Category: AI Strategy

  • How Women Leaders Are Driving Inclusive and Ethical AI Innovation

    How Women Leaders Are Driving Inclusive and Ethical AI Innovation

    As generative AI moves from experimentation into everyday business use, the real question is not how companies adopt this technology—it is who gets to shape it. Women leaders are playing an increasingly critical role in AI development, bringing diverse perspectives, responsible decision-making, and inclusive innovation to the forefront. Their involvement is helping organizations create AI systems that are more balanced, transparent, and aligned with real-world needs.

    The Gender Gap in AI Adoption

    Women use generative AI tools at a noticeably lower rate than men, with recent research showing a gap of 16 to 25 percentage points that persists across countries and industries. This gap matters because AI’s future should reflect everyone’s judgment, not just a narrow set of voices. Limited training, underrepresentation in design, and real barriers to shaping these tools all contribute to the disparity.

    Caution as a Strength, Not a Weakness

    Lower adoption rates among women are often characterized as hesitation or a confidence gap, but closer examination suggests something else: caution toward a powerful and still-evolving technology can reflect sound judgment. Women consistently report greater concern than men about AI’s data security, privacy, and trustworthiness. Research shows women are roughly twice as likely to anticipate a negative personal impact from AI over the next two decades. This skepticism is justified by documented performance issues in AI systems where women’s outcomes are concerned.

    Independent consultant Raquel Roses, author of The Visual Guide to AI, describes choosing to form her own opinion before consulting an AI tool and finding the tool’s answer flat and generic. Senior advisor Caroline Creven Fourrier calls the habit of feeding a question to AI and accepting the answer without scrutiny “cognitive outsourcing.” Questioning outputs, rather than accepting them at face value, is a leadership capability organizations will need to cultivate, not diminish.

    When Bias Gets Automated

    AI systems reflect the data and teams behind them. When inputs lack range, bias follows: recruitment tools trained on historical hiring data have shown documented discrimination against female applicants and candidates from minority backgrounds. A widely used government dataset for facial recognition included 75% men and under 5% women of color. Roughly 42% of global companies already use predictive AI in recruitment, despite repeated evidence of discriminatory outcomes.

    Groups least represented in today’s data are the most likely to be overlooked in tomorrow’s algorithms. Organizations hold considerable leverage: collective expenditure on AI tools reaches billions annually, granting buyers substantial purchasing power to demand transparency from vendors about training data and team diversity.

    Building AI Skill as a Leadership Skill

    AI capability should be recognized alongside strategy and finance as a core leadership competency, not a technical add-on. This requires deliberate attention to who receives access to AI training and tools, who is included in pilot projects, and who participates in governance discussions. Surveys consistently identify insufficient training as the foremost barrier, followed by unclear governance, ethical concerns, and limited psychological safety around experimentation.

    The encouraging finding is that modest interventions can produce meaningful results. Research from Google indicates that a few hours of targeted training can significantly increase women’s AI adoption, especially when combined with peer mentoring and visible role models.

    Closing the Gap

    The World Economic Forum estimates that gender equity remains 123 years away on current trends. AI is already reshaping labor markets, and women face disproportionate exposure to roles most vulnerable to automation. Closing this gap requires coordinated effort across governments, technology firms, businesses, media, and academic institutions to work toward safe and accountable AI systems, stronger institutional trust, and greater representation of women in AI leadership and design roles. With trillions of dollars projected in global AI infrastructure investment, the opportunity to establish these norms exists now.

    The question confronting every organization is not whether AI will transform leadership—it already has. It is whether that transformation extends to everyone, or leaves a key portion of the workforce excluded from its benefits.

  • How CEOs Can Master Strategic Planning in the Age of AI

    How CEOs Can Master Strategic Planning in the Age of AI

    Corporate strategy has rarely stayed still for long, and the current business cycle belongs to artificial intelligence. AI is reshaping how budgets are allocated, how boards weigh risk, and how leadership teams justify their decisions. Few CEOs today can separate strategic planning from questions about AI governance and readiness.

    What makes this moment unique is the mismatch between spending and sentiment among top executives. CEO confidence in revenue prospects has slipped to its lowest point in five years, as leaders navigate uneven AI returns alongside geopolitical and cyber risks. Companies keep raising AI budgets even as the executives approving them grow noticeably more cautious about near-term payoff.

    Confidence Gaps Are Widening

    Strategic planning now stretches well beyond quarterly revenue targets and competitive positioning. It includes technology governance, workforce readiness, and infrastructure decisions inside one connected roadmap. Only three in ten CEOs report confidence in revenue growth over the coming year, down sharply from 38% last year. Playbooks built during calmer economic periods often fail to hold up against today’s faster, less predictable market swings.

    Boards and CEOs Remain Misaligned

    A global survey of 625 CEOs and board members found that 35% of CEOs feel their boards overestimate what AI can genuinely replace. Friction between leadership teams and governance layers routinely stalls decisions that competitors are already acting on. Board composition has quietly become part of this planning conversation. Directors without deep technical grounding sometimes push for faster AI rollouts, underestimating what operational readiness truly demands.

    Accountability Has Become Central

    Seventy-eight percent of executives admit they lack confidence in passing an independent AI governance audit within 90 days. A strategy missing clear accountability invites lasting reputational damage long before regulators get involved.

    How CEOs Can Build AI-Driven Business Strategies

    Spending Alone Does Not Guarantee Results

    A resilient AI strategy demands far more than rolling out new software across every department. Leaders need to connect data quality, talent planning, and measurable outcomes from the very first planning session. More than 80% of multinational leaders expect higher AI spending this year, and nearly 90% are raising budgets for emerging agentic capabilities. Bigger budgets rarely produce clarity on their own, especially without a framework guiding where that money actually goes.

    Depth of Adoption Outperforms Speed

    Firms scaling AI across several business functions consistently pull ahead of companies testing one isolated pilot at a time. Deployment leaders scaling AI across at least two categories redesign workflows more often—49% versus 32% among firms still piloting. The lesson here has little to do with how quickly a company adopts new tools. It has everything to do with whether existing workflows get rebuilt around what those tools can actually deliver.

    Best Strategic Planning Techniques for CEOs

    A handful of practices consistently separate CEOs building lasting AI strategies from those chasing short-lived headlines. Consider the following priorities before committing further budget toward any large-scale rollout:

    • Treat AI governance as a board-level responsibility rather than a technical afterthought.
    • Concentrate budgets on high-return use cases instead of broad, unfocused rollouts.
    • Pair every major technology investment with structured workforce upskilling programs.
    • Set measurable accountability checkpoints before scaling any AI initiative further.
    • Revisit strategic assumptions each quarter, since conditions now shift faster than before.

    Governance and Growth Have to Move Together

    Heavy AI investment without matching governance tends to produce strategies that crumble under scrutiny. CEOs pairing genuine ambition with structured accountability end up with plans that survive both market pressure and boardroom skepticism.

    Final Words

    Strategic planning in the AI era rewards CEOs who balance genuine ambition with disciplined execution rather than reactive spending. Confidence has declined across many boardrooms, yet companies pairing investment with strong governance keep outperforming hesitant competitors. The path forward favors leaders willing to treat AI as a core strategic function, not a scattered collection of experiments. CEOs committing to measurable accountability today are quietly building the foundation for steady, durable growth ahead.

  • Why 80% of AI Pilots Fail: Ashish Chandra on Moving from Experiments to Enterprise Impact

    Why 80% of AI Pilots Fail: Ashish Chandra on Moving from Experiments to Enterprise Impact

    Artificial intelligence is reshaping the way enterprises operate, moving beyond predictive analytics to systems capable of autonomous decision-making and intelligent execution. Even with increased investment in AI infrastructure, many companies struggle to make pilot initiatives drive enterprise-wide change because of disjointed data, isolated systems, poor governance, and a lack of measurable business goals.

    In the latest episode of the Analytics Insight Podcast, Ashish Chandra, a global AI thought leader, shares his perspective on how organizations can transition from AI experimentation to enterprise-scale execution. He talks about reasons why most AI projects don’t succeed in generating value, the need for good-quality data and governance, ways that companies should stay away from ‘AI Theatre,’ the skills needed to be ready for AI, and what’s ahead for AI in the business world during the next ten years. Here are the excerpts:

    Why do Many Organizations Fail to Scale AI Beyond Pilot Projects?

    In my opinion, the major problem is that firms perceive AI as a technological effort rather than a business transformation. Trying out new things is not difficult, but transforming the entire business involves data, governance, ownership, operating models, and business impacts. At present, most firms possess hundreds of proofs of concept but only a few production-ready AI products. Those organizations that succeed will be those that use AI as part of their everyday business activities. Only through this can the value of AI be realized.

    How Can Enterprises Build AI as Core Business Capability Instead of Another Technology Layer?

    I believe AI should become part of the enterprise architecture rather than sitting on top of existing systems as another application. Organizations need to create an AI fabric that connects enterprise data, business processes, knowledge layers, memory systems, human expertise, and decision intelligence. Instead of deploying hundreds of disconnected AI assistants, businesses should establish a unified intelligence layer that continuously learns from operations. The future will be intelligence-centric rather than application-centric. Just as cloud computing transformed enterprise infrastructure over the past decade, AI will become the underlying intelligence powering every critical business function.

    Why are Data Quality and Governance Critical to Successful Enterprise AI?

    As I have said before, data and context should be considered the bedrock of enterprise AI. Bad data leads to bad intelligence, no matter how intelligent the model is. Enterprises should invest in data foundations, metadata management, semantics, security, compliance, explainability, and governance. Equally critical is developing infrastructure purpose-built for AI workloads rather than relying on existing enterprise infrastructure. In the years ahead, enterprises won’t have any more AI; they’re going to have more trustworthy AI. Trust will be the key differentiator as companies rely on AI systems that can produce transparent and explainable results.

    How Should Organizations Measure Return on Investment from AI Initiatives?

    I think the assessment of the success of an AI solution has to be much wider than a conventional financial ROI approach. There are four areas where I measure the AI value. First, it is a financial one, involving such metrics as increased revenue, optimized costs, higher margins, and automation benefits. The second one is operational and involves faster cycle times, increased efficiency, and lower error rates. Third, it is the area of intelligence that relates to parameters such as improved decision-making quality, higher accuracy of forecasting and predictions, and better reusability of knowledge. Finally, there is a strategic aspect that includes innovation, customer experience, competitiveness, and agility.

    What does Future of Enterprise AI Look Like Over Next Decade?

    This is going to be a time when companies will go beyond systems of record and into systems of reasoning. Not only will businesses stop digitizing themselves, but they will also start thinking, predicting, optimizing, and adapting continuously. There will be self-governing business capabilities, AI-driven business models, enterprise memory, agentic workforces, self-optimizing supply chains, digital twins, and executive decision intelligence all in real-time in this new age. Human beings will have to play a greater role in judgment, governance, creativity, and strategy. Companies that embrace the intelligent transformation will not only be more efficient but will also create the future of competitive advantage.

    Listen to the full conversation on the Analytics Insight Podcast for deeper insights into building enterprise AI that delivers measurable business impact.

  • Big Tech’s Hidden AI Debt Surpasses $1.65 Trillion, Report Reveals

    Big Tech’s Hidden AI Debt Surpasses $1.65 Trillion, Report Reveals

    A new report by Nikkei Asia reveals that the world’s largest technology companies—Microsoft, Alphabet, Amazon, Meta, and Oracle—have collectively taken on approximately $1.65 trillion in off-balance-sheet debt tied to artificial intelligence investments. This “invisible debt” figure exceeds their combined official liabilities, highlighting the scale of their commitments to AI infrastructure.

    The hidden debt stems from future payment obligations such as long-term operating leases for data centers not yet built, bulk GPU supply contracts, and joint ventures. These tech giants are not required to record these as liabilities on their balance sheets until the assets are delivered or the facilities become operational.

    Meta and Oracle carry the largest hidden debts. Meta’s off-balance-sheet debt is estimated at $420 billion—three times its official debt—while Oracle’s stands at approximately $273.3 billion. The remaining three companies collectively hold around $1.45 trillion in off-balance-sheet obligations for cloud services and other business commitments as of March 2026.

    What Does ‘Off-Book’ Spending Mean?

    The term ‘off-book’ spending refers to agreements where a company commits to future payments that are not listed on its regular debt sheet. For example, long-term rentals for data centers, AI chips, and cloud services allow Big Tech companies to secure critical resources before demand escalates further.

    Why These Commitments Matter

    These future payments could become a financial burden if the AI market slows. Companies would still be obligated to pay for contracts they have signed, even if their AI business underperforms. Agencies like Morgan Stanley and Moody’s have warned about these rising debt commitments. While AI creates new opportunities, it also pushes companies into massive financial commitments that could shape their future growth.

  • 7 Executive Strategies for Dominating the AI Economy

    7 Executive Strategies for Dominating the AI Economy

    The AI economy is reshaping competitive dynamics, cost structures, and customer expectations simultaneously. According to McKinsey’s 2026 State of AI report, 78% of organizations now use AI in at least one business function, up from 55% the year before — a pace of adoption that means the gap between leaders and laggards is widening faster than most boards anticipated. For CXOs, the question is no longer whether to adopt AI but how to adopt it in ways that create durable advantage rather than temporary efficiency gains.

    Treat AI as a Strategic Priority, Not an IT Project

    The most frequent pitfall in executive oversight is to delegate AI adoption to the technology department and measure success by operational outcomes only. Leading organizations are those where the CEO has set clear AI goals linked to revenue and holds the entire C-Suite accountable. According to Gartner’s 2026 CIO research, companies whose CEO sponsors AI programs are 2.5 times more likely to have business impact from them. In this era, AI literacy is not negotiable for non-tech executives: an incompetent CFO unable to critically assess an AI investment case or a CMO who cannot judge the attribution model of an AI marketing campaign is at a considerable disadvantage.

    Build AI into the Core, Not the Edge

    Many organizations in 2025 ran AI pilots at the edges of the business: isolated proofs-of-concept in one department, a chatbot on customer service, a forecasting tool in one market. These generated learnings but rarely competitive advantage, because they were never connected to the core operating model. Winning in the AI economy requires integrating AI into the processes that directly determine how value is created: the customer experience, the supply chain, the product development cycle, and the sales motion. Companies that have integrated AI into their core operations report 40% higher productivity gains compared to those running isolated experiments, according to BCG’s 2026 AI adoption analysis.

    Rethink the Workforce, Don’t Just Retrain It

    AI will displace some jobs, radically alter many others, and bring into being job categories that do not currently exist. Managers who view this as a training issue invariably underestimate the magnitude of the change needed. The better way to think about this challenge is through workforce redesign — figuring out how to map activities into areas managed by AI, those requiring AI-human interaction, and those requiring uniquely human input, and then redesigning job categories, teams, and incentives accordingly. Companies that redesign their workforces with AI rather than augmenting pre-existing designs with AI realize far greater benefits.

    Govern AI Before It Governs You

    AI adoption without governance is a weakness rather than a strength. Failures show that the costs incurred in terms of reputation and regulation can often outweigh the efficiency benefits which motivated the adoption of AI technology in the first place. Leading organizations have established AI governance frameworks with clear accountability, mandatory human oversight for high-stakes outputs, regular bias auditing, and documented correction processes. This is not a compliance exercise. It is risk management and increasingly, a customer trust signal.

    Win on Data, Not Just on Models

    The commoditization of foundation models means model access alone is no longer a competitive differentiator. The durable advantage in the AI economy sits in proprietary data. Organizations that have invested in data quality, infrastructure, and governance for years can fine-tune models on their own customer behavior and operational history in ways competitors without that data asset cannot easily replicate. For executives building AI strategy, data strategy and AI strategy are the same document, and the executives who understand that distinction earliest will be the ones best positioned to lead in the AI economy over the decade ahead.

    Why This Matters

    Organizations that strategically integrate AI into their business models are more likely to improve productivity, accelerate innovation, and respond quickly to changing market conditions. Executive leadership, not technology alone, will determine which companies gain lasting competitive advantage.

    FAQs

    What is the AI economy?

    The AI economy refers to the growing business environment where artificial intelligence drives productivity, innovation, customer experiences, and competitive advantage across industries. Organizations increasingly use AI to automate workflows, improve decision-making, and create new products and services.

    Why should CEOs treat AI as a business strategy instead of an IT project?

    AI influences every business function, including finance, marketing, operations, and customer service. When executive leadership aligns AI initiatives with revenue growth and strategic objectives, organizations are more likely to achieve measurable business outcomes than when AI remains solely a technology initiative.

    How can organizations successfully integrate AI into core business operations?

    Companies should embed AI into value-generating processes such as customer service, supply chain management, sales forecasting, product development, and decision support. Integrating AI into daily operations delivers greater long-term benefits than isolated pilot projects.

    What is AI governance, and why is it important?

    AI governance establishes policies, oversight mechanisms, and accountability for AI systems. It includes bias testing, human review, regulatory compliance, data privacy protection, and continuous monitoring to reduce legal, ethical, and reputational risks.

    Why is data strategy essential for AI success?

    High-quality proprietary data enables organizations to train and fine-tune AI systems for better business outcomes. Since AI models are becoming more widely accessible, unique data assets increasingly provide the strongest competitive advantage.

  • Google Employees Mobilize: 4,500+ Sign Petition to CEO Sundar Pichai Over AI-Driven Job Security Fears

    Google Employees Mobilize: 4,500+ Sign Petition to CEO Sundar Pichai Over AI-Driven Job Security Fears

    More than 4,500 Google employees have formally petitioned CEO Sundar Pichai, urging the company to implement stronger job protections as it ramps up investment in artificial intelligence. The petition, delivered by members of the Alphabet Workers Union, represents one of the largest employee-led movements in the company’s recent history.

    The workers’ concerns center on the growing disconnect between Google’s aggressive AI expansion and the ongoing layoffs affecting staff worldwide. The petition demands concrete measures, including guaranteed severance packages, voluntary buyout options before any compulsory layoffs, and reforms to Google’s performance review system.

    Alphabet Workers Union President Parul Koul criticized the company’s priorities, stating, "These layoffs and cuts are not difficult decisions, but simply profit being put over the people that make this company run." Koul accused Google of valuing shareholder returns above employee welfare, despite the company’s rising share price.

    Google has not announced any new layoffs directly linked to the petition. In previous statements, the company has maintained that AI is designed to enhance worker productivity, not replace human roles. However, the petition underscores persistent anxiety among employees as Google deepens its AI commitments.

    The movement gained further visibility when approximately 100 employees gathered outside Google’s campus to show solidarity. The petition has been delivered to Pichai’s office, signaling a clear demand for leadership to address job security in the age of AI.

  • From GenAI to GenOps: Shashank Sharma on Scaling Enterprise AI with Intelligent Automation

    From GenAI to GenOps: Shashank Sharma on Scaling Enterprise AI with Intelligent Automation

    Podcast Highlights: Inside GenOps with Shashank Sharma

    As enterprises move beyond initial Generative AI experiments, the focus shifts to scaling AI efforts while maintaining governance, compliance, efficiency, and business impact. In a recent episode of the Analytics Insight podcast, host Priya Dialani spoke with Shashank Sharma, Global Head of Intelligent Operations, AI and Automation at Brandtech Plus, about the evolution from Generative AI to GenOps. The conversation covered AI-RPA integration, intelligent business operations, human oversight, enterprise governance, automation strategies, and maximizing ROI through responsible AI scaling.

    Key Takeaways from the Interview

    Generative AI vs. GenOps

    Sharma explained that while Generative AI primarily focuses on creating content—such as text, images, and videos—GenOps applies AI to business operations. GenOps integrates AI with governance, automation, compliance, and operations to increase efficiency, reduce costs, and ensure AI functions effectively within enterprises.

    Why AI and RPA Are Stronger Together

    AI provides intelligence and reasoning, whereas Robotic Process Automation (RPA) handles rule-based tasks consistently. By combining both, organizations can automate complex business processes, minimize errors, secure information assets, and balance the probabilistic nature of AI with the deterministic execution of RPA.

    “AI provides intelligence and reasoning, whereas RPA handles rule-based tasks consistently. By combining both, organizations can automate complex business processes, minimize errors, secure their information assets, and balance the probabilistic aspects of AI with the deterministic aspects of RPA execution.”

    Business Benefits of AI and Automation

    Organizations can expect productivity gains, faster process execution, reduced costs, enhanced compliance, improved data accuracy, and higher returns on investment. Automation frees employees from routine tasks, allowing them to focus on more strategic functions.

    Why Human Oversight Remains Essential

    AI can make smart decisions, but it can also make wrong or dangerous ones. Human intervention is necessary to confirm important decisions, govern, prevent unnecessary errors, ensure accountability, and keep business transactions safe until AI technology becomes fully reliable.

    “AI can make smart decisions, but it can also make wrong or dangerous ones. Human intervention is required to confirm important decisions, govern, prevent unnecessary errors, hold people accountable, and keep business transactions safe until AI technology becomes reliable.”

    Challenges in Scaling AI

    Sharma identified budget constraints, staff resistance, poor-quality data, and lack of clarity about organizational processes as major obstacles. He recommended focusing on the most significant AI use case, experimenting, engaging subject-matter experts, and improving data quality to overcome these issues.

    Conclusion

    The shift from Generative AI to GenOps represents a maturation of enterprise AI. By integrating intelligent automation with robust governance and human oversight, companies can scale their AI initiatives responsibly while achieving tangible business outcomes. As Sharma’s insights demonstrate, the future of enterprise AI lies in balancing innovation with operational discipline.

  • From Pilot to Profit: How to Make AI Commercially Viable at Scale

    From Pilot to Profit: How to Make AI Commercially Viable at Scale

    Over the past few years, enterprises have become proficient at launching AI pilots. However, the real challenge—and the key to lasting value—lies in scaling those pilots into reliable, commercially viable capabilities. Technical success in a test environment tells us very little about an initiative’s readiness for production. To bridge that gap, organizations must focus on five critical areas: business case alignment, cost transparency, governance, ownership, and workforce upskilling.

    1. Anchor AI Initiatives in Business Metrics

    One of the most common mistakes is delaying the business case until after a pilot shows promise. Instead, establish the economic logic at the outset. Every use case should tie directly to a metric the organization already values—such as turnaround time, conversion rate, productivity, service quality, or risk reduction. Early-stage experimentation will involve uncertainty, but there should always be a clear hypothesis about where value will come from and what must be true for the investment to make sense at scale. Additionally, prioritize business functions where end-users have a higher propensity to adopt AI, increasing the likelihood of successful scaling.

    2. Understand the Full Cost of Production

    During a pilot, costs like model access or platform fees are visible. But production deployments bring a much broader cost base: data cleaning and maintenance, system integration, security testing, ongoing monitoring, human review, and workflow changes. These costs are manageable when anticipated early. The right question is not simply how much the technology costs, but whether the value created continues to justify the total cost of operation as usage expands. Avoid scaling first and asking about economics later.

    3. Establish Clear Governance and Leadership Direction

    Governance, risk, and compliance concerns are major scaling barriers—50% of respondents in a recent Deloitte study cited them as a primary obstacle. Organizations often swing between rushing ahead without adequate controls or delaying useful initiatives until every data and governance issue is solved. Leadership must assess readiness in the context of each use case. The level of oversight should reflect the consequences of error. Security, compliance, evaluation, and human-in-the-loop processes should be designed in from the start, not bolted on after a successful pilot is waiting for approval.

    4. Define Clear Ownership Across Teams

    AI initiatives commonly begin in innovation, technology, or transformation teams—appropriate during exploration. But difficulty arises when ownership must move beyond that group. Once AI becomes part of a business process, the benefiting function should be accountable for outcomes. At the same time, technology teams own architecture and reliability, risk teams need visibility into controls, and finance must understand the economics. Successful scaling happens when these groups are involved early enough to make decisions together, rather than being brought in sequentially as approval gates.

    5. Invest in Contextual AI Upskilling

    A major barrier to AI adoption and scaling is the lack of a common understanding of AI among employees. Generic online courses often fail to connect to an individual’s day-to-day work in their specific industry, domain, and organization. Enterprises should invest in customized, curated learning and development programs that help employees appreciate how AI applies to their roles. This leads to better adoption and more effective scaling of AI use cases.

    Concluding Note

    As enterprise AI programs mature, success will be defined not by the volume of initiatives but by the value they deliver. Organizations must become more disciplined about prioritizing use cases that demonstrate clear business impact, operational readiness, and sustainable economics. The companies that get this right will not necessarily have the longest list of AI initiatives—but they will consistently turn pilots into trusted, scalable solutions. Ultimately, the true measure of AI maturity is the ability to create lasting business value long after the initial excitement has faded.

    Authored by Raghu A, Partner, Deloitte India

  • Wipro Lowers Utilization to Build Capacity, Maintains AI Hiring Focus Despite Soft Demand

    Wipro Lowers Utilization to Build Capacity, Maintains AI Hiring Focus Despite Soft Demand

    Wipro has deliberately reduced employee utilization to create capacity for upcoming client engagements, even as the company continues to hire in close alignment with business demand. The strategy is aimed at ensuring the IT services firm has enough workforce ready to execute deals already in the pipeline.

    Speaking after the company’s June quarter results, Chief Financial Officer Aparna Iyer said the lower utilization was a planned move rather than a sign of weaker execution.

    “If you look at utilization year on year, we have decreased our utilization to invest in some of the deals that we’re looking at,” Iyer said. “That will also go up through the year. That will also be a lever.”

    Margins Depend on AI Investments

    Iyer said utilization would remain one of the company’s key margin levers over the coming quarters, alongside productivity gains from fixed-price projects, pyramid optimization, and tighter control over administrative expenses.

    She noted that Wipro’s operating margin has improved steadily over the past eight quarters, rising from nearly 15% to the 17-17.5% range. However, she said sustaining or improving those levels would depend on how much the company invests in artificial intelligence capabilities.

    Hiring to Remain Demand-Driven

    Chief Human Resources Officer Saurabh Govil said Wipro is not planning aggressive hiring despite a modest increase in headcount during the June quarter. Recruitment will continue to mirror client demand, while productivity improvements are expected to offset the need for significant workforce expansion.

    “Our overall headcount and hiring will be linked more to the demand environment, which is right now a bit muted,” Govil said. “But I don’t see a headcount going up drastically. I see that the gains of productivity, which we’re talking about, will have a play as we move forward.”

    Govil added that the company is balancing investments in upskilling existing employees with the hiring of specialized AI talent, which remains expensive.

    “Some of the AI skills are rare, so they are coming at a premium. So all forces are in play. We’ll have to invest, grow, and make sure we maintain our margins.”

    Headcount Rises, Bookings Remain Strong

    Wipro added 888 employees during the June quarter, taking its total workforce to 2.43 lakh from 2.42 lakh in the previous quarter. Attrition edged up slightly to 13.9% from 13.8%.

    The company reported a 0.9% year-on-year increase in quarterly revenue, while operating margins declined by 120 basis points due to a client-specific deal and continued investments in AI. Wipro also secured $3.3 billion in bookings during the quarter, including 13 large deals valued at $1.6 billion.

  • Databricks Reaches $188 Billion Valuation With New Strategic Funding for Enterprise AI

    Databricks Reaches $188 Billion Valuation With New Strategic Funding for Enterprise AI

    Data and AI company Databricks has announced a new strategic funding round that values the company at $188 billion, signaling strong investor confidence in enterprise artificial intelligence. The financing, led by existing investor Coatue with participation from other new and existing investors, is expected to close later this summer.

    The fresh capital will be used to strengthen Databricks’ AI portfolio, expand research efforts, and support future acquisitions as the company meets rising enterprise demand for AI-powered business solutions.

    Focus on AI Products

    Databricks said the funding will accelerate development across three key offerings: Unity AI Gateway, Genie, and Lakebase.

    Unity AI Gateway enables enterprises to manage AI models from multiple providers while improving governance, security, and cost control. Genie acts as an AI-powered coworker that helps employees turn business data into actionable insights. Lakebase is a serverless PostgreSQL database designed to support AI agents and modern enterprise applications.

    According to the company, these products aim to address what it describes as the ‘context gap’ in enterprise AI, where disconnected data systems, governance challenges, and rising infrastructure costs prevent organizations from deploying AI effectively at scale.

    Enterprise AI Strategy

    Commenting on the funding, Databricks Co-founder and CEO Ali Ghodsi said enterprises are increasingly prioritizing measurable business value over indiscriminate AI spending.

    ‘Enterprises are moving from tokenmaxxing to valuemaxxing. They don’t want to burn expensive tokens on the smartest model for every task, they want the best outcome per dollar. That means having the freedom to choose the right AI for the job,’ Ghodsi said.

    He added that the new investment will help the company strengthen its multi-AI strategy by enhancing Unity AI Gateway, expanding Genie, and advancing Lakebase.

    Growing Enterprise Adoption

    Headquartered in San Francisco, Databricks serves more than 20,000 organizations worldwide, including Adidas, AT&T, Bayer, Block, Mastercard, Rivian, and Unilever. The company says around 70% of Fortune 500 companies use its Data + AI Platform to build AI applications, analytics tools, and autonomous AI agents.

    The latest funding further strengthens Databricks’ position in the rapidly expanding enterprise AI market as businesses continue to invest in scalable, governed, and cost-efficient AI infrastructure.