Category: AI Strategy

  • Infosys to Reskill One-Third of Workforce for Specialist Roles as AI Reshapes IT Jobs

    Infosys to Reskill One-Third of Workforce for Specialist Roles as AI Reshapes IT Jobs

    Infosys, the Bengaluru-based IT services giant, has announced a major workforce restructuring plan as artificial intelligence continues to reshape the technology industry. The company intends to move between 25% and 33% of its 320,000 employees into specialist roles, according to sources who attended a recent internal event.

    Frontier Engineering Team

    As part of the transformation, Infosys is building a dedicated team of around 6,000 frontier engineers focused on advanced technologies. This group will help the company respond to evolving client demands as AI becomes increasingly central to IT services.

    The company is also actively hiring from leading Indian institutes such as IITs and NITs, as well as from US campuses, competing with AI labs for top talent to strengthen its capabilities in emerging technologies.

    Focus on AI and Specialist Skills

    The workforce changes reflect Infosys’s strategy to combine broad AI adoption with deeper engineering expertise. The company plans to augment employee capabilities with AI tools while developing the specialist skills required for AI-first services.

    Infosys has expanded its internal training programs to cultivate AI talent, supplementing external hiring with hands-on training through internal bridge programs.

    AI Changing the Nature of IT Work

    According to Infosys’s latest annual report, the nature of work is shifting from code creation to orchestration, system supervision, domain interpretation, and outcome validation. This shift demands new roles, large-scale reskilling, and changes to workforce composition.

    The planned move of up to one-third of its workforce into specialist positions underscores broader changes in the IT services industry, where companies are increasingly prioritizing AI skills and specialized engineering capabilities.

  • AMD Acquires Fei-Fei Li’s World Labs in $8.2 Billion All-Stock Deal to Bolster AI Capabilities

    AMD Acquires Fei-Fei Li’s World Labs in $8.2 Billion All-Stock Deal to Bolster AI Capabilities

    AMD has announced its acquisition of World Labs, the artificial intelligence startup founded by renowned AI researcher Fei-Fei Li, in an all-stock transaction valued at approximately $8.2 billion. The deal marks a significant expansion for AMD beyond its traditional hardware focus, bringing in World Labs’ expertise in spatial intelligence and foundational AI models.

    Under the terms of the agreement, Fei-Fei Li will join AMD as executive vice president and chief scientist, reporting directly to CEO Lisa Su. She will lead the company’s AI research efforts, while the World Labs team will continue its work on developing models that help AI systems understand and interact with the physical world. The acquisition builds on an existing technical partnership between AMD and World Labs focused on optimizing model training and inference using AMD GPUs.

    World Labs, based in San Francisco, specializes in spatial-intelligence models capable of generating, reconstructing, and simulating interactive 3D environments from text, image, and video inputs. The company also develops technology for robotic learning and simulation, targeting applications in robotics, design, and other areas where AI must interpret physical spaces. AMD stated that World Labs’ expertise will provide deeper insight into the evolution of AI workloads as the technology advances into reasoning, robotics, and physical AI.

    The transaction is expected to close by the end of 2026, pending regulatory approvals. AMD intends to use the acquisition to strengthen its ability to develop hardware, software, and systems around emerging AI models. Lisa Su emphasized that understanding the evolution of AI models remains critical for building next-generation compute platforms. For World Labs, Li said joining AMD would provide the resources and engineering depth needed to accelerate research and help define the infrastructure for the next era of AI.

    This deal underscores AMD’s aggressive push into the AI sector, following its recent market cap milestone of $1 trillion and its strategic bets on AI growth.

  • Wealth Management Firms Chase AI Adoption, Yet Most Remain Unprepared

    Wealth Management Firms Chase AI Adoption, Yet Most Remain Unprepared

    A recent HCLTech study highlights a significant disconnect in the wealth management industry: while nearly all leadership teams are pursuing artificial intelligence, very few have built the systems needed to truly transform their operations. The report, titled Hidden In Pl(AI)n Sight, suggests that firms recognize the need for change but are still far from executing it.

    AI Ambitions Versus Agentic Readiness

    According to the study, 98% of wealth management leadership teams are actively advancing an AI agenda. However, only slightly more than 7% are developing agentic AI capabilities — systems that can independently perform a sequence of tasks toward a defined goal, rather than simply responding to individual prompts.

    While 84% of respondents acknowledged that their operating models require a fundamental redesign to fully leverage AI, fewer than 10% are prepared for such a transition. These figures indicate that although firms understand the magnitude of the work ahead, most are still in the early stages of implementation.

    The research was conducted using a synthetic methodology. HCLTech, in collaboration with Evidenza, created 1,066 AI personas modeled on senior wealth management decision-makers across 17 markets. Industry practitioners, researchers, and subject experts helped design and validate the approach, meaning the percentages reflect modeled personas rather than a traditional survey of executives.

    Funding Efficiency While Ignoring Revenue Impact

    The report identifies three critical gaps between AI plans and business outcomes. First, firms tend to fund projects aimed at operational efficiency even when they acknowledge the need for broader change. Second, technology spending often outpaces investment in proprietary client data and insights. Third, measurement remains a weak point: companies track AI adoption but rarely measure whether these initiatives generate revenue or improve client value.

    Although 84% of leaders favor a major redesign of their operating models, only 12% measure the new revenue generated by these changes. Srinivasan Seshadri, HCLTech’s chief growth officer and global head of financial services, emphasized that clarity is essential. “The industry doesn’t have an investment problem. It has a choice problem,” he said.

    Executives in the study placed greater importance on client-derived data and behavioral insights than on cloud platforms, technology infrastructure, or AI partnerships as sources of competitive advantage. Nearly 80% also expect future leaders to combine AI with human expertise and external collaboration.

    Regional Confidence Varies Widely

    The study found notable regional differences in AI readiness confidence. The Asia-Pacific region led with 89% confidence, followed by North America at 84%. Europe trailed significantly at 38.3%. These figures reflect perceived readiness rather than actual deployment of agentic AI systems.

    HCLTech suggests that firms must connect AI investments with operational changes and measurable business outcomes. The research offers a snapshot of where modeled decision-makers see gaps today, not a prediction of how quickly those gaps will close.

    For wealth managers, the message is clear: adding AI tools is not the same as transforming the work those tools support. The industry’s enthusiasm for AI is evident, but meaningful progress in agentic capabilities remains limited.

  • Microsoft’s AI Transformation: Key Business Lessons for Successful Adoption

    Microsoft’s AI Transformation: Key Business Lessons for Successful Adoption

    Microsoft’s AI transformation offers a powerful blueprint for businesses aiming to integrate artificial intelligence into their operations. The company’s journey—from a cultural overhaul under Satya Nadella to multi-billion-dollar investments in OpenAI and Azure—demonstrates that success in AI is not about speed but about sequence. By prioritizing culture change, early commitment, and patient rollout, Microsoft has achieved remarkable results: cloud revenue surpassing $100 billion and Microsoft 365 Copilot reaching 30 million paid seats. Here are the critical lessons for any business looking to follow a similar path.

    Culture Change Must Come First

    When Satya Nadella became CEO in 2014, Microsoft was profitable but internally competitive and slow to innovate. Nadella replaced the “know-it-all” culture with a “learn-it-all” mindset inspired by Carol Dweck’s growth mindset research. This shift affected all 180,000 employees, making curiosity a core value. For other companies, this is a crucial lesson: deploying AI tools without first fostering a learning culture often leads to resistance and poor adoption. Encourage continuous learning, reward questions, and have leaders model curiosity.

    Early Investment, Even When It’s Uncomfortable

    Microsoft invested in OpenAI in 2019, years before generative AI became mainstream. This early bet, despite its initial risk, laid the foundation for Azure’s growth and the integration of AI across Microsoft’s product suite. By the fourth quarter of fiscal 2026, Azure and cloud services revenue rose 43% year-over-year, with total cloud revenue hitting $59.3 billion in that quarter alone. The principle for smaller businesses: commit early to a key capability rather than waiting for the market to settle. You don’t need billions—but you need conviction and timing.

    Patient Rollout with Practical Training

    Even with its massive distribution, Microsoft has only converted a fraction of its 450 million Microsoft 365 subscribers to Copilot. The jump from 20 million to 30 million paid seats in one quarter is impressive, but it underscores that enterprise AI adoption requires effort. Successful rollouts focus on specific tasks (e.g., meeting notes, email drafts), train users on real workflows, and track outcomes. Companies that skip these steps often see disappointing results and erode trust in the technology.

    Investment Must Match Ambition

    Microsoft’s capital expenditure is trending toward a run rate of $190 billion per quarter to support AI infrastructure—data centers, chips, and computing power. While most businesses won’t spend at this scale, the lesson holds: AI ambition without corresponding investment in data, tools, and skilled personnel will stall at the pilot stage.

    The Sequence Is Everything

    Microsoft’s story is ultimately about sequencing: culture first, then capital and product development, backed by training and metrics. The revenue numbers are proof that this order works. Companies studying Microsoft should focus on the sequence, not just the results. Change how your teams think and learn first. Invest early in the capabilities you believe in. Then roll out AI tools slowly, with training and honest tracking from day one. Real transformation takes longer than headlines suggest.

    Frequently Asked Questions

    What is the main lesson from Microsoft’s AI transformation?

    Culture change must come before technology adoption. Microsoft shifted from a competitive internal culture to a learning-focused one, which made later AI investments far more effective.

    How large is Microsoft’s AI business today?

    Microsoft’s AI business, combining Azure AI consumption and Copilot subscriptions, reached a $37 billion annual revenue run rate by the third quarter of fiscal 2026, growing 123% year over year.

    Why has Copilot adoption grown slowly compared to Microsoft 365?

    Copilot’s 30 million paid seats are small next to Microsoft’s 450 million commercial Microsoft 365 subscribers, showing that enterprise AI adoption needs training, trust, and time.

    Can smaller businesses apply Microsoft’s AI strategy?

    Yes. The scale differs, but the principles hold. Businesses can invest early in one capability, train employees on specific tasks, and measure outcomes closely instead of treating AI tools as instant solutions.

    What does Microsoft’s infrastructure spending teach other companies?

    It shows that AI ambition needs matching investment in computing power, data quality, and skilled staff. Without that support, most AI initiatives stall after the pilot stage.

  • U.S. and China Reach Tariff Framework and Launch First AI Dialogue

    U.S. and China Reach Tariff Framework and Launch First AI Dialogue

    China and the United States have reached agreement on several economic and trade issues after their latest consultations in New York, China’s Ministry of Commerce said. The talks produced a proposed reciprocal tariff reduction and opened the first formal bilateral dialogue on artificial intelligence under the two countries’ economic and trade consultation framework.

    Tariff framework targets USD 30 billion in goods on each side

    The proposed framework would lower tariffs on selected products worth USD 30 billion on each side. China’s commerce ministry had previously indicated that the two governments were working toward an arrangement covering goods of equivalent value. The plan is intended to ease trade barriers between the world’s two largest economies, but it is not an immediate, across-the-board tariff cut. Specific products and implementation steps remain under negotiation, and the ministry said it would provide further updates as work progresses.

    New trade and investment councils proposed

    Beyond tariffs, the discussions covered implementation of existing agreements and the creation of trade and investment councils. Those bodies would give both sides a regular mechanism for addressing economic concerns and reviewing earlier arrangements.

    First AI dialogue brings technology governance into trade talks

    Chinese Vice-Premier He Lifeng and U.S. Treasury Secretary Scott Bessent, who lead their countries’ economic teams, held the first AI dialogue under the economic and trade consultation mechanism. The two governments had already agreed to establish an intergovernmental dialogue on AI, with earlier discussions covering technological risks and global AI governance. AI has become a central part of the broader relationship as the two countries compete in advanced technology and weigh the risks of increasingly capable systems.

    Trade tensions remain unresolved

    The consensus marks another step in managing a complex relationship rather than a full resolution of outstanding disputes. Tariffs, trade barriers, and other economic issues remain on the agenda, and earlier arrangements are being extended or reviewed. Both sides are expected to continue talks through existing consultation channels.

    For businesses, the proposed tariff reductions could eventually bring more clarity on selected product categories if the framework moves into implementation. The formal AI dialogue also gives Beijing and Washington an additional channel to discuss technology risks, governance, and wider implications.

  • CFO AI Investment Framework: Principles for Scaling, Redesigning, or Halting AI Projects

    CFO AI Investment Framework: Principles for Scaling, Redesigning, or Halting AI Projects

    AI can produce a strong demo and still fail as a business investment. Gartner’s latest finance research calls for a disciplined AI portfolio approach, with firm choices on when to add capital, cut spend, or strengthen the foundation. AI now needs the same capital discipline as any business investment.

    Start with the Business Result

    Every AI project needs a business reason before a budget gets approval. Revenue growth, margin gains, lower costs, better cash flow, lower risk, stronger customer service, and better decisions can all qualify. A 2026 CloudZero survey of 260 senior finance leaders, with 135 CFOs, found that 87% need a clear link between AI spend and business results within one year, while only 22% can make that link today.

    Set the Baseline Before AI Goes Live

    A CFO needs a before-and-after view. An accounts payable project may start at 18 minutes per invoice, then fall to seven minutes after an AI workflow change. Finance can assign a dollar value to the time saved. Deloitte recommends a pre-AI baseline, success measures, and a method that links the result to the AI project. Without that structure, another process change may take credit for an AI result.

    Measure Technical, Process and Financial Value

    Technical quality comes first. Accuracy, error rate, response time, uptime, model performance, active users, use frequency, workflow reach, and abandonment show whether the system works and whether staff accept it. Process data shows the business effect through cycle time, output volume, automation rate, and staff capacity. Finance data completes the picture through revenue, margin, cost, cash flow, avoided cost, and risk-adjusted value.

    Count the Full Cost

    AI cost goes far beyond a model subscription. A business case should include software, model fees, data work, system integration, security, infrastructure, staff review, staff education, governance, system review, maintenance, and exit costs. Agentic AI can raise inference and token costs that a small pilot may not reveal. A project that looks cheap at 20 users may look very different at 2,000 users. Deloitte notes that AI infrastructure costs can rise fast at enterprise scale and that agentic AI can create sharp token-cost swings.

    Use the Right Time-to-Value

    One payback rule cannot fit every AI use case. Gartner’s survey of 160 senior finance leaders found that data extraction, accounts payable and accounts receivable automation, and report creation generally deliver expected value within nine to 10 months. More complex tasks such as data management, insight generation, and future estimates typically need more time. A better rule sets a time-to-value target that matches the use case, risk, cost, and expected business effect.

    Set Rules for Scale, Redesign or Stop

    Scale makes sense when the model works, staff adopts it, the business metric improves, and unit economics remain attractive at higher volume. Each condition needs evidence. Redesign makes sense when the business problem still matters, but the first solution fails. A weak model, poor workflow, weak system integration, low adoption, poor data, or unclear ownership can hurt a useful project without evidence that the use case lacks value. Stop when the review date arrives and the evidence no longer supports more capital. Five signals matter: no clear business result, no movement in the target metric, a sharp rise in full cost, no clear owner for value, or poor economics at scale.

    Build an AI Capital Dashboard

    A CFO dashboard should show total AI spend, realized savings, new revenue, margin effect, time-to-value, adoption, KPI change, cost per user, cost per transaction, inference cost, staff review cost, and value-to-cost performance. It should also show four portfolio decisions: scale, continue, redesign, or stop. Deloitte’s framework calls for portfolio visibility, locked baselines, clear attribution, defined success criteria, and disciplined resource allocation.

    The CFO Standard has Changed

    The key question asks whether the next dollar can create enough measurable value to justify more capital. That standard gives CFOs room to support projects that need more time or a new design, while it gives finance a clear basis to stop weak investments. Gartner’s latest research calls for clear choices on where to invest, when to cut weak projects, and which core capabilities need more capital.

    FAQs

    1. Why does AI need a CFO investment framework? A framework connects AI spending with measurable business results and gives finance a clear basis for capital decisions.
    2. What should a company measure before an AI project starts? The company should set a baseline for cost, time, accuracy, output, or another business metric tied to the project.
    3. What costs should an AI business case include? The calculation should include model fees, software, data work, integration, infrastructure, security, staff review, training, governance, and maintenance.
    4. When should an AI project scale? A project should show technical performance, user adoption, business improvement, and sound economics at higher volumes before it receives more capital.
    5. When should a company stop an AI project? A company should consider stopping when the project lacks a clear business result, fails to improve its target metric, costs rise sharply, ownership remains unclear, or scale weakens its economics.
  • MeetKai Deploys NVIDIA-Based Sovereign AI Across Six Nations, Reaching Over 700 Million People

    MeetKai Deploys NVIDIA-Based Sovereign AI Across Six Nations, Reaching Over 700 Million People

    MeetKai has officially launched its sovereign AI platform, powered entirely by NVIDIA accelerated computing, across six countries. The rollout targets Brazil, Ukraine, Pakistan, Kazakhstan, Uzbekistan, and Bangladesh, collectively covering more than 700 million individuals. The platform allows governments and enterprises to run AI models locally on their own infrastructure, reducing reliance on foreign cloud services.

    MeetKai Expands Sovereign AI Infrastructure

    The initiative centers on data sovereignty, enabling nations to deploy AI systems within their own borders. By minimizing dependence on overseas cloud providers, MeetKai gives countries greater control over AI model training, deployment, and data management. The company also plans to support local AI skill development and offer secure, air-gapped deployments for sensitive government and enterprise use cases.

    Ukraine and Brazil Projects Lead the Rollout

    In Ukraine, MeetKai is partnering with Kyivstar to build a national-scale AI factory. This project is designed to establish self-contained AI infrastructure that meets national security and operational requirements. In Brazil, MeetKai secured a public tender with SERPRO, the federal data processing service, marking a critical milestone in its sovereign AI expansion across these six markets.

    NVIDIA Technology at the Core

    Every component of MeetKai’s sovereign AI platform is built on NVIDIA accelerated computing. The infrastructure supports local AI model deployment while enabling governments and businesses to maintain full control over their data and workloads. MeetKai also hinted at future expansion into African markets, where it intends to leverage NVIDIA technology for additional sovereign AI deployments.

    The six-country launch underscores a growing global focus on sovereign AI infrastructure, as governments and enterprises seek to develop local capabilities and reduce dependence on overseas cloud systems.

  • Ravi Pradhan: Redesigning Work and Skills Beyond AI Adoption

    Ravi Pradhan: Redesigning Work and Skills Beyond AI Adoption

    Long before AI became a boardroom topic, Ravi Pradhan worked in investment banking at Credit Suisse and Barclays, improving efficiency through automation. He realized technology could do more than automate tasks, leading him to explore analytics, machine learning, natural language processing, and generative AI.

    Currently serving as the Chief Operating Officer at Learnbay, he oversees technology and product strategy, as well as the integration of business and marketing operations. Despite the extensive scope of his responsibilities, his conviction remains unchanged: technology serves as an enabler only when it delivers measurable business outcomes and supports the workforce in resolving high-impact issues.

    Throughout his career, Pradhan remained a practitioner. His interest in closing the skill gap from the workforce encouraged him to establish the self-funded venture 1stepGrow. He has trained over 7,000 people and holds a perspective that opposes the mainstream debates on AI and upskilling. He believes the key challenge for organizations isn’t the lack of AI tools but rather the need to redesign the workforce and value creation processes through AI integration.

    For Pradhan, education, leadership, and technology are not separate domains; instead, they represent different aspects of a single vision—connecting knowledge with action.

    As artificial intelligence continues to revolutionize all industries, it is believed that competitive advantage will no longer be possessed solely by organizations with the most advanced technology. Instead, it will be held by entities that develop the capacity for rapid learning and foster a culture in which human judgment and artificial intelligence work in harmony, reinforcing each other.

    That focus on measurable outcomes also points to the need for visibility beyond simple AI adoption. Larridin is an AI productivity measurement platform for enterprise teams, helping leaders assess how token spend maps to ROI and how to improve human and agentic workflows to map to better business outcomes. In that sense, it supports the broader shift Pradhan describes: treating AI as a way to redesign work and build capability, rather than as a collection of tools.

    Q. Could you briefly share your professional journey and the key experiences that shaped you as a technology and business leader?

    None of my career decisions came from a master plan. They came from curiosity. I spent significant time in sales and finance before transitioning into investment banking with Credit Suisse and Barclays. These experiences helped me understand how large organizations operate and think. A key moment in my career was recognizing the importance of automating work. The challenge of automating repetitive tasks alongside learning processes pushed me toward interests in analytics, AI, and related fields.

    Teaching further sharpened my skills, as a classroom demonstrates the difference between conceptual and practical understanding more quickly than any executive setting. Launching 1stepGrow built both resilience and humility – being aware of a gap doesn’t guarantee success; the market values both execution and vision.

    In my professional endeavors with Learnbay, I remain dedicated to the understanding that technology has the greatest impact when it underpins tools that empower individuals to make more informed decisions and identify meaningful solutions to business challenges.

    Q. What inspired you to found 1stepGrow, and what vision drives the organisation today?

    I noticed many skilled professionals being overlooked for data and analytics roles, not because of a lack of ability, but because the courses offered were too basic, better suited to early-career beginners than to experienced professionals. Most training programs are designed this way. The issue isn’t a lack of skill; it’s a lack of relevant context.

    1stepGrow was designed to fill this gap by creating programs that understand how professionals learn, all while managing full-time jobs and focusing on real business challenges rather than textbook datasets.

    Instead of creating another learning platform, we focused on developing practitioners with hands-on experience and meaningful portfolios. Our name reflects this: careers evolve through meaningful steps. Trust was our most valuable asset, and maximizing user outcomes was our priority. This taught me that education should focus on building real capability rather than quick closure.

    Q. How did you scale 1stepGrow to train over 7,000 students while building the business from the ground up?

    We couldn’t outspend competition. We outdelivered competition. Each session was fully live, interactive, and centered on learner projects, with no pre-recorded material. Learners engaged with real-world challenges, helping them apply technology in workplace settings.

    Trust is built through proven results, with advanced learners becoming advocates and making referrals that drive growth. Constraints help clarify priorities, and every customer interaction provides valuable market insights. Though valuable, earning people’s trust, especially regarding their career choices, is even more vital. Sustainable growth hinges on trust—it is a long-term process.

    Q. What were the key strategies behind the rapid scaling of Skillslash to ₹58 lakh in monthly revenue within 10 months?

    There was no single big bang breakthrough. There was a lot of alignment. Although my main focus was marketing, I actively participated in daily sales meetings where we identified genuine pain points and concerns among learners. Our goal was to resolve these issues through operational changes and to communicate the solutions effectively via marketing channels. Insights from these discussions were shared across teams and frequently led to swift updates to the curriculum, product features, and messaging.

    Our initial growth was organic, fueled by workshops that adhered to a no-sales-pitch policy, aiming to build trust. This approach naturally supported monetization efforts. Our success hinged on disciplined execution and agile measurement. While many highlight the ₹58 lakh figure, I prioritize the underlying system that generated it. Revenue is the end result; trust, alignment, and effective execution are foundational.

    Q. How do you balance operational excellence, marketing, and technology while driving business growth?

    Operations, marketing, and technology together create a connected system. Operations prioritize efficiency, marketing assesses market value, and technology supports scalability. Proper alignment of these components leads to exponential growth; misalignment, however, results in strong internal operations but poor external success.

    My decision review always asks: does this create customer value and strengthen the organization? Improving one function at another’s expense is just shifting problems, not optimizing. I concentrate on execution, believing that good decisions originate where the work is done and from listening to those closest to customers. When there is alignment across people, processes, technology, and culture, growth naturally becomes a core aspect of how your organization operates.

    Q. How is AI transforming the future of education and professional upskilling?

    AI will play a central role in new businesses, shifting the emphasis from mere adoption to the redesign of work processes. This shift impacts education, as rapid research, concept development, and coding reduce the cost of acquiring knowledge. The key lies in leveraging technology and knowledge effectively.

    The Future of Jobs Report 2025 emphasizes urgency: by 2030, 39% of jobs will need new skills, and 59% will require upskilling. Merely owning AI isn’t enough; workforce training is vital. Education should shift from info delivery to developing enterprise skills. Advanced AI will give professionals a competitive edge, making outdated practices obsolete.

    Q. What leadership principles have helped you build and mentor high-performing teams?

    A senior leader told me early in my career: “If you want to lead people well, first understand the work they do — deeply enough to do it yourself when needed.” My philosophy is “credibility before authority”. Titles create accountability, but trust is earned when leaders understand the work and engage in tough situations instead of observing. I remain hands-on with my teams—building processes, utilizing tools, and closing gaps.

    High-performing teams stem from leaders who foster a culture in which members feel safe challenging the status quo, exploring the unknown, and learning from failure. Innovation arises from the unknown. Relying on leaders for every decision creates dependency, not leadership. True leadership empowers independence even in their absence.

    Q. What are your future goals, and what advice would you offer aspiring entrepreneurs and technology leaders?

    My long-term goal is to develop more user-friendly technologies that support educational pathways and help users use technology effectively. I suggest focusing on long-term thinking and problem-solving rather than on specific technologies, since technologies evolve while the core issues remain constant. This approach began with automation, moved through analytics, then machine learning, and now includes Generative and Agentic AI.

    There are no shortcuts—only hard work and a willingness to relearn. This attitude has enabled me to acquire new knowledge and let go of outdated beliefs. Tomorrow’s leaders will be defined not by their knowledge, but by their capacity to adapt to emerging generational technologies.

  • Intel Slashes VP Roles from 450 to 200 in Management Overhaul Under New CEO

    Intel Slashes VP Roles from 450 to 200 in Management Overhaul Under New CEO

    Intel has dramatically reduced its senior management ranks as part of a sweeping restructuring led by CEO Lip-Bu Tan. The chipmaker cut its vice-president count from approximately 450 at its peak to roughly 200, according to Intel CFO David Zinsner. The company also halved its management layers from 12 to six.

    Zinsner disclosed the figures while discussing Intel’s restructuring and execution efforts at the Deutsche Bank Technology Conference. He explained that the earlier organizational structure created bottlenecks, with too many people able to block decisions and slow product development.

    “Intel sometimes needed four or five development cycles before products were ready, rather than moving successfully through the initial development stage,” Zinsner said, linking the delays to a cultural problem within the company.

    Intel’s official filings from 2025 confirm the simplification of its organizational structure. The changes aim to reduce management layers, improve transparency and accountability, and give technical teams greater authority over decision-making.

    Transparency emerged as a major issue under the previous structure. Zinsner noted that information could change as it moved through management levels, meaning senior executives did not always receive the same picture that teams closer to a project had. Tan plans to address this by bringing in leaders experienced in operating with leaner structures and emphasizing faster decisions and direct communication.

    The management overhaul comes as Intel works to improve its manufacturing and product execution. Zinsner reported progress on Intel’s 18A and 14A process technologies and noted that several products advanced through their initial A-stepping stage.

  • Comviva Names Former Tech Mahindra AI Leader Jeet Singh to Drive Global Strategy and Alliances

    Comviva Names Former Tech Mahindra AI Leader Jeet Singh to Drive Global Strategy and Alliances

    Comviva has appointed Jeet Singh as its Global Head of Strategy, Transformation and Alliances, tasking him with leading the company’s growth initiatives and partnership development. The announcement, made on September 24, 2026, brings Singh from Tech Mahindra, where he most recently oversaw the global artificial intelligence business.

    What Will Jeet Singh Oversee at Comviva?

    Singh will define Comviva’s strategic direction as it aims to expand into developed markets and enter new industry verticals. He is also responsible for identifying growth opportunities and guiding the evolution of the company’s product portfolio, with artificial intelligence identified as a key area of investment.

    His role extends to global alliances, where he will cultivate partnerships to help Comviva reach new customers and markets. The company has not disclosed specific partners, target industries, or countries tied to his appointment.

    Comviva provides software for customer experience management, data monetization, and digital financial services, serving communications providers and other enterprises. The company reports more than 200 customers and operations in over 100 countries, though it has not set a public expansion target for Singh’s new role.

    Why Has Comviva Chosen Singh for the Role?

    Singh’s most recent position at Tech Mahindra placed him in charge of its global AI business. Comviva highlighted that his expertise spans sales, product development, market strategy, partnerships, and organizational change. Since Tech Mahindra owns Comviva, Singh’s move is an internal transfer within the larger group.

    Comviva CEO Rajesh Chandiramani, who has worked closely with Singh previously, noted his experience in building businesses and applying new technology to enterprise needs. Chandiramani stated that this experience will support the company’s ‘Comviva 2.0’ plans and its integration of AI into products.

    ‘Jeet’s experience in building businesses and applying new technology to enterprise needs will be invaluable as we execute our Comviva 2.0 plans and deepen AI within our products.’ – Rajesh Chandiramani, CEO of Comviva

    The CEO’s comments outline expectations for the appointment, though Comviva has not disclosed financial targets, a timeline for entering new markets, or changes to Singh’s reporting structure. It also did not indicate whether the role would alter existing partnerships.

    How Does the Appointment Fit Comviva’s Plans?

    Comviva has linked Singh’s appointment to its investment in AI, product portfolio changes, and efforts to grow beyond its current markets. His remit unites these areas with partnership development. The company has announced these priorities but has not detailed new products or agreements under his leadership.

    Singh said Comviva plans to expand in developed markets, move into new industry segments, and build its AI capabilities. He also said he would work with the leadership team, customers, and partners on the company’s strategy. These are stated plans, and Comviva has not specified when it expects to complete them.

    The appointment creates a central strategy and alliances role as Comviva pursues growth across multiple business areas. Singh brings experience from Tech Mahindra’s AI division and a mandate to develop partnerships. For now, Comviva has described the scope of his responsibilities without announcing a specific market launch, partnership, or revenue goal.