. Yet the gap between this technology's promise and the delivery of real business value remains stubbornly wide. A recent study by BCG found that while 98% of companies are exploring AI, only 26% have developed working products and a mere 4% have achieved significant returns on their investments. This striking implementation gap raises a critical question: Why do so many AI initiatives fail to deliver meaningful value? Spicy AI-generated TACO memes are taking over social media because 'Trump always chickens out' What is 'ghostworking'? Most employees say they regularly pretend to work Lego's first book nook is an addictively interactive diorama A big part of the answer lies in a fundamental disconnect at the leadership level: to put it bluntly, many senior executives just don't understand how AI works. One recent survey found that 94% of C-suite executives describe themselves as having an intermediate, advanced, or expert knowledge of AI, while 90% say they are confident in making decisions around the technology. Yet a large study of thousands of U.S. board-level executives reported in MIT Sloan Management Review in 2024 found that just 8% actually have 'substantial levels of conceptual knowledge regarding AI technologies.' The only way AI initiatives can deliver significant value is when they are aligned with the organization's broader enterprise architecture. When I introduced the terminology of 'strategic enterprise architecture' back in 2000 (e-Enterprise, Cambridge University Press), I wanted to emphasize the importance of aligning technical architecture with the broader structure of the business as a whole–its purpose, strategies, processes, and operating models. With AI, this alignment is more important than ever. But it relies on the ability of senior leaders to understand both parts of the enterprise equation. The current gap between confidence and competence creates a dangerous decision-making environment. Without foundational AI literacy, leaders simply can't make informed decisions about how any given AI implementation fits with strategic priorities and the processes and existing tech infrastructure of the business. Ultimately, they end up delegating critical strategic choices to technical teams that often lack the business context necessary for value-driven implementation. The result? Millions of dollars invested in AI initiatives that fail to deliver on their promises. In addition to project failure, a lack of AI literacy leads to strategic opportunity costs. When CEOs can't distinguish between truly transformative AI applications and incremental improvements, they risk either underinvesting in game-changing capabilities or overspending on fashionable but low-impact technologies. Becoming AI-literate doesn't mean that CEOs need to be able to build neural networks or understand the mathematical intricacies of deep learning algorithms. Rather, leaders need the kind of foundational practical knowledge that lets them align AI initiatives with core business operations and strategic direction. At minimum, CEOs should develop a working understanding of AI in three broad areas. CEOs should understand the differences between the four major types of AI, the business applications of each, and their current maturity level. Analytical/Predictive AI focuses on pattern recognition and forecasting. This technology has been maturing for decades and forms the backbone of data-driven decision making in domains from finance to manufacturing. Deterministic AI systems apply predefined rules and logic to automate processes and decision-making, creating efficiency but requiring careful governance. Generative AI—the current hype king—creates new content that resembles human work, offering unprecedented creative capabilities alongside significant ethical challenges. Agentic AI is the new kid on the block. It not only analyzes or produces outputs but takes bounded actions toward defined goals. Agentic AI offers the greatest opportunity and the largest risks for enterprise transformation, but is largely untested at scale. The infrastructure underpinning AI implementations shapes what is possible and practical for specific organizations. · Deployment Models determine where and how AI systems operate. On-premises deployments maximize control over data, systems, and compliance but require significant capital investment and specialized personnel. Cloud-based deployments offer scalability and access to cutting-edge hardware but increase exposure to data security and vendor lock-in risks. Hybrid models retain sensitive processes in-house while outsourcing other workloads. · Open and Closed Systems. Closed AI systems—proprietary systems created by commercial vendors—simplify deployment and provide enterprise-grade support but normally offer limited transparency and customization. Open (or open source) systems provide greater control and flexibility, particularly for specialized applications, but require more internal capacity and ongoing maintenance. · Computing Resource Needs vary dramatically based on how AI is deployed. Most organizations primarily use AI for inference (using the reasoning capabilities of trained models) rather than training their own models. This approach significantly reduces hardware requirements but limits customization and mission-specific capabilities. · Data Infrastructure is the foundation for successful AI implementations. This includes data pipelines for collecting and transforming information, storage systems for managing structured and unstructured data, processing frameworks for maintaining data quality, and governance mechanisms for ensuring compliance and security. Organizations with mature data infrastructure can implement AI more rapidly and effectively than those still struggling with data silos or quality issues. The contemporary AI stack comprises five interconnected layers that transform raw data into outputs designed to create value for the enterprise. · The Foundation: Data & Storage This foundation captures, cleans, and catalogs both structured and unstructured information. · The Engine: Compute & Acceleration High-density Graphics Processing Units (GPUs), AI-optimized chips, and elastic cloud clusters provide the parallel processing that deep-learning workloads require. Container orchestration tools abstract these resources, allowing cost-effective experimentation and deployment. · The Brain: Model & Algorithm This is where foundation models, domain-specific small language models, and classical machine-learning libraries coexist. Organizations must decide whether to consume models 'as-a-service,' fine-tune open-source checkpoints, or build custom networks—decisions that involve trade-offs between control, cost, and compliance. · The Connectors: Orchestration & Tooling Retrieval-augmented generation (RAG), prompt pipelines, automated evaluation harnesses, and agent frameworks sequence models into end-to-end capabilities. · User Access and Control: Applications & Governance This top layer exposes AI to users through APIs and low-code builders that embed intelligence in user-facing systems. For further foundational information on AI tech stacks, see IBM's introductory guide. How can busy executives develop the AI literacy they need to lead effectively? Here are some practical approaches to closing the knowledge gap. Establish a personal learning curriculum. Set aside time for structured learning about AI fundamentals through executive education programs, books, or online courses specifically designed for business leaders. Build a balanced advisory network. Surround yourself with advisors who bridge technical expertise and business acumen. This might include both internal experts and external consultants who can translate complex concepts into business terms without oversimplifying. Institute regular technology briefings. Create a structured process where technical teams provide regular updates on AI capabilities, limitations, and potential applications in your industry. The key is ensuring these briefings focus on business implications rather than technical specifications. Experience AI directly. Hands-on experience with AI tools provides an essential perspective. Work directly with your company's AI applications to develop an intuitive understanding of capabilities and limitations. Foster organization-wide literacy. Support AI education across all business functions, not just technical departments. When marketing, finance, operations, and other leaders share a common understanding of AI capabilities, cross-functional collaboration improves dramatically. True leadership in the age of AI begins with curiosity and the courage to CEOs become tech literate, they don't just adapt to the future—they help shape it. This post originally appeared at to get the Fast Company newsletter: Sign in to access your portfolio
Fast Company
2 days ago
- Business
- Fast Company
For CEOs, AI tech literacy is no longer optional
Artificial intelligence has been the subject of unprecedented levels of investment and enthusiasm over the past three years, driven by a tide of hype that promises revolutionary transformation across every business function. Yet the gap between this technology's promise and the delivery of real business value remains stubbornly wide. A recent study by BCG found that while 98% of companies are exploring AI, only 26% have developed working products and a mere 4% have achieved significant returns on their investments. This striking implementation gap raises a critical question: Why do so many AI initiatives fail to deliver meaningful value? Knowledge gap A big part of the answer lies in a fundamental disconnect at the leadership level: to put it bluntly, many senior executives just don't understand how AI works. One recent survey found that 94% of C-suite executives describe themselves as having an intermediate, advanced, or expert knowledge of AI, while 90% say they are confident in making decisions around the technology. Yet a large study of thousands of U.S. board-level executives reported in MIT Sloan Management Review in 2024 found that just 8% actually have 'substantial levels of conceptual knowledge regarding AI technologies.' The only way AI initiatives can deliver significant value is when they are aligned with the organization's broader enterprise architecture. When I introduced the terminology of 'strategic enterprise architecture' back in 2000 (e-Enterprise, Cambridge University Press), I wanted to emphasize the importance of aligning technical architecture with the broader structure of the business as a whole–its purpose, strategies, processes, and operating models. With AI, this alignment is more important than ever. But it relies on the ability of senior leaders to understand both parts of the enterprise equation. Opportunity costs The current gap between confidence and competence creates a dangerous decision-making environment. Without foundational AI literacy, leaders simply can't make informed decisions about how any given AI implementation fits with strategic priorities and the processes and existing tech infrastructure of the business. Ultimately, they end up delegating critical strategic choices to technical teams that often lack the business context necessary for value-driven implementation. The result? Millions of dollars invested in AI initiatives that fail to deliver on their promises. In addition to project failure, a lack of AI literacy leads to strategic opportunity costs. When CEOs can't distinguish between truly transformative AI applications and incremental improvements, they risk either underinvesting in game-changing capabilities or overspending on fashionable but low-impact technologies. What CEOs need to know Becoming AI-literate doesn't mean that CEOs need to be able to build neural networks or understand the mathematical intricacies of deep learning algorithms. Rather, leaders need the kind of foundational practical knowledge that lets them align AI initiatives with core business operations and strategic direction. At minimum, CEOs should develop a working understanding of AI in three broad areas. 1. The Types of AI CEOs should understand the differences between the four major types of AI, the business applications of each, and their current maturity level. Analytical/Predictive AI focuses on pattern recognition and forecasting. This technology has been maturing for decades and forms the backbone of data-driven decision making in domains from finance to manufacturing. Deterministic AI systems apply predefined rules and logic to automate processes and decision-making, creating efficiency but requiring careful governance. Generative AI —the current hype king—creates new content that resembles human work, offering unprecedented creative capabilities alongside significant ethical challenges. Agentic AI is the new kid on the block. It not only analyzes or produces outputs but takes bounded actions toward defined goals. Agentic AI offers the greatest opportunity and the largest risks for enterprise transformation, but is largely untested at scale. 2. Technical Infrastructure Considerations The infrastructure underpinning AI implementations shapes what is possible and practical for specific organizations. · Deployment Models determine where and how AI systems operate. On-premises deployments maximize control over data, systems, and compliance but require significant capital investment and specialized personnel. Cloud-based deployments offer scalability and access to cutting-edge hardware but increase exposure to data security and vendor lock-in risks. Hybrid models retain sensitive processes in-house while outsourcing other workloads. · Open and Closed Systems. Closed AI systems—proprietary systems created by commercial vendors—simplify deployment and provide enterprise-grade support but normally offer limited transparency and customization. Open (or open source) systems provide greater control and flexibility, particularly for specialized applications, but require more internal capacity and ongoing maintenance. · Computing Resource Needs vary dramatically based on how AI is deployed. Most organizations primarily use AI for inference (using the reasoning capabilities of trained models) rather than training their own models. This approach significantly reduces hardware requirements but limits customization and mission-specific capabilities. · Data Infrastructure is the foundation for successful AI implementations. This includes data pipelines for collecting and transforming information, storage systems for managing structured and unstructured data, processing frameworks for maintaining data quality, and governance mechanisms for ensuring compliance and security. Organizations with mature data infrastructure can implement AI more rapidly and effectively than those still struggling with data silos or quality issues. 3. The AI Tech Stack The contemporary AI stack comprises five interconnected layers that transform raw data into outputs designed to create value for the enterprise. · The Foundation: Data & Storage This foundation captures, cleans, and catalogs both structured and unstructured information. · The Engine: Compute & Acceleration High-density Graphics Processing Units (GPUs), AI-optimized chips, and elastic cloud clusters provide the parallel processing that deep-learning workloads require. Container orchestration tools abstract these resources, allowing cost-effective experimentation and deployment. · The Brain: Model & Algorithm This is where foundation models, domain-specific small language models, and classical machine-learning libraries coexist. Organizations must decide whether to consume models 'as-a-service,' fine-tune open-source checkpoints, or build custom networks—decisions that involve trade-offs between control, cost, and compliance. · The Connectors: Orchestration & Tooling Retrieval-augmented generation (RAG), prompt pipelines, automated evaluation harnesses, and agent frameworks sequence models into end-to-end capabilities. · User Access and Control: Applications & Governance This top layer exposes AI to users through APIs and low-code builders that embed intelligence in user-facing systems. For further foundational information on AI tech stacks, see IBM's introductory guide. Developing AI literacy in the C-Suite How can busy executives develop the AI literacy they need to lead effectively? Here are some practical approaches to closing the knowledge gap. Establish a personal learning curriculum. Set aside time for structured learning about AI fundamentals through executive education programs, books, or online courses specifically designed for business leaders. Build a balanced advisory network. Surround yourself with advisors who bridge technical expertise and business acumen. This might include both internal experts and external consultants who can translate complex concepts into business terms without oversimplifying. Institute regular technology briefings. Create a structured process where technical teams provide regular updates on AI capabilities, limitations, and potential applications in your industry. The key is ensuring these briefings focus on business implications rather than technical specifications. Experience AI directly. Hands-on experience with AI tools provides an essential perspective. Work directly with your company's AI applications to develop an intuitive understanding of capabilities and limitations. Foster organization-wide literacy. Support AI education across all business functions, not just technical departments. When marketing, finance, operations, and other leaders share a common understanding of AI capabilities, cross-functional collaboration improves dramatically. True leadership in the age of AI begins with curiosity and the courage to learn. When CEOs become tech literate, they don't just adapt to the future—they help shape it.
Forbes
2 days ago
- Business
- Forbes
Is Target Blaming Boycotts For Its Slump?
Is Target Blaming Boycotts For Its Slump? getty The country's seventh largest retailer has been the bullseye for grassroots consumer boycotts, but the real cause of its woes is in the c-suite. For a moment there, at the onset of the COVID-19 quarantine, Target seemed to be riding high on a wave of innovation that broke out when retailers of every stripe and category had to scramble to save their businesses from a global catastrophe. As we reported here in 2021, Target managed a rapid roll-out of an experimental click-and-curbside-collect program while simultaneously building out a credible e-commerce platform to drive sales. The gambit was a big success. Target's e-commerce business boomed, growing faster than Amazon and Walmart. The company won kudos for touches such as placing well-staffed pickup counters directly in front of the main entrance, ensuring crisp customer service. Shoppers were spared from standing in long lines and were more inclined to park and take a stroll through the store before leaving. What a difference a pandemic makes. Over the past two years, Target has found itself on fumbling defense. At this time a year ago, the company first started reporting that a long stretch of revenue growth had run out of gas because (according to Target execs) its customer base had been spending less on nesting (think throw pillows and furnishings) and more on travel and entertainment. For its fiscal year that ended on January 31, 2024, the company said revenue retreated by 1.6% and comparable store sales sagged by nearly 4%. In sharp contrast, both Walmart and Costco—with overlapping customer bases—posted annual revenue growth of more than 6%. What went wrong? A series of clear leadership missteps? Among the explanations was a boycott over an in-store Pride Month promotion in 2023 that backfired spectacularly. Also in 2023, Target closed nine stores in urban areas citing theft and violence, but an in-depth CNBC investigation claimed it found that crime rates were actually lower at the closed stores than at other nearby stores that remained open. The difference: stores that stayed open were in higher-income neighborhoods. According to CNBC, the findings, 'cast doubt on Target's explanation for the store closures and raise questions about whether the company's announcement was designed to advance its legislative agenda and obscure poor financial performance.' Target ran into yet another publicity buzzsaw last year during Black History Month when several historical figures such as Booker T. Washington were misidentified on a collection of refrigerator magnets. And a customer filed a class action lawsuit claiming the company 'surreptitiously' operated an anti-theft surveillance system that violated Illinois' Biometric Information Privacy Act. In the head-to-head competition with rivals, Target has seemed to be running a me-too campaign. It was late in developing a robust line of private label merchandise, far behind Walmart and Costco. And the company ballyhooed a long-term plan to add 300 mostly full-sized locations just as its rivals were planning smaller stores in neighborhood shopping centers. Finally, four days after the new administration took office in November, Target dismantled its DEI efforts—in which it had invested a lot of brand capital—which unleashed a fresh wave of scorn. The latest news is more bleakness for the nation's seventh-largest retailer. Since January, foot traffic has been steadily declining. The company's management has been mostly silent, according to a recent report on Forbes, and analysts describe the company's woes as self-inflicted' and its leadership as 'drifting.' Given the current economic cycle, with so much uncertainty and wariness among consumers, it's hard to imagine what Target could do to rescue itself from itself. Before the pandemic there were rumors that Amazon had its eye on acquiring the company as a quick way to create a bricks-and-mortar presence that could compete with Walmart, Costco, and others. Nothing became of the Amazon rumor, and it is harder to imagine today than it might have been in 2019. But it will probably take a shake-up of equal magnitude—a leveraged buyout and a clean c-suite slate, perhaps—and a major rebranding to reverse the slide and possibly resurrect Target's once-coveted cachet as the classy discount store. Investors might consider, what has been the 5-year return on invested shares? As of today, negative twenty-one percent (-21%) roughly vs S&P at plus ninety-four percent (+94%).
Yahoo
3 days ago
- Business
- Yahoo
$57.7 Bn Online Recruitment Platform Market by Functionality, Seniority Level, Deployment Model, Enterprises Size, Industry - Global Forecast to 2030
Online Recruitment Platform Market Dublin, May 29, 2025 (GLOBE NEWSWIRE) -- The "Online Recruitment Platform Market by Functionality, Seniority Level, Deployment Model, Enterprises Size, Industry - Global Forecast to 2030" report has been added to Online Recruitment Platform Market grew from USD 51.53 billion in 2024 to USD 57.70 billion in 2025. It is expected to continue growing at a CAGR of 12.17%, reaching USD 102.70 billion by 2030. The digital transformation of talent acquisition has reached an inflection point, demanding a fresh perspective on how organizations attract, engage, and hire top talent. As competition intensifies and workforce expectations evolve, recruitment platforms have morphed into strategic hubs that drive employer branding, candidate experience, and data-driven decision making. Understanding these dynamics is critical for HR leaders, technology buyers, and C-suite executives executive summary distills the most pressing trends and shifts shaping the online recruitment landscape. It unpacks the transformative forces driven by technological innovation, policy changes, and evolving candidate behaviors. By exploring the implications of new US tariffs, examining market segments, regional variations, and competitive dynamics, this analysis provides a cohesive narrative designed to inform strategic planning. Ultimately, this introduction lays the groundwork for targeted recommendations and a robust methodology, equipping decision makers with the insight needed to navigate an increasingly complex Digital Transformation is Redefining Talent AcquisitionTalent acquisition is undergoing a seismic shift propelled by advanced analytics, artificial intelligence, and a relentless focus on candidate experience. Algorithms now power intelligent resume screening, predictive matching, and automated interview scheduling, reducing time-to-hire and improving quality of hire. Meanwhile, mobile-first and social recruiting strategies have reshaped how organizations engage passive and active candidates, creating seamless, intuitive touchpoints across multiple digital a result, recruitment platforms have transitioned from transactional systems into holistic talent engagement solutions. Employers are integrating chatbots and virtual assistants to answer candidate queries in real time, while immersive technologies such as video assessments and gamified challenges foster richer interactions. This confluence of innovation not only enhances recruitment efficiency but also strengthens employer brands. In this era of digital transformation, organizations that embrace these technological advancements will secure the competitive advantage needed to attract, nurture, and retain critical talent. Regional Dynamics Driving Growth Across Recruitment SolutionsAcross the Americas, North America retains leadership with mature cloud adoption and a strong appetite for AI-driven sourcing, while Latin American markets are gaining momentum through mobile-first deployments and regional talent networks. Local labor regulations and diversity mandates continue to influence platform feature roadmaps, driving localized innovation and strategic Europe, Middle East & Africa, diverse regulatory environments require solutions that balance data sovereignty with cross-border collaboration. The European Union's stringent data privacy framework has prompted robust on-premise offerings, while emerging markets in the Middle East seek scalable cloud deployments to keep pace with rapid economic growth. African nations are also accelerating digital transformation efforts, leveraging recruitment platforms to streamline large-scale public and private sector hiring exhibits some of the fastest rates of platform adoption globally. China, India, Japan, Australia, and Southeast Asian economies are investing heavily in AI-powered candidate engagement, localized career portals, and integrated learning management features. High population densities and burgeoning digital workforces underscore the immense potential for cloud-native solutions, with governments and large enterprises driving large-scale deployment projects to address critical skills Landscape of Leading Recruitment Platform VendorsThe competitive landscape of online recruitment platforms is defined by a mix of established enterprise suites and rapidly evolving niche providers. Leading vendors leverage comprehensive talent management ecosystems that integrate sourcing, assessment, onboarding, and analytics, targeting organizations seeking end-to-end solutions. These incumbents continuously enhance their offerings through strategic acquisitions and global alliance programs to maintain market the same time, challenger companies differentiate by focusing on specialized modules, user-centric design, and agile implementation models. These innovators often excel in delivering rapid ROI through preconfigured industry templates and AI-enabled candidate matching. Their customer success teams emphasize consultative deployment approaches, ensuring that mid-market buyers can achieve value quickly without extensive between platform vendors and complementary service providers have become increasingly common. By forging partnerships with assessment firms, employer branding agencies, and HR analytics consultancies, leading companies enrich their ecosystems and provide integrated experiences. As the industry matures, the ability to weave together a best-of-breed partner network may prove as important as standalone product capabilities. Key Attributes: Report Attribute Details No. of Pages 194 Forecast Period 2025 - 2030 Estimated Market Value (USD) in 2025 $57.7 Billion Forecasted Market Value (USD) by 2030 $102.7 Billion Compound Annual Growth Rate 12.1% Regions Covered Global Market Segmentation & Coverage Functionality Applicant Tracking Systems Career Portals Recruitment Marketing Platforms Social Recruiting Platforms Seniority Level Entry Level Executive Level Mid Level Senior Level Deployment Model Cloud-Based On-Premise Enterprises Size Large Enterprises Small & Medium Enterprises Industry Aerospace & Defense Automotive & Transportation Banking, Financial Services & Insurance Building, Construction & Real Estate Consumer Goods & Retail Education Energy & Utilities Government & Public Sector Healthcare & Life Sciences IT & Telecommunication Manufacturing Media & Entertainment Travel & Hospitality This research report categorizes to delves into recent significant developments and analyze trends in each of the following companies: Bullhorn, Inc. CareerBuilder, LLC Ceipal Corp. ClearCompany, LLC Coderbyte Cornerstone OnDemand, Inc. Dayforce, Inc. DHI Group, Inc. Eightfold AI Inc. Employ, Inc. HackerRank HerKey Restart Portal Private Limited HT Media Limited iCIMS, Inc. Info Edge (India) Limited iSmartRecruit JobScore, Inc. Joinrs S.r.l. LHH Recruitment Solution LinkedIn Corporation Michael Page International Recruitment Pvt Ltd Oracle Corporation Randstad N.V. Recruit Holdings Co., Ltd. Scholiverse Educare Private Limited Seek Limited Inc. Talentica Software India Pvt. Ltd. TalentLyft Platform TalentNow TeamLease Service Limited Times Internet Ltd. Top Echelon Software, LLC ZipRecruiter, Inc. Zoho Corporation Pvt. Ltd. For more information about this report visit About is the world's leading source for international market research reports and market data. We provide you with the latest data on international and regional markets, key industries, the top companies, new products and the latest trends. Attachment Online Recruitment Platform Market CONTACT: CONTACT: Laura Wood,Senior Press Manager press@ For E.S.T Office Hours Call 1-917-300-0470 For U.S./ CAN Toll Free Call 1-800-526-8630 For GMT Office Hours Call +353-1-416-8900Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Forbes
3 days ago
- Business
- Forbes
Employees Get 46% Less Focus Time Than They Need, Research Reveals
Time to focus getty Modern work can be characterized by a level of busyness unseen in previous years. Our time can get snapped up by sitting in meetings, replying to emails, and responding to urgent requests. For anyone who has sat through a meeting you didn't need to be in, who was cc'd in a group email (for your reference), or feels frustrated by the lack of time to focus, think, and reflect, this article is for you. The recent Microsoft Office Trends Report, sampling over 10,000 Microsoft Office users, revealed some alarming findings about how busy employees are at work. Employees are averaging 6.6 hours of overtime each week, attending 29.6% more meetings than they would like to, and are experiencing an average of 4.7 cancelled and rescheduled meetings per week. Even with working overtime, the results indicate that busy work is up, and focused work is down. Employees surveyed reported that they can access 46% less focus time than they report needing. These statistics are heightened for leaders. The C-suite spends, on average, 10.2 hours per week working overtime. This is 35.3% more overtime than the average employee and adds up to over 50 hours per week. Executives also attend the greatest number of meetings per week, at 11.5, compared with someone in a non-managerial role attending 8.2. Focus time is the time needed for often independent work dedicated to achieving long-term goals or simply getting things done. When asked about challenges relating to scheduling, respondents ranked 'defending enough focus time to get stuff done' as the number one issue (63.9%), followed by managing and syncing multiple calendars (62.9%), and keeping schedules flexible to accommodate urgent changes (60.1%). Providing boundaries to protect work-life balance also featured in the list (47.4%, which is unsurprising when looking back at the amount of overtime workers are racking up each week. Executives, managers, non-managers, consultants, and even students all reported attending more meetings per week than they indicated as their ideal number. This difference between ideal and actual meetings suggests that there is work to do to minimize unnecessary meetings. This could be achieved by better delegation, by asking for higher order summaries and action items to be shared with a larger number following a meeting with only the crucial players, or with technology. AI scheduling tools, such as Calendly, or ClickUp Calendar, can help to automate scheduling, block time for focused tasks, and limit distractions by automatically coordinating schedules and aligning optimal windows in different time zones. According to the report, employees spend an average of 4.2 hours per week just managing their calendars. Executives top this figure with 4.5 hours per week. In their average 50.2-hour work week, that equates to 9.1% of executives' overall work time spent on managing their own time. Strategic thinking occurs when we dedicate time to the important, but non-urgent goals. If it constantly feels like we're putting out fires, when everything is urgent and reactionary, we don't have the time or focus to plan strategically for the big, longer-term goals. While individuals can play a role in protecting their own boundaries, pushing back on unnecessary meetings, and adopting technological scheduling assistants, organizational cultural shifts are needed to stop this trend of busyness from collecting further momentum. Leaders can do this by role-modelling healthy work-life boundaries, encouraging staff to consider their own wellbeing as well as the wellbeing of their colleagues, and by allowing real time for focus by eliminating non-essential practices and procedures.
