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Forbes
4 days ago
- Business
- Forbes
Measuring AI's Impact And Value: 20 Essential Factors To Consider
getty As AI systems become more embedded in core business functions, traditional metrics like precision and recall capture only part of the picture. Measuring ROI now requires a holistic lens—one that accounts for AI's impact on workflows, decision-making speed and long-term adaptability. Whether a business is assessing its internal AI tools or the AI-powered features included in its products, relying solely on technical benchmarks can result in missing or misinterpreting the broader value—or potential risk—AI systems introduce. Below, members of Forbes Technology Council highlight key factors worth considering when assessing AI success and ROI, explaining why each one offers a more complete view of performance. 1. Hours Reclaimed A practical metric I use to measure AI's ROI is hours reclaimed. I recently rebuilt our GTM messaging across three segments—what previously took 20 hours to do manually, I completed in two, and then in 45 minutes using AI. That time saved is measurable, repeatable and directly tied to productivity gains, reduced errors and faster execution across teams. - Farrukh Mahboob, PackageX 2. Decision Latency Reduction Decision latency reduction is a powerful AI success metric. It measures how quickly AI enables smart, confident decisions, compressing the time between insight and action. Unlike cost savings, this reflects real strategic agility. When decisions speed up, it shows AI is truly embedded in how the business moves. - Jason Missildine, Intentional Intensity Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify? 3. CO2 Usage A metric recently brought into the measurement equation is CO2 usage. Along with tracking more traditional efficiency metrics that showcase faster or cheaper results thanks to an AI system, calculating how much energy it uses provides an offset figure that can be incorporated into evaluations and influence longer-term strategy. - Mark Thirlwell, BSI Group 4. Ethical Outcomes One powerful metric is how well AI systems translate human values into safe, bias-free outcomes that benefit society and stakeholders. More than delivering correct answers, AI systems need to model responsible behaviors, which in turn leads to growth, innovation and a better customer experience. - Vishal Talwar, Wipro Ltd. 5. Contextual Adaptation Quotient Contextual adaptation quotient is a powerful new metric that measures how well AI systems sustain performance across varying domains, users or conditions without retraining. Unlike static accuracy scores, CAQ captures real-world adaptability, highlighting robustness, transferability and long-term ROI in dynamic environments. - Nikhil Jain, SmartThings, Inc. 6. 'Trust Delta' One insightful metric is the 'trust delta,' or how much more (or less) people trust your system after you add AI. You can measure this through user feedback and behavior changes. The smartest AI is useless if people won't use it. If your AI makes people second-guess themselves or feel uneasy, it's actually slowing them down. The trust delta shows whether you're building something people want to work with or work around. - Kehinde Fawumi, Amazon 7. Time To Confidence A genuinely insightful ROI metric for AI systems is time to confidence—how quickly a user reaches a decision they trust. In high-stakes fields like investing, speed alone isn't enough; decisions must also be defensible. - Mike Conover, Brightwave 8. Innovation Rate In my view, the innovation rate metric stands out above all. This tracks the number of new products, services or process improvements directly enabled by AI-driven insights. While ROI focuses on optimizing the present, this metric reveals how effectively AI is building a company's future. A high innovation rate proves AI is not just a cost center, but a strategic engine for growth and market leadership. - Mohan Mannava, Texas Health 9. Autonomy-To-Intervention Ratio A cutting-edge metric is the autonomy-to-intervention ratio, which tracks how long an AI system can operate before needing human correction. It moves beyond traditional KPIs like precision to reveal trust, scalability and operational ROI in real terms. A high AIR means AI isn't just working; it's learning, adapting and truly offloading cognitive burden. - Nicola Sfondrini, PWC 10. Time To Insight Reduction One emerging and insightful metric is time to insight reduction, which is how much more quickly actionable intelligence is derived from data. It reflects the AI system's real-world impact on decision velocity, efficiency and responsiveness, making it a powerful indicator of true ROI beyond cost savings or accuracy alone. - Hrushikesh Deshmukh, Fannie Mae 11. Decision Outcome Improvement The true measure of AI isn't just technical performance, but its real-world impact. Decision outcome improvement quantifies the tangible uplift in valuable results achieved when AI influences a decision, versus the baseline without it. This metric is crucial because it cuts through tech specs to show the practical, profitable difference AI makes, revealing its true ROI where it matters most. - Raghu Para, Ford Motor Company 12. Revenue Per AI Decision Revenue per AI decision is a metric that I find myself looking at quite often. It shows how well an AI system drives actual business outcomes. At our company, if an AI model suggests a payment plan and it closes faster or with higher value, that's measurable success. It ties AI performance directly to bottom-line impact, which matters more than model accuracy or usage stats alone. - Ashish Srimal, Ratio 13. Time To Value Realization One insightful metric is time to value realization, which measures how quickly a company can start deriving business value from an AI implementation. A shorter TTVR indicates efficient deployment, effective user adoption and that the AI is solving a real problem quickly, directly correlating to faster benefits and competitive advantage. - Ambika Saklani Bhardwaj, Walmart Inc. 14. Adaptive Learning Rate One unique metric for measuring AI success is adaptive learning rate, which helps quantify the speed at which an AI system can learn from new data. For instance, in audio processing, a high ALR means an AI can quickly adapt to new accents or background noises, continuously improving without constant retraining. This shows an AI's true long-term value, beyond initial deployment. - Harshal Shah 15. Autonomous Resolution Rate A powerful new metric is autonomous resolution rate, which is the percentage of tasks completed end-to-end by AI agents without human intervention. In ERP/CRM, ARR reflects true ROI by measuring how effectively AI agents handle processes like order creation, invoice matching or case resolution. High ARR signals reduced operational costs, improved efficiency and successful agent adoption at scale. - Giridhar Raj Singh Chowhan, Microsoft 16. Model Utilization Rate One enlightening measure is the model utilization rate—the percentage of the output of an AI model that gets used for decision-making or operations. It's instructive because accuracy is of no consequence if the truths are not acted on. It's a measure of real-world application and trust in AI that demonstrates the relevance and value it has in business. - Saket Chaudhari, TriNet Inc. 17. Feature Abandonment Recovery Feature abandonment recovery is the percentage of users who return to an AI feature after experiencing initial frustration. Most metrics show first-touch success, but this shows resilience. If users give your AI a second chance after it fails them, you've built something valuable. It indicates your AI provides enough value that users forgive mistakes—the ultimate sign of product-market fit. - Marc Fischer, Dogtown Media LLC 18. Resource Efficiency Index The resource efficiency index measures how well AI saves time, effort and resources by reducing manual work and enhancing productivity. Unlike traditional ROI, REI captures indirect benefits such as innovation and agility, providing a holistic view of AI's impact on workforce efficiency and strategic value in modern business operations. - Dileep Rai, Hachette Book Group 19. Access Management Data Access management data provides powerful, real-time metrics that analyze the impact and adoption of technologies and digital systems, such as those using AI. This data offers actionable insights into how tools are being used and their effect on productivity. By mapping usage trends to business outcomes, organizations can identify gaps, optimize training and prove ROI. - Fran Rosch, Imprivata 20. Return On Disruption One novel metric is return on disruption, which measures how AI redefines workflows or business models, not just cost or revenue gains. ROD captures transformative impact, signaling true innovation and long-term competitive advantage rather than incremental efficiency. - Lori Schafer, Digital Wave Technology
Forbes
5 days ago
- Business
- Forbes
20 Hurdles For Healthcare Tech Startups In Scaling Solutions
A healthcare startup may launch with a bold and innovative idea, but turning that idea into a scalable solution that works across hospitals and complex health systems is rarely straightforward. From integrating with legacy infrastructure to navigating strict compliance requirements and diverse stakeholder priorities, even the most agile teams can struggle to scale effectively. Left unaddressed, these challenges can stall adoption, drain internal resources and limit a product's long-term impact. Below, members of Forbes Technology Council highlight some of the most common hurdles healthcare startups must be ready for and share their expertise on breaking into and succeeding in this challenging sector. 1. Finding Client Champions To scale solutions within a health system or payer organization, you need to engage a team of internal champions who can understand, justify and prioritize your platform. Organizations evaluate dozens if not hundreds of companies each year. Champions help articulate your value, often leveraging their professional credibility to advocate for it. Finding and 'winning' them is essential to scaling. - Graham Gardner, Kyruus Health 2. Navigating Integration Requirements Across Hospital Systems One of the biggest hurdles is navigating the complexities of integration requirements across different hospital systems. Healthcare startups frequently underestimate the time and resources needed for integrations. Success requires building flexible APIs from day one and having dedicated integration specialists who understand healthcare IT infrastructure, not just general software development. - Ted Kail, Cority Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify? 3. Gaining Traction In A Risk-Averse Environment Most healthcare organizations are risk-averse and don't want to be early adopters. They look for proven, well-established companies and products, making it difficult for healthcare startups to get traction, even when they have clearly better solutions. Partnering and delivering real value to that first set of clients is critical in scaling early on. - John Bou, Modio Health 4. Integrating With Insurance Systems Health insurance companies amplify the interoperability challenge by adding another layer of complex, often siloed, data and systems that healthcare tech startups must integrate with. This makes the negotiation and implementation of business associate and HIPAA agreements more complicated, given the data types, security requirements and shared liabilities that arise from integrating with both providers and payers. - Ajai Paul, Affirm Inc. 5. Understanding The Complex Stakeholder Ecosystem Healthcare startups often make the mistake of viewing the U.S. healthcare system with a 'singular' point of view. It is an integrated ecosystem where each stakeholder is affected by the others, which means multiple interests must be aligned when adopting new technologies. - Raghav Ramabadran, Intelligine Technologies 6. Building Custom Integrations For Each Customer Healthcare startups' challenges include integrating with electronic health record systems, which is not a 'plug and play' process. Each hospital or system has its own highly customized version of an EHR, with unique workflows, data fields and security protocols. This lack of standardization means startups must build a new integration for nearly every customer. - Chris Ciabarra, Athena Security Inc. 7. Developing Strong Governance From The Outset Healthcare startups often wait to build full product depth until after landing their first client, but healthcare's high-risk, structured environment demands strong governance from day one. Change control, release management and a deep understanding of current operations, especially when replacing legacy systems, are essential before customizing. Building depth late risks delays and failure! - Trisha Swift, Mula Integrative Health & Wellness 8. Maintaining HIPAA Compliance With Digital Content One challenge healthcare startups face when scaling tech is managing digital content while staying HIPAA-compliant. Hospitals need more than stock photos and shared drives—they expect secure, role-based access to branded visuals that convey authenticity and protect patient privacy. Without a digital asset management strategy, startups risk falling short on compliance, credibility and growth. - Andrew Fingerman, PhotoShelter 9. Ensuring Consistent Performance And Compliance Across Disparate Systems Healthcare startups often struggle to scale because hospital environments vary widely in terms of infrastructure, workflows and data systems. Without a unified data architecture, real-time metrics, and built-in security and governance, it's hard to ensure consistent performance—or meet privacy requirements like HIPAA and business associate agreements governing protected health information. - Dave Albano, Diliko 10. Working Within Complex Pricing And Claims Rules One of the challenges is the integration of new tech into strict hospital billing and compliance processes. Hospitals have complex pricing and claims rules, and startups must work within these rules. They can't disrupt revenue or patient data safety. Doing this right builds trust and helps a solution scale faster. - Abhishek Sinha, Accenture 11. Processing Both Structured And Unstructured Health Data Integrating structured data (EHRs; lab results) and unstructured data (clinical notes; imaging; video) can be a major challenge. Healthcare startups must ensure their tech can process both, all while adapting to varying data and privacy standards across systems, which further complicates scaling and interoperability. Fortunately, generative AI is making this easier to do. - David Talby, John Snow Labs 12. Balancing Accuracy And Transparency With Scalability The healthcare and life sciences sector faces rigorous accuracy and transparency requirements that cannot be sacrificed and must be built into products from the start. Balancing this with scalability—which is really code for 'solving problems you don't have yet'—is a constant challenge—especially for startups, which often place a key focus on agility and speed. - Martin Snyder, Certara 13. Maintaining A Consistent, Accurate Record Of Core Assets One key challenge healthcare startups face when scaling tech solutions across systems is the inability to maintain a consistent and accurate record of core assets—such as patients, providers and devices—due to the absence of a robust master data management strategy. This causes data fragmentation, which in turn hinders decision-making, innovation and seamless integration across platforms. - Somnath Banerjee 14. Keeping Up With A Range Of Regional Norms And Laws Key challenges include a wide range of compliance requirements, regulations, cultural norms, and data privacy and region-specific laws—making a one-size-fits-all solution impractical, even within a single organization. Startups often rely on business rules engines that lack user friendliness. Agentic AI offers a more adaptable and intuitive alternative. - Koushik Sundar, Citibank 15. Working Within Legacy Hospital Systems One major challenge healthcare startups face when scaling tech solutions is integration with legacy hospital systems. Many hospitals rely on outdated EHRs or siloed IT infrastructure, making interoperability difficult. Startups must ensure compliance, data security and seamless integration to gain trust and adoption at scale. - Srikanth Bellamkonda 16. Clearly Demonstrating ROI And Pathways To Reimbursement Healthcare startups often struggle to clearly demonstrate a return on investment and secure reimbursement pathways. Without established billing codes or tangible cost-savings data, hospitals hesitate to allocate budget. Startups must invest heavily in economic validation, health economics and outcomes research, ensuring payers and finance teams see sustainable revenue models before adoption. - Manav Kapoor, Amazon 17. Creating An Internal COE Establishing an internal center of excellence with deep industry experience in scaling healthcare systems is vital, but costly. A key challenge lies in selecting vendors that align with the company's DNA. Bridging the gap between emerging tech and the unique demands of healthcare requires thoughtful planning and a nuanced understanding of both innovation and patient-centric outcomes. - Hari Sonnenahalli, NTT Data Business Solutions 18. Overcoming Resistance To New Tech I've regularly observed the challenges clinical sites face when adopting new technologies. There is often reluctance or resistance to change; staffing shortages further exacerbate these issues. A more effective approach may be to 'mirror' site-level data. This would allow AI-driven platforms to build a harmonized system that enables forward progress without disrupting existing workflows. - Rachel Tam, Bristol Myers Squibb 19. Accounting For Integration Issues When Building Solutions The biggest hurdle to overcome when scaling tech solutions across hospitals or healthcare systems is not technical; rather, it is integration—into provider workflows, clinical practice guidelines, financial models and revenue cycle management programs. Unless the issues around integration are considered and covered when building the solution, scaling will not occur. The landscape is littered with misaligned HealthTech startups. - Mark Francis, Electronic Caregiver 20. Completing Vendor Risk Documentation Post-ransomware, hospitals demand extensive vendor risk audits with hundreds of security controls, SOC 2/HITRUST docs, and custom BAAs. Completing these lengthy questionnaires stretches sales cycles to 18 to 24 months, burning cash and pulling engineers from product work to compliance, blocking scale. - Mohit Menghnani, Twilio
Forbes
5 days ago
- Business
- Forbes
Tech Retirement Crisis: Vulnerable Industries And What They Must Do
As seasoned tech professionals with decades of institutional knowledge approach retirement, many industries risk losing the expertise required to maintain aging systems and tools. These legacy technologies often power critical operations but are unfamiliar to younger tech professionals, creating a widening skills gap. The retirement of veteran tech experts is a wake-up call for industries still tethered to outdated systems—the challenge isn't just replacing those systems, but also preserving the knowledge that keeps them running. Below, members of Forbes Technology Council highlight the industries most at risk and share what leaders can do now to transfer essential know-how and prepare the next generation to step in with confidence. 1. Ports Ports still rely on terminal operating systems (TOS) and EDI standards from the 1980s. Young engineers face a steep learning curve with cryptic interfaces and minimal documentation. To future-proof, leaders must digitize maritime workflows using AI-integrated APIs while creating simulation labs for cross-training younger talent on legacy systems—before the sea of knowledge ebbs away. - Jagadish Gokavarapu, Wissen Infotech 2. Financial Services Financial services is one industry that relies on legacy systems younger tech professionals can't maintain. To ensure continuity as technology leaders retire, organizations must urgently modernize by migrating to the cloud, unlocking AI and attracting top talent for future growth. - Chetan Mathur, Next Pathway Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify? 3. Automotive; Heavy Machinery Industries like automotive and heavy machinery rely on proprietary software and specialized control systems. As experienced engineers retire, there is a risk of knowledge gaps emerging. Leaders should implement mentorship programs in which older professionals share their expertise with their younger counterparts. Investing in intuitive training materials can also help bridge the generational divide. - Neel Sendas, Amazon 4. OT And Manufacturing Operational technology and manufacturing often run on legacy operating systems and hardware and rarely incorporate AI into critical workflows. While these sectors require deterministic outcomes, pairing AI models with strict guardrails—such as limiting outputs to a predefined set of approved results—can still boost efficiency and innovation without introducing undue risk to sensitive environments. - Keren Katz, Tenable 5. Corporate Banking Corporate banking risks falling behind as legacy systems outlast the talent trained to maintain them. To stay competitive, banks must modernize with interoperable cloud, SaaS and AI solutions; ensure structured knowledge transfer between generations; and foster a culture of innovation to future-proof operations, enhance compliance and meet rising customer expectations. - Alex Ford, Encompass Corporation 6. Professional Services Many professional services firms still rely on outdated tools—spreadsheets, siloed systems and tribal knowledge held by a few. Younger talent isn't trained on or drawn to these ways of working. As experts retire, firms risk losing both continuity and edge. Leaders must modernize workflows and capture knowledge in connected, accessible systems to future-proof their teams and retain their advantage. - Sarah Edwards, Kantata 7. Energy The energy sector still leans heavily on legacy SCADA systems and custom PLC setups that younger professionals rarely encounter. As veteran engineers retire, leaders should invest in cross-generational training programs and gradually modernize infrastructure—bridging knowledge gaps before they become operational risks. - Kirill Sagitov, COYTX GLOBAL LLC 9. Healthcare The healthcare industry continues to rely on legacy technology, such as EHR systems built with platforms like MUMPS, a language that is now unfamiliar to most new professionals. As experienced staff retire, younger pros lack the skills to maintain them. Leaders should prioritize mentorship, detailed documentation and phased modernization to ensure continuity and prepare for a tech-forward future. - Tannu Jiwnani, Microsoft 10. Aerospace The aerospace industry still relies on legacy systems, such as Fortran-based simulations and outdated telemetry tools. As seasoned experts retire, leaders must document key knowledge, modernize tools and build mentorship pipelines. Just as NASA blends Apollo-era wisdom with Artemis-era innovation, bridging generations ensures mission continuity and tech evolution without losing institutional memory. - Shelli Brunswick, SB Global LLC 11. Retail Retail is a prime example of an industry still dependent on legacy systems, siloed data and the deep expertise of long-tenured employees. As these experts retire, leaders should accelerate the shift to unified commerce platforms, modernize tools for frontline staff, and leverage AI for both onboarding and customer engagement to future-proof their operations. - Zornitza Stefanova, BSPK 12. Global Logistics Global logistics runs on fragile legacy EDI pipes, which younger pros rarely touch. Instead of patching, leaders should build a blockchain-backed trade graph. Pair retiring EDI experts with AI agents to translate workflows, compress the past and future-proof the industry. - Akhilesh Sharma, A3Logics Inc. 13. Utilities The utilities industry still runs on SCADA and COBOL—tools young tech talent rarely encounters. Leaders must build simulation labs for training, capture tacit knowledge digitally and layer modern APIs over core systems to ensure continuity before the grid's human backbone retires. - Mark Mahle, NetActuate, Inc. 14. Broadcast Media The broadcast media industry still runs on decades-old video playout servers, SDI hardware and proprietary codecs. As tech veterans retire, leaders must digitize tribal knowledge, modernize with IP-based workflows and train new talent through virtual production labs—before the signal fades on this critical yet aging infrastructure. - Roman Vinogradov, Improvado 15. Life Insurance The life insurance industry uses policy administration systems that need to be maintained for the lifetime of the insured (which is usually up to 40 or 50 years). The older books of business need to be migrated to modern systems to mitigate the risk of a loss of support due to retiring professionals. - Arnab Mukhopadhyay, VNS Health 16. Government Agencies Too many government agencies run COBOL systems, managing payments on half-century-old infrastructure. We can prioritize 'bridge' roles, pairing retiring experts with younger staff to transfer knowledge of both technical systems and the legal frameworks behind them. Leaders must modernize while preserving privacy protections through gradual adoption of new technologies. - Nick Hart, Data Foundation 17. Airlines The airline industry is deeply reliant on legacy reservation and operations systems built in languages like Fortran and TPF, which are unfamiliar to modern developers. Leaders should launch dual-track modernization: Pair retiring experts with junior engineers on active systems while building cloud-native replacements. Preserve tribal knowledge now or risk turbulence tomorrow. - Sandipan Biswas
Forbes
6 days ago
- Business
- Forbes
How Hackers Use AI Today—And How To Stay Safe
As artificial intelligence advances, so do the tactics of malicious actors. Hackers are now using AI to scale attacks, exploit vulnerabilities more quickly and create deceptive content that's nearly undetectable with traditional defenses. From deepfakes and synthetic identities to AI-generated malware and real-time phishing schemes, the threat landscape is evolving fast. Below, members of Forbes Technology Council share new ways hackers are weaponizing AI, along with practical strategies for defending against these risks. 1. Targeting Real-Time Payments Fraudsters are using AI to create sophisticated fake communications and synthetic identities that target real-time payments at an unprecedented scale; 75% of financial institutions admit bad actors leverage AI more effectively than they do, exposing vulnerabilities. There's no silver bullet, but organizations must leverage AI better across the full customer lifecycle, not just for identity verification. - Yinglian Xie, DataVisor 2. Committing Large-Scale, Humanlike Fraud Hackers now use agentic AI to create fake accounts and commit fraud with humanlike precision at a massive scale. These AI agents mimic real user behavior, bypassing traditional defenses. To stay protected, organizations must move beyond CAPTCHAs and invest in advanced detection systems that analyze behavior, device intelligence and interaction patterns in real time. - Dan Pinto, Fingerprint Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify? 3. Analyzing Code For Vulnerabilities Hackers are weaponizing AI to rapidly analyze large volumes of code and uncover vulnerabilities—often before they're even detected. By shifting security left and right, organizations can operationalize security continuously throughout the software development lifecycle to prevent weak or misconfigured security controls, closing gaps from code to deployment that make them vulnerable to AI-powered adversaries. - Brittany Greenfield, Wabbi 4. Infiltrating Hiring Processes Hackers use AI to deploy deepfakes that impersonate job candidates during virtual interviews and hiring assessments, allowing them to bypass traditional identity checks and gain insider access. To defend against this, organizations should adopt biometric authentication and certified identity verification, ensuring both incorporate liveness detection and presentation attack defenses. - Michael Engle, 1Kosmos 5. Building Custom Malware Threat actors are increasingly using unregulated black market LLMs to create malware that can bypass traditional defenses. Once inside, they move laterally to attack. Instant detection of unusual network activity is critical to stop them. Security teams should develop a baseline of normal behavior and continuously monitor their networks to identify anomalies for rapid investigation and remediation. - Rob Greer, ExtraHop 6. Rapidly Exploiting Security Flaws With AI, the time needed to exploit security flaws (vulnerabilities) is drastically reduced from months to days—perhaps, in the near future, even minutes. We already see machines (AI agents) automatically researching, developing and exploiting machines in the wild. What organizations can do is to adopt advanced security controls that can remediate or mitigate threats in a faster, more efficient way. - Roi Cohen, Vicarius 7. Supercharging Attacks AI has become a performance enhancer for threat actors, helping them execute stronger malware attacks, more realistic phishing scams and sophisticated social engineering rackets. The best defense against these AI-enhanced offensives is to embrace zero trust, in which policies and controls are made to contain threats moving at machine speed by limiting lateral movement and enforcing least privilege. - Thyaga Vasudevan, Skyhigh Security 8. Using Deepfakes For Extortion And Deception Hackers are using AI to create deepfakes to extort execs and voice signatures to trick employees. Companies need to leverage multiple policies and processes, including zero-trust principles, multifactor authentication, scalable monitoring tools and continuous employee education and awareness to guard against expanding AI threats. - Rob Green, Insight Enterprises 9. Finding Logic Flaws In Custom Apps And APIs Hackers are using AI to find logic flaws in custom apps and APIs. AI models predict weak points by simulating inputs and analyzing code, uncovering exploits faster than manual scans. Defend with AI-powered code review tools, runtime anomaly detection and red team simulations. For consumers: Reduce app connections and use smart identity monitoring. - Saby Waraich, Clackamas Community College 10. Creating Constantly Morphing Malware Hackers are using AI to create malware that constantly changes to avoid detection, making old antivirus tools useless. To fight back, companies need AI-powered security that watches behavior instead of just known threats. It's about catching suspicious actions in real time and assuming nothing is safe by default—because AI-driven attacks move fast. - Haider Ali, WebFoundr 11. Launching Next-Gen Botnet Attacks Hackers are using AI to quickly develop new botnet propagation and control mechanisms to create bigger, more versatile botnets. An example is the Aisuru botnet, which has been in the news for launching record-breaking distributed denial-of-service attacks. As these new botnets emerge, organizations that have internet-facing apps should reevaluate their DDoS defenses to ensure they are evolving along with the threat. - Carlos Morales, Vercara, a DigiCert Company 12. Lowering Attack Barriers With No-Code Tools The onset of AI-powered no-code tools and 'vibe coding' platforms has lowered the technical barrier for bad actors to launch sophisticated attacks; however, the core tactics remain rooted in social engineering and phishing. The best defense is continuous training, reinforced by phishing simulation tests. Only by developing intuitive awareness of attack vectors can we build lasting workforce resilience. - Pawel Rzeszucinski, Webpros 13. Duplicating Writing Styles Hackers now use AI to create customized phishing emails that duplicate writing patterns and contextual elements to evade detection systems. Organizations need to purchase AI-based threat detection systems and teach staff members to identify minor warning signs, because security awareness has evolved into a human-AI collaboration. - Raju Dandigam, Navan 14. Scanning Open-Source Code For Zero-Day Vulnerabilities Hackers now use AI to scan open-source code and binaries at scale, rapidly uncovering zero-day vulnerabilities in real time. To defend, shift security left by using AI-powered code analysis in CI/CD, continuously scanning for risks across systems, and integrating threat intelligence. As AI accelerates attacks, organizations must respond with early detection, automation and proactive patching. - Harikrishnan Muthukrishnan, Florida Blue 15. Chaining Zero-Day Exploits Hackers now use AI to autonomously chain together zero-day exploits—mapping incomplete vulnerabilities across multiple systems and executing coordinated breaches. To defend, organizations must implement AI-led threat modeling that simulates cross-domain attack paths, enabling preemptive patching even when individual flaws seem harmless in isolation. - Jagadish Gokavarapu, Wissen Infotech 16. Spreading Malware Through Fake GitHub Repositories As seen in a recent case, threat actors use AI to create fake GitHub repositories, misleading developers into downloading malware. Organizations can protect themselves by reviewing open-source code, deploying AI-driven analytics, educating employees on risks, and implementing multifactor authentication and regular patch management. - Arpna Aggarwal 17. Mimicking Trusted Behavior Malicious attackers aren't just using AI to break systems—they're also using it to blend in. When threats mimic trusted behavior, traditional detection falls short. AI can help defenders learn what 'normal' looks like and spot what seems familiar but doesn't match expected patterns. The goal isn't to flag the unusual but to catch the usual in the wrong place, at the wrong time, with the wrong badge. - Leah Dodson, Piqued Solutions 18. Cracking Password Patterns By combining AI and leaked passwords, hackers can now find hidden patterns in our brains. As humans, we are terrible at creating and remembering random passwords; instead, we rely on patterns. Yet, AI can quickly discover these patterns and replay them to find passwords for other services. - Kevin Korte, Univention 19. Causing GenAI Systems To Produce Undesirable Responses One of the new ways hackers are using AI is through 'prompt injection' and 'output injection' on generative AI systems. This results in undesirable responses from enterprise systems—and the outputs produced by these generative AI solutions are critical for end users. Organizations and consumers must refer to the OWASP top 10 list of AI risks and refer to the recommended strategies to mitigate them. - Sid Dixit, CopperPoint Insurance 20. Evolving Ransomware With Adaptive Encryption AI-powered ransomware now adapts encryption methods in real time, making traditional backup strategies less effective. Organizations should implement immutable backups that are stored offline and test recovery procedures monthly to stay ahead of evolving threats. - Chongwei Chen, DataNumen, Inc.
Associated Press
15-07-2025
- Business
- Associated Press
How India's GCCs Are Shaping the Future of Global Enterprises
In a June 2025 Forbes article, Avtar Sehmbi examines the strategic evolution of India's Global Capability Centres (GCCs) from cost-focused support hubs to innovation-led engines of enterprise transformation. Drawing on deep industry experience, he highlights how Indian GCCs are leading in areas such as AI, multi-cloud architecture, edge computing, and sustainable operations. LONDON, GB / ACCESS Newswire / July 15, 2025 / How India's GCCs Are Shaping The Future Of Global Enterprises By Avtar Sehmbi Forbes Technology CouncilAvtar Sehmbi Forbes How GCCs Are Shaping The Future of Global Enterprises Having spent decades leading large-scale digital transformations across global markets, I've experienced firsthand how enterprise operating models evolve in response to disruption, scale and strategic necessity. One of the most significant shifts in the last decade has been the rise of global capability centers (GCCs). And if there's one geography that's shaped this evolution, it's India. India has emerged as the nucleus of the global GCC ecosystem. This didn't happen overnight, and it didn't happen by chance. I've worked closely with leadership teams building out capability centers in every region, from Central Europe to Southeast Asia. Time and again, India stands out as the most strategic choice. Why India Is Poised To Continue Leading Every time I've assessed potential GCC locations, whether for scalability, risk mitigation or access to digital skills, India has consistently surfaced as the most compelling option. The foundations are rock solid. India offers a large and evolving tech talent pool, mature delivery models and a cultural readiness to work across time zones and functions. Recently, I've seen significant traction in Tier 2 cities such as Coimbatore and Jaipur, which offer cost-effective talent with strong retention metrics. Incentives from the Indian government, including special economic zones (SEZs), fast-track approvals and tax benefits, further strengthen the case for long-term investment. One of the most underrated yet foundational strengths of India's GCC ecosystem is its education pipeline. Having led global technology teams for decades, I can confidently say that the scale, quality and consistency of India's talent output is unlike anything I've seen elsewhere. Institutions such as the Indian Institutes of Technology (IITs) and top private universities have partnered with industry to evolve their curricula. Today, they offer courses in relevant subjects such as AI, blockchain, data engineering and cybersecurity. Equally important is India's culture of continuous learning. Upskilling is the norm, not the exception. Platforms like Coursera, upGrad and corporate academies help ensure professionals remain current and future-ready. This academic infrastructure, combined with a digital-first mindset, enables companies to build large, agile and globally integrated teams with confidence. In a world where tech capability defines your competitive edge, India's education system is one of its greatest strategic assets. The Market Speaks For Itself The latest data mirrors what I've seen on the ground. India now employs more than 1.6 million professionals in this sector. It's projected that by 2028, there will be more than 2,100 GCCs employing 3.4 million professionals with a combined revenue in excess of $90 billion. These are no longer just back offices. These GCCs are leading R&D labs, managing global cloud platforms, securing digital ecosystems and developing AI solutions for worldwide markets. Managing The Realities Although I've witnessed the tangible advantages India-based GCCs deliver across most of the firms I've worked for, including global giants such as Deloitte, HSBC and Cigna, there are operational realities to manage. Talent attrition in Tier 1 cities, especially in high-demand functions like AI, DevOps and cybersecurity, can be a challenge. Competition across GCCs is fierce, and professionals have multiple market options. One of the strategies I've used is to split engagement between Tier 1 (e.g., Hyderabad and Bengaluru) and Tier 2 (e.g., Chandigarh and Jaipur) locations and invest in internal mobility. Supply and demand naturally play a part across locations, but talent is very much viable in Tier 2 locations. Additionally, regulatory dynamics, including labor law compliance, data localization and taxation, require proactive governance. The presence of a strong legal and compliance team is essential. Finally, cultural integration plays a pivotal role. Successful global-India integration hinges on shared context, mutual values and co-ownership of outcomes. When this alignment is missing, silos form. When it exists, trust and innovation flourish. The Ultimate Validation Indian-born and India-trained executives are now leading some of the world's most influential companies. That, to me, is the ultimate validation of India's maturity, talent and leadership pipeline. For example, Sundar Pichai, CEO of Alphabet and Google, grew up in Chennai and was educated in India before rising to lead one of the world's most powerful companies. Satya Nadella, CEO of Microsoft, studied in Hyderabad and led the company's transformation into a cloud-first, AI-led enterprise. These leaders often started in technical or consulting roles within Indian operations or multinational firms with a strong GCC presence. What sets them apart is their hands-on understanding of how to scale and transform complex enterprises. In my own experience advising Fortune 500 boards and building global capabilities, I've seen this trend accelerate. India is no longer just a source of execution talent. It's producing globally relevant CxOs with both operational depth and commercial foresight. For organizations investing in India-based GCCs, this can be a long-term strategic advantage. These centers don't just deliver outcomes. They build the leaders who will define enterprise success in the decade ahead. Contact InformationAvtar Sehmbi Global CTO | COO +447481825362 SOURCE: Avtar Sehmbi press release
