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Adapting to new forms of communication and behavior So the question is not how hiring managers connect with young job candidates who, if they are well-educated, will have many job offers in a tight labor market. Rather, the question is how companies can adapt to new communication and social forms as well as changing value systems — regardless of the generation of applicants and job candidates. The answer lies in a changing system. Although Generation Z is having a catalytic effect on changing values within society, it is not the trigger. When people place more value on work-life balance, it can lead to conflicts in traditional work environments that require overtime or constant availability. For example, we offer our employees the option of working from their home office two days a week. This provides the necessary flexibility and space for balance. And it benefits mental health. A value-oriented corporate culture is a must Due to multiple crises (war, climate and inflation), employees are increasingly looking for a value-oriented corporate culture. Sustainable action and social responsibility are in demand — companies that falter here will find it difficult to attract and retain talent from Generation Z (and others). This in turn involves fine-tuning the company’s processes. source

AI can also be used to enable a much more decentralized data infrastructure by having a centralized intelligence that employs agentic AI to manage the decentralized infrastructure. Hundreds of thousands of agents can enforce standards and ensure data consistency, which, according to Sáiz, is one of the biggest challenges companies face in regard to data infrastructure. For example, AI can help ensure the systems of records of a particular client are consistent in all systems including CRM, contact center software, and financial applications. “To maintain consistency, whenever there’s a customer interaction with a contact center or with the web, all systems get the change in near real time,” says Sáiz. “Where you used to have more latency and lots of manual checks before, now it’s all driven by AI, which constantly checks on the state and the master data set to determine, based on intelligence, whether a record needs to be updated in the whole system.” Beatriz Sanz Sáiz, global AI sector leader, EY EY According to Sáiz, knowledge is becoming more important than data because it helps interpret the data. A knowledge layer can be built on top of the data infrastructure to provide context and minimize hallucinations. “If somebody in telco runs a forecasting model, the variables, inputs, and results will be different than running the same model for financial forecasting,” she says. “The more you focus on knowledge, the more accurate your AI.” source

The one constant of the dynamic world of AI is change — and the latest surge of innovation in this space could transform the enterprise workforce.  AI agents are capable of collaboration and decision making without human intervention. That’s a step change compared with generative AI, which can produce human-like text, generate insights, and automate tasks, but only works with human prompts. As organizations seek to become more agile and efficient, using AI agents across their procurement and supply chain function offers a pathway to growth in challenging economic conditions. What are AI agents? As Grant Gross writes in CIO.com, agentic AI can be thought of as a more operational, business-specific way of leveraging genAI. It moves beyond genAI’s creational capabilities toward autonomous decision-making in enterprise workflows. Agentic AI, at its core, is designed to automate a specific function within an organization’s myriad business processes, without human intervention.[1] Procurement teams today often grapple with reams of data, leading to slow decision-making and missed opportunities. They’re also held back by manual processes that prevent them from monitoring real-time supplier risks and compliance issues. Enter AI agents, which can help teams rapidly understand large datasets, monitor supplier performance in real time, and automate repetitive tasks, reducing cycle times by as much as 30%, according to The Hackett Group.[2] Agentic AI can also automatically enforce procurement policies to help businesses control costs.  “AI agents are set to revolutionize procurement by addressing existing challenges, enhancing efficiency, and paving the way for a more strategic and proactive procurement function,” says Santosh Nair, GEP’s chief product officer. “Organizations that embrace this technology stand to gain a competitive edge in the evolving business landscape.” Due to these transformative benefits, it comes as no surprise that Gartner expects 33% of enterprise software applications will include agentic AI by 2028 — a massive uptick from less than 1% in 2024.[3] Meanwhile Author Brian Hopkins, vice president of the Forrester emerging tech portfolio, says: “AI agents are now leveraging advanced language models to perform complex tasks, make decisions, and interact autonomously on behalf of enterprises or individuals,” he writes. “This shift from purely generative AI to ‘agentic AI’ promises more sophisticated and less brittle automation capabilities.”[4] AI agents in procurement By implementing agentic AI into their operations, procurement teams will have new decision-making partners that work at lightning-fast speeds with laser-like precision. “AI agents can handle routine procurement activities, freeing up human resources for strategic tasks,” Nair explains. Here are some of the ways agentic AI promises to transform procurement: Managing supply chain volume and complexity. Agentic AI can process vast amounts of procurement data, optimizing inventory levels, automating order fulfillment, and identifying and rectifying supply chain inefficiencies. Real-time decision-making. By continuously analyzing market conditions, pricing fluctuations, and supplier performance, agents help procurement teams make data-driven decisions instantly, minimizing delays and reducing costs. Enhanced supplier collaboration. AI agents can automate negotiations, track supplier performance, and facilitate seamless communication — all of which Nair believes will add up to deliver better procurement outcomes. Risk mitigation through predictive analytics. By identifying potential disruptions, fraud risks, and compliance gaps before they escalate, agentic AI helps teams proactively safeguard operations and ensure supply chain resilience. “AI is projected to significantly reduce procurement costs and optimize processes, driving substantial savings,” Nair says. Learn more about how AI can optimize your supply chain, end to end. 1 CIO.com, Agentic AI: Decisive, operational AI arrives in business, August 2024 2 Coupa.com, Digital Transformation Reduces Strategic Sourcing Costs and Cycle Time By 30% 3 Gartner.com, Intelligent Agents in AI Really Can Work Alone. Here’s How, October 2024 4 CIO.com, Agentic AI: Decisive, operational AI arrives in business, August 2024 source

According to Clas Artvin, the Swedish fuel company’s CIO, embracing digitalization isn’t only better for business, but essential to brand identity considering the massive shifts taking place in the industry. source

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Jai Prakash Sharma, Executive VP – Technology at Info Edge states that technology is a key business differentiator of digital-first businesses like online job portals, matrimony platforms, ed-tech solutions, and real estate marketplaces. He says, “My role evolved beyond IT when leadership recognized that platform scalability, AI-driven matchmaking, personalized recommendations, and data-driven insights were crucial for business success. For example, in the online job market, optimizing search algorithms and AI-driven candidate-job matching directly impacts user engagement and revenue.” For Shajy Thomas, Regional Head of Tech – APAC at Technicolor, technology actively contributes to shaping long-term business outcomes. “I am a key member of the council responsible for formulating the company’s business strategy and setting goals, followed by developing 1-year, 3-year, and 5-year plans. This ensures that our technology roadmap is fully aligned with our overarching business objectives and fosters a continuous cycle of innovation and efficiency.” source

Artificial intelligence (AI) has rapidly shifted from buzz to business necessity over the past year—something Zscaler has seen firsthand while pioneering AI-powered solutions and tracking enterprise AI/ML activity in the world’s largest security cloud. Enterprises are increasingly adopting AI tools to enhance productivity, automate workflows, and accelerate decision-making. However, cybercriminals are leveraging the same technology to scale sophisticated attacks, from hyper-realistic deepfakes to advanced phishing schemes. With AI fundamentally changing both how businesses operate and how cybercriminals attack, organizations must maintain a current and comprehensive understanding of the enterprise AI landscape. The just-released ThreatLabz 2025 AI Security Report examines the intersection of enterprise AI usage and security, drawing insights from 536.5 billion AI/ML transactions in the Zscaler Zero Trust Exchange. The report reveals how enterprises worldwide and across industries are using and managing AI/ML tools, highlighting both their benefits and security concerns. It examines rising risks associated with AI, from cybercriminals weaponizing AI to the security implications of recent AI advancements like DeepSeek, while providing best practices for mitigating these risks. 5 key findings: AI usage and threat trends The ThreatLabz research team analyzed activity from over 800 known AI/ML applications between February and December 2024. Here are the notable findings: 1. AI/ML usage surged exponentially: AI/ML transactions in the Zscaler cloud increased 36x (+3,464.6%) year-over-year, highlighting the explosive growth of enterprise AI adoption. The surge was fueled by ChatGPT, Microsoft Copilot, Grammarly, and other generative AI tools, which accounted for the majority of AI-related traffic from known applications. Zscaler Figure 1: Top AI applications by transaction volume 2. Enterprises blocked a large proportion of AI transactions: 59.9% of AI/ML transactions were blocked, signaling concerns over data security and the uncontrolled use of AI applications. As organizations work to establish AI governance frameworks, many are taking a cautious approach, restricting access to certain AI applications as they refine policies around data protection. 3. U.S. and India drive the most AI/ML traffic: The United States and India recorded the highest volume of AI/ML transactions in the Zscaler cloud, reflecting strong enterprise adoption and a growing focus on AI-driven innovation. Other top contributors include the United Kingdom, Germany, and Japan, each exhibiting different levels of AI/ML activity. 4. Finance & Insurance and Manufacturing dominate AI adoption: The Finance & Insurance (28.4%) and Manufacturing (21.6%) sectors generated the most AI/ML traffic. Following them, Technology, Healthcare, and Government are integrating AI at varying rates as they navigate the fine line between adoption and apprehension. Zscaler Figure 2: Industries driving the largest proportions of AI transactions 5. AI-driven cyber risks are escalating: Threat actors are leveraging AI to enhance phishing campaigns, automated attacks, and create realistic deepfake content. ThreatLabz researchers demonstrated how DeepSeek can be manipulated to quickly generate phishing pages that mimic trusted brands. Additionally, ThreatLabz uncovered a malware campaign in which attackers created a fake AI platform to exploit interest in AI and trick victims into downloading malicious software. Securing AI and staving off AI threats with Zscaler The ThreatLabz 2025 AI Security Report provides detailed guidance for enterprises looking to securely adopt AI while minimizing risks and stopping AI-powered cyberthreats. Enterprises must rethink security strategies to account for new vulnerabilities, expanded attack surfaces, and AI-fueled cyberattacks. Traditional security approaches reliant on firewalls and VPNs are woefully insufficient against the speed and sophistication of AI-powered threats. Enterprises must adopt a zero trust approach, eliminating implicit trust, enforcing least-privilege access, and continuously verifying all AI interactions. Zscaler’s zero trust architecture delivers Zero Trust Everywhere—securing user, workload, and IoT/OT communications—infused with comprehensive AI capabilities. Its AI models detect and disrupt advanced threats, blocking millions of attacks daily to enhance enterprise security outcomes and mitigate emerging risks. The report details how to stop AI-powered threats with Zscaler, including: Zero trust architecture: Reduce the attack surface by hiding applications and IP addresses from attackers and enforcing least-privilege access. AI-powered cyberthreat protection: Detect and block AI-generated phishing campaigns, adversarial exploits, and AI-driven malware in real time. AI-powered data classification and DLP: Use AI-driven classification to detect and protect sensitive data across Zscaler’s Data Fabric. AI-powered app segmentation: Stop lateral movement within networks, ensuring attackers cannot easily escalate privileges or access critical systems. AI-powered breach prediction: Preempt potential breach scenarios using generative AI and multi-dimensional predictive models. Real-time AI insights: Employ predictive and generative AI for actionable insights that enhance security operations and digital performance. AI visibility: Get in-depth visibility into AI application trends and interactions through interactive dashboards. Get the report Download the ThreatLabz 2025 AI Security Report for additional data-driven insights and analysis of AI’s impact on cybersecurity, with expert guidance to help enterprises securely embrace AI and mitigate its risks. source

Expansion of project scope during the migration Weaknesses in project management  Underestimated testing and data migration phases   Revision loops for concepts and processes Decision-making  issues Failure due to misjudgments According to study director and Horváth partner Christian Daxböck, many problems during the transition are rooted in an incorrect program setup. The complexity of the project and the required resources are underestimated, while organizational competence is overestimated. “This mismatch leads to the enormous discrepancies between plan and results,” he says.   Another problem, according to Daxböck, involves prioritization: Too many goals are classified as equally important and should therefore, at best, be addressed simultaneously, which is ultimately also a consequence of inadequate project management.   source