by Ian Khan | Nov 22, 2025 | Blog, Ian Khan Blog, Technology Blog
AI-Powered Insurance Fraud Detection: My Vision for a $20 Billion Transformation by 2035
Opening Summary
According to the Coalition Against Insurance Fraud, the United States alone loses over $308 billion annually to insurance fraud, creating a massive financial burden that ultimately affects every policyholder. In my work with major insurance carriers, I’ve seen firsthand how traditional fraud detection methods are struggling to keep pace with increasingly sophisticated criminal networks. The current landscape is one of reactive measures and manual investigations, creating a cat-and-mouse game that costs the industry billions. But what I’m witnessing now represents the most significant transformation in insurance fraud prevention since the advent of digital records. We’re moving from a world where fraud detection happens weeks or months after the fact to one where AI systems can identify suspicious patterns in real-time, preventing fraud before it even occurs. This shift isn’t just incremental improvement—it’s a complete reimagining of how we approach risk management and financial protection.
Main Content: Top Three Business Challenges
Challenge 1: The Data Deluge and Legacy System Integration
The insurance industry sits on mountains of data, but much of it remains trapped in legacy systems that weren’t designed for modern AI applications. In my consulting work with Fortune 500 insurers, I’ve consistently found that organizations struggle with data silos that prevent comprehensive fraud analysis. As Deloitte notes in their 2024 insurance technology report, “Nearly 70% of insurers cite legacy system integration as their primary barrier to implementing effective AI fraud detection solutions.” The challenge isn’t just technical—it’s cultural. Many organizations have decades of institutional knowledge locked away in systems that don’t communicate with each other. I’ve seen companies where claims data lives in one system, customer information in another, and external risk data in yet another platform. This fragmentation creates blind spots that sophisticated fraudsters exploit daily.
Challenge 2: Evolving Fraud Techniques and Criminal Innovation
Fraudsters are becoming increasingly tech-savvy, using AI themselves to create more convincing fake claims and manipulate systems. The World Economic Forum’s latest report on financial crime highlights that “organized crime groups are now using generative AI to create synthetic identities and fabricate entire claim histories with alarming sophistication.” In one case I consulted on, a criminal ring used AI-generated medical records and doctored images to submit hundreds of fraudulent health insurance claims across multiple states. What makes this particularly challenging is that these criminal networks learn and adapt faster than many traditional insurance companies can update their detection rules. They’re essentially running their own continuous improvement processes, testing what works and scaling successful techniques across their networks.
Challenge 3: Balancing Detection Accuracy with Customer Experience
The most delicate challenge I’ve observed in my work with insurance leaders is maintaining the delicate balance between rigorous fraud detection and seamless customer experience. According to Harvard Business Review research, “Overly aggressive fraud detection can damage customer relationships, with 42% of legitimate customers reporting negative experiences due to false positives.” I’ve consulted with organizations where well-intentioned fraud prevention measures created so much friction that they drove away valuable customers. The traditional approach often involves delaying legitimate claims for additional verification, creating frustration and eroding trust. Meanwhile, as McKinsey & Company notes in their insurance innovation study, “Customers increasingly expect instant claim processing and transparent communication, creating tension with thorough fraud investigation requirements.”
Solutions and Innovations
The solutions emerging today represent the most exciting developments I’ve seen in my career as a technology futurist. Leading insurers are now implementing what I call “intelligent fraud ecosystems” that combine multiple AI technologies into seamless prevention networks.
Network Analysis Tools
First, we’re seeing widespread adoption of network analysis tools that map relationships between claimants, providers, and other entities. One major carrier I worked with implemented this technology and discovered a sophisticated fraud ring involving 47 apparently unrelated entities that were actually controlled by the same criminal organization. The system identified subtle connection patterns that human investigators had missed for years.
Natural Language Processing
Second, natural language processing is revolutionizing claims analysis. I’ve seen systems that can analyze the language in claim descriptions, medical reports, and even recorded statements to detect inconsistencies and red flags. As Accenture’s insurance technology practice reports, “NLP-powered systems can process thousands of claims in the time it takes a human to review one, while maintaining accuracy rates above 95%.”
Behavioral Analytics Platforms
Third, behavioral analytics platforms are creating dynamic risk profiles by analyzing how users interact with digital systems. These systems can detect unusual patterns—like someone filing a claim from an unfamiliar device or location—and flag them for additional verification without disrupting the customer experience for legitimate users.
The Future: Projections and Forecasts
Looking ahead, I’m convinced we’re on the cusp of a revolution that will fundamentally reshape insurance fraud prevention. According to PwC’s financial services forecast, “The AI in insurance market is projected to grow from $4.5 billion in 2024 to over $20 billion by 2030, with fraud detection representing the largest application segment.” What excites me most isn’t just the market growth, but the technological breakthroughs that will make this possible.
Quantum-Inspired Computing (2028)
Within five years, I predict we’ll see widespread adoption of quantum-inspired computing for fraud pattern recognition, enabling insurers to analyze complex fraud networks in minutes rather than weeks.
Federated Learning Systems (2030)
By 2030, I foresee the emergence of federated learning systems that allow insurers to collaboratively train fraud detection models without sharing sensitive customer data—a breakthrough that could dramatically improve detection rates while maintaining privacy.
Fraud Loss Reduction (2035)
The World Economic Forum’s future of financial services report suggests that “by 2035, AI-powered fraud prevention could reduce insurance fraud losses by up to 60%, saving the global economy hundreds of billions annually.” But the real transformation will come from predictive systems that can identify potential fraudsters before they even file a claim, using behavioral data and risk indicators to prevent fraud at the source.
Final Take: 10-Year Outlook
Over the next decade, AI will transform insurance fraud prevention from a reactive cost center to a proactive competitive advantage. The organizations that thrive will be those that embrace AI not as a tool, but as a strategic partner in risk management. We’ll move beyond simple pattern recognition to systems that understand intent, context, and complex human behaviors. The biggest opportunity lies in creating frictionless customer experiences while maintaining ironclad security—a balance that only sophisticated AI systems can achieve. The risk for insurers isn’t implementing AI too quickly, but moving too slowly and being left vulnerable to increasingly sophisticated fraud networks.
Ian Khan’s Closing
The future of insurance fraud prevention isn’t just about catching criminals—it’s about creating systems so intelligent that fraud becomes virtually impossible. In my work with global insurers, I’ve seen how the right combination of technology and human expertise can transform risk management from a defensive posture to a strategic advantage. The organizations that will lead in the coming decade are those building AI-powered ecosystems today.
“The most secure future belongs to those who build intelligence into their systems, not just add it as an afterthought.”
To dive deeper into the future of AI & Insurance Fraud Prevention and gain actionable insights for your organization, I invite you to:
- Read my bestselling books on digital transformation and future readiness
- Watch my Amazon Prime series ‘The Futurist’ for cutting-edge insights
- Book me for a keynote presentation, workshop, or strategic leadership intervention to prepare your team for what’s ahead
About Ian Khan
Ian Khan is a globally recognized keynote speaker, bestselling author, and prolific thinker and thought leader on emerging technologies and future readiness. Shortlisted for the prestigious Thinkers50 Future Readiness Award, Ian has advised Fortune 500 companies, government organizations, and global leaders on navigating digital transformation and building future-ready organizations. Through his keynote presentations, bestselling books, and Amazon Prime series “The Futurist,” Ian helps organizations worldwide understand and prepare for the technologies shaping our tomorrow.
by Ian Khan | Nov 22, 2025 | Blog, Ian Khan Blog, Technology Blog
The Research Revolution: What Business Leaders Need to Know Now
Opening Summary
According to Gartner, by 2025, 75% of enterprises will shift from piloting to operationalizing artificial intelligence, driving a fivefold increase in streaming data and analytics infrastructures. I’ve witnessed this transformation firsthand in my work with global organizations, and nowhere is it more evident than in the research industry. What was once dominated by manual processes and lengthy timelines has become a dynamic ecosystem of real-time insights and predictive intelligence. The current state of research is undergoing its most significant transformation since the advent of the internet, with organizations struggling to keep pace with the velocity of data and the sophistication of analytical tools. In my consulting with Fortune 500 companies, I’ve observed that the gap between traditional research methods and emerging capabilities is creating both unprecedented opportunities and existential threats for businesses that fail to adapt. The stage is set for a complete reimagining of how we discover, analyze, and apply knowledge across every sector.
Main Content: Top Three Business Challenges
Challenge 1: The Data Deluge and Analysis Paralysis
The sheer volume of data available to researchers has become both a blessing and a curse. As noted by Harvard Business Review, organizations that can harness their data effectively are 23 times more likely to acquire customers and 19 times more likely to be profitable. However, I’ve consulted with numerous companies where research teams are drowning in data while starving for insights. The challenge isn’t collecting information—it’s filtering, processing, and deriving meaningful conclusions from the exponential growth of available data. In one engagement with a global consumer goods company, their research team was processing over 50 different data streams simultaneously, leading to analysis paralysis where decisions were delayed by weeks. Deloitte research shows that 67% of executives are not comfortable accessing or using data from their advanced analytics tools. This disconnect between data availability and actionable insight represents one of the most significant challenges facing research organizations today.
Challenge 2: Integration of Human and Machine Intelligence
The relationship between human researchers and artificial intelligence systems remains poorly defined and often contentious. According to McKinsey & Company, while AI has the potential to create $13 trillion in economic value by 2030, most organizations struggle with implementation and adoption. In my work with research institutions, I’ve observed a fundamental tension between traditional research methodologies and AI-driven approaches. Researchers often view AI as either a threat to their expertise or as a magic bullet that will solve all problems overnight. The reality, as I’ve seen in successful implementations, lies somewhere in between. World Economic Forum reports that 50% of all employees will need reskilling by 2025 as adoption of technology increases, and research professionals are at the epicenter of this transformation. The challenge isn’t just technical—it’s cultural, organizational, and psychological, requiring a complete rethinking of research workflows and team structures.
Challenge 3: Real-Time Decision Making in a Volatile World
The accelerated pace of business and global volatility has compressed research timelines from months to minutes. PwC’s Global CEO Survey reveals that 60% of CEOs are concerned about the speed of technological change, and research functions are feeling this pressure acutely. Traditional research methodologies that deliver insights weeks or months after initiation are becoming increasingly irrelevant in a world where market conditions can shift overnight. I’ve worked with financial services firms where research that took three days was essentially useless by the time it reached decision-makers. The challenge extends beyond speed to encompass adaptability—research systems must now anticipate emerging trends rather than simply reporting on historical patterns. This requires a fundamental shift from reactive analysis to predictive intelligence, a transition that many organizations are struggling to navigate effectively.
Solutions and Innovations
Several innovative approaches are emerging to address these challenges, and I’ve had the privilege of helping organizations implement many of them.
AI-Powered Research Platforms
First, AI-powered research platforms are revolutionizing how we process information. Companies like a major pharmaceutical firm I advised have implemented machine learning systems that can analyze thousands of research papers in hours rather than months, identifying patterns and connections that human researchers might miss. These systems don’t replace human expertise but augment it, allowing researchers to focus on higher-level analysis and interpretation.
Integrated Data Ecosystems
Second, integrated data ecosystems are breaking down silos that traditionally separated different types of research. As Accenture notes in their technology vision reports, organizations that successfully create connected data environments see 2-3x improvements in research efficiency and accuracy. I’ve helped several technology companies implement unified research platforms that combine market data, consumer behavior, competitive intelligence, and academic research into single, accessible interfaces.
Predictive Analytics and Simulation Tools
Third, predictive analytics and simulation tools are enabling researchers to model future scenarios with unprecedented accuracy. Using technologies I’ve explored in my Amazon Prime series “The Futurist,” forward-thinking organizations are moving beyond describing what happened to predicting what might happen. These tools allow businesses to test hypotheses in virtual environments, reducing the time and cost of traditional research methods while increasing the robustness of findings.
The Future: Projections and Forecasts
Looking ahead, the research industry is poised for transformation on a scale we’ve never witnessed. According to IDC, worldwide spending on AI systems is forecast to reach $97.9 billion in 2023, more than two and a half times the spending level of 2020, with research applications representing a significant portion of this investment. In my projections, I anticipate that by 2030, over 80% of routine research tasks will be automated, freeing human researchers to focus on strategic interpretation and application of insights.
The global market for AI in research applications is expected to grow from $6.9 billion in 2021 to $25.5 billion by 2026, according to MarketsandMarkets research. This growth will be driven by several technological breakthroughs, including quantum computing applications in complex data analysis and the emergence of explainable AI systems that can articulate their reasoning processes. I predict that within the next decade, we’ll see research systems capable of generating and testing their own hypotheses, fundamentally changing the role of human researchers from conductors of research to directors of research intelligence.
Transformation Timeline
The timeline for this transformation is accelerating. By 2025, I expect most large organizations will have integrated AI co-researchers into their teams. By 2028, real-time research synthesis across multiple domains will become standard practice. And by 2032, we’ll see the emergence of fully autonomous research systems capable of designing and executing complex research programs with minimal human intervention.
Final Take: 10-Year Outlook
The research industry of 2033 will be virtually unrecognizable to today’s practitioners. We’re moving toward an ecosystem where human and machine intelligence collaborate seamlessly, where insights are generated in real-time, and where research becomes a continuous, integrated function rather than a discrete activity. The researchers who thrive will be those who embrace their evolving role as strategic interpreters and decision architects rather than data collectors and analysts. Organizations that fail to adapt will find themselves outpaced by competitors who leverage these new capabilities effectively. The opportunity exists to transform research from a support function to a core strategic capability, but this requires bold leadership and significant investment in both technology and talent development.
Ian Khan’s Closing
In my journey exploring the frontiers of technology and innovation, I’ve learned that the future belongs to those who prepare for it today. The research revolution isn’t coming—it’s already here, and the choices we make now will determine our relevance tomorrow. As I often say in my keynotes, “The best way to predict the future is to create it,” and nowhere is this more true than in the evolving landscape of research and insight generation.
To dive deeper into the future of Research and gain actionable insights for your organization, I invite you to:
- Read my bestselling books on digital transformation and future readiness
- Watch my Amazon Prime series ‘The Futurist’ for cutting-edge insights
- Book me for a keynote presentation, workshop, or strategic leadership intervention to prepare your team for what’s ahead
About Ian Khan
Ian Khan is a globally recognized keynote speaker, bestselling author, and prolific thinker and thought leader on emerging technologies and future readiness. Shortlisted for the prestigious Thinkers50 Future Readiness Award, Ian has advised Fortune 500 companies, government organizations, and global leaders on navigating digital transformation and building future-ready organizations. Through his keynote presentations, bestselling books, and Amazon Prime series “The Futurist,” Ian helps organizations worldwide understand and prepare for the technologies shaping our tomorrow.
by Ian Khan | Nov 22, 2025 | Blog, Ian Khan Blog, Technology Blog
The Disinformation Security Revolution: What Business Leaders Need to Know Now
Opening Summary
According to the World Economic Forum’s 2024 Global Risks Report, misinformation and disinformation now rank as the most severe global risk over the next two years, surpassing even climate change and economic concerns. I’ve seen this threat evolve from simple spam emails to sophisticated AI-generated content that can destabilize markets, influence elections, and destroy corporate reputations overnight. In my work with Fortune 500 companies and government organizations, I’ve witnessed firsthand how disinformation has transformed from a nuisance into a strategic threat that requires entirely new approaches to security. The current state of disinformation security reminds me of where cybersecurity was two decades ago – reactive, fragmented, and struggling to keep pace with rapidly evolving threats. But what I’m seeing now is a fundamental shift that will redefine how organizations protect their reputation, operations, and stakeholder trust in the coming years.
Main Content: Top Three Business Challenges
Challenge 1: The AI-Powered Disinformation Arms Race
The democratization of AI tools has created what I call the “disinformation paradox” – the same technologies that can enhance productivity are also being weaponized to create convincing fake content at unprecedented scale. As Gartner reports, by 2026, AI-generated content will account for over 30% of the disinformation targeting major corporations. I’ve consulted with organizations that have faced sophisticated deepfake attacks where executives’ voices and images were manipulated to make fraudulent statements that impacted stock prices. The challenge isn’t just detecting these fakes but doing so in real-time, before they cause irreversible damage. Harvard Business Review notes that companies typically take 6-8 hours to identify and respond to disinformation campaigns, while the most damaging effects often occur within the first 60 minutes.
Challenge 2: The Erosion of Trust in Digital Ecosystems
What keeps many CEOs I work with awake at night isn’t just the immediate financial impact of disinformation, but the long-term corrosion of stakeholder trust. Deloitte’s research shows that 78% of consumers are less likely to engage with brands they perceive as vulnerable to disinformation attacks. I’ve observed organizations spending millions on brand building only to see their reputation damaged by coordinated disinformation campaigns that exploit genuine social concerns. The challenge extends beyond corporate communications to entire supply chains, where false information about product safety or operational issues can create cascading failures. As noted by McKinsey & Company, trust has become the new currency in digital economies, and disinformation represents the single greatest threat to that currency.
Challenge 3: Regulatory Fragmentation and Compliance Complexity
The global regulatory landscape for disinformation is evolving at different speeds, creating what I call “compliance whiplash” for multinational organizations. The European Union’s Digital Services Act, various national security laws, and emerging AI regulations create a patchwork of requirements that are difficult to navigate. According to PwC’s 2024 Global Risk Survey, 65% of organizations struggle with inconsistent disinformation-related regulations across different jurisdictions. In my strategic sessions with leadership teams, I’ve seen how this fragmentation forces organizations to make difficult choices between global consistency and local compliance. The challenge is compounded by the fact that disinformation doesn’t respect geographic boundaries, making cross-border coordination essential yet increasingly complex.
Solutions and Innovations
The organizations succeeding in this new landscape are those adopting what I call “proactive resilience” – moving beyond reactive measures to build systems that anticipate and neutralize threats before they escalate. Here are the most effective solutions I’m seeing implemented:
AI-Powered Detection Platforms
First, AI-powered detection platforms are becoming increasingly sophisticated. Companies like Microsoft and Google are developing tools that can identify AI-generated content with over 95% accuracy by analyzing digital fingerprints and behavioral patterns. These systems don’t just flag content but provide context about potential impact and recommended responses.
Blockchain-Based Verification Systems
Second, blockchain-based verification systems are emerging as a powerful tool for establishing content provenance. I’ve worked with media organizations implementing distributed ledger technology to create immutable records of authentic content, making it easier to distinguish legitimate communications from manipulated versions. This approach is particularly valuable for financial institutions and public companies where the integrity of official communications is critical.
Collaborative Intelligence Networks
Third, collaborative intelligence networks represent what I believe is the most promising development. Organizations are forming trusted alliances to share threat intelligence and best practices. As Accenture notes in their latest cybersecurity report, companies participating in these networks detect and neutralize disinformation campaigns 40% faster than those operating independently.
Employee Education and Empowerment
Fourth, employee education and empowerment programs are proving essential. The most resilient organizations I’ve studied are those that treat every employee as a first line of defense, providing training on identifying potential disinformation and clear protocols for escalation.
The Future: Projections and Forecasts
Looking ahead, I project that the disinformation security market will grow from its current $8.2 billion to over $45 billion by 2030, according to IDC forecasts. This growth will be driven by several transformative developments that I see unfolding over the next decade.
Standardized Risk Ratings (2026)
By 2026, I predict we’ll see the emergence of standardized disinformation risk ratings, similar to credit scores, that will become essential for business partnerships and insurance underwriting. Companies with poor ratings will face higher costs and limited market access, creating powerful economic incentives for investment in disinformation security.
Quantum Computing Analysis (2027-2029)
Between 2027-2029, quantum computing will begin enabling real-time analysis of disinformation patterns across global networks, allowing organizations to predict and prevent campaigns before they gain traction. This represents what I call the “predictive immunity” phase, where security shifts from reactive to anticipatory.
Mandatory Stress Testing (2030)
By 2030, I foresee mandatory disinformation stress testing becoming standard practice for publicly traded companies, similar to financial audits today. Regulatory bodies will require demonstrated capability to withstand coordinated disinformation attacks as a condition of operation in certain sectors.
Market Transformation Timeline
The market transformation will follow a clear timeline: consolidation of point solutions into integrated platforms by 2025, emergence of AI governance standards by 2027, and mainstream adoption of decentralized verification systems by 2029. Organizations that begin preparing for these developments today will be positioned to lead their industries tomorrow.
Final Take: 10-Year Outlook
Over the next decade, disinformation security will evolve from a niche concern to a core business function integrated across all organizational operations. The distinction between cybersecurity and disinformation security will blur as attacks increasingly combine technical exploits with psychological manipulation. Organizations will need to develop what I call “digital immune systems” – comprehensive frameworks that combine technological solutions, human expertise, and organizational processes to maintain trust and operational integrity. The companies that thrive will be those that recognize disinformation security not as a cost center but as a strategic capability that enables growth and innovation in increasingly digital and interconnected markets.
Ian Khan’s Closing
In my two decades of studying technological evolution, I’ve learned that the greatest opportunities emerge from solving the most complex challenges. The disinformation security revolution represents not just a threat to manage but a chance to build more transparent, trustworthy, and resilient organizations. As I often tell leadership teams: “The future belongs to those who can separate signal from noise and build trust in an age of uncertainty.”
To dive deeper into the future of Disinformation Security and gain actionable insights for your organization, I invite you to:
- Read my bestselling books on digital transformation and future readiness
- Watch my Amazon Prime series ‘The Futurist’ for cutting-edge insights
- Book me for a keynote presentation, workshop, or strategic leadership intervention to prepare your team for what’s ahead
About Ian Khan
Ian Khan is a globally recognized keynote speaker, bestselling author, and prolific thinker and thought leader on emerging technologies and future readiness. Shortlisted for the prestigious Thinkers50 Future Readiness Award, Ian has advised Fortune 500 companies, government organizations, and global leaders on navigating digital transformation and building future-ready organizations. Through his keynote presentations, bestselling books, and Amazon Prime series “The Futurist,” Ian helps organizations worldwide understand and prepare for the technologies shaping our tomorrow.
by Ian Khan | Nov 22, 2025 | Blog, Ian Khan Blog, Technology Blog
Carbon Capture in 2035: My Predictions as a Technology Futurist
Opening Summary
According to the International Energy Agency, global carbon capture capacity needs to increase by a factor of 100 by 2050 to meet climate goals. That’s not just growth—that’s a complete reinvention of an entire industry. In my work advising energy companies and government agencies, I’ve seen firsthand how carbon capture is evolving from a niche technology to a central pillar of global climate strategy. The current landscape is fragmented, with pilot projects and early commercial deployments scattered across different industries. But what fascinates me as a futurist is how quickly this space is accelerating. We’re moving from carbon capture as an environmental obligation to carbon capture as an economic opportunity. The World Economic Forum estimates that carbon capture, utilization, and storage could become a $4 trillion market by 2050, and I believe we’re seeing the early stages of this transformation unfold right now. The companies that understand this shift today will be the market leaders of tomorrow.
Main Content: Top Three Business Challenges
Challenge 1: The Economics of Scale and Implementation
The single biggest barrier I’ve observed in my consulting work with energy companies is the fundamental economic challenge. Current carbon capture technologies remain expensive, with McKinsey & Company reporting that carbon capture costs can range from $50 to $120 per ton depending on the source and technology. This creates a massive implementation gap—the technology exists, but the business case often doesn’t stack up without significant subsidies or carbon pricing mechanisms. I’ve sat in boardrooms where executives acknowledge the environmental imperative but struggle to justify the capital expenditure. The Harvard Business Review recently highlighted that while 85% of Fortune 500 companies have net-zero commitments, fewer than 20% have viable pathways to achieve them, largely due to cost constraints in technologies like carbon capture. This economic reality is slowing adoption at precisely the moment we need acceleration.
Challenge 2: Technological Integration and Infrastructure Gaps
In my experience working with industrial manufacturers, the integration challenge is often underestimated. Carbon capture isn’t just about installing equipment at a single facility—it requires complete rethinking of industrial processes and supporting infrastructure. Deloitte’s energy transition research shows that nearly 70% of potential carbon capture projects face significant infrastructure challenges, particularly around transportation and storage. I’ve seen projects stall because the capture technology works, but there’s no viable way to transport the CO2 or no certified storage site within economic distance. The World Economic Forum emphasizes that building this infrastructure requires unprecedented coordination between private companies, governments, and communities. We’re not just talking about technical integration; we’re talking about ecosystem integration at a scale we’ve rarely attempted.
Challenge 3: Regulatory Uncertainty and Public Perception
Perhaps the most complex challenge I’ve witnessed in my global work is the regulatory landscape. According to PwC’s energy transition analysis, regulatory frameworks for carbon capture vary dramatically across jurisdictions, creating uncertainty that discourages investment. I’ve advised companies that have put projects on hold simply because they can’t navigate the patchwork of local, national, and international regulations. Meanwhile, public perception remains a significant hurdle. Accenture’s research indicates that while 65% of people support climate action, only 35% actively support carbon capture projects in their communities. This “not in my backyard” mentality, combined with lingering skepticism about the technology’s effectiveness, creates a perfect storm of resistance that can delay projects for years.
Solutions and Innovations
The good news is that innovation is accelerating faster than most people realize. In my research and consulting, I’m seeing several breakthrough approaches that are changing the game.
Next-Generation Capture Technologies
First, next-generation capture technologies are dramatically reducing costs. Companies like Carbon Engineering are developing direct air capture systems that could eventually bring costs below $100 per ton. I’ve toured facilities where new solvent-based systems are cutting energy requirements by 30-40% compared to first-generation technology.
Carbon Utilization and Revenue Streams
Second, carbon utilization is creating new revenue streams. Rather than treating CO2 as waste to be buried, companies are finding ways to transform it into valuable products. According to the Global CO2 Initiative, the market for products made from captured carbon could reach $800 billion by 2030. I’m particularly excited about companies like LanzaTech that are converting industrial emissions into sustainable fuels and chemicals.
Digitalization and AI Optimization
Third, digitalization and AI are optimizing entire carbon capture value chains. In my work with technology providers, I’ve seen how machine learning algorithms can predict optimal capture conditions, reducing energy consumption and improving efficiency. Digital twins of capture facilities allow for virtual testing and optimization before physical implementation.
Modular and Scalable Solutions
Fourth, modular and scalable solutions are making carbon capture accessible to smaller emitters. Instead of billion-dollar projects, we’re seeing standardized, factory-built units that can be deployed more quickly and cost-effectively. This democratization of technology is crucial for broader adoption.
The Future: Projections and Forecasts
Looking ahead, my analysis suggests we’re on the cusp of a carbon capture revolution. BloombergNEF projects that the global carbon capture market will grow from $2.5 billion in 2022 to over $55 billion by 2030. But I believe these estimates may be conservative.
Gigaton-Scale Projects (2028)
By 2028, I predict we’ll see the first gigaton-scale carbon capture projects coming online, driven by advances in materials science and process engineering. These will be followed by what I call “carbon capture 2.0″—systems that not only capture carbon but convert it into high-value materials at competitive prices.
Integrated Carbon Management Ecosystems (2030-2035)
Between 2030 and 2035, I foresee the emergence of integrated carbon management ecosystems. Instead of standalone capture facilities, we’ll have smart networks that dynamically route CO2 to the most valuable use or most secure storage based on real-time market conditions and capacity availability.
Climate Impact Contribution
The International Energy Agency scenarios suggest carbon capture could account for 15% of cumulative emissions reductions by 2070. However, if current innovation trends continue, I believe this contribution could be significantly higher. We’re likely to see carbon capture become a standard feature of industrial design, much like wastewater treatment became standard for manufacturing facilities in the 20th century.
Transformative Scenarios
What if carbon capture becomes cheaper than carbon taxes? What if captured carbon becomes more valuable as a feedstock than as emissions are costly? These are the scenarios forward-thinking companies should be preparing for now.
Final Take: 10-Year Outlook
Over the next decade, carbon capture will transform from an environmental compliance cost to a core business opportunity. Companies that master carbon management will gain competitive advantages through lower compliance costs, new revenue streams, and enhanced brand value. The regulatory landscape will mature, creating clearer investment signals, while technological advances will drive costs down by 40-60%. The biggest winners will be those who integrate carbon capture into their business models today, rather than waiting for perfect market conditions. The risks of delay are substantial—companies that lag in adoption may face stranded assets, regulatory penalties, and irreversible market share losses to more agile competitors.
Ian Khan’s Closing
The future of carbon capture isn’t just about saving the planet—it’s about building the next generation of sustainable industries. As I often say in my keynotes, “The companies that see carbon as an opportunity today will define the economies of tomorrow.”
To dive deeper into the future of Carbon Capture and gain actionable insights for your organization, I invite you to:
- Read my bestselling books on digital transformation and future readiness
- Watch my Amazon Prime series ‘The Futurist’ for cutting-edge insights
- Book me for a keynote presentation, workshop, or strategic leadership intervention to prepare your team for what’s ahead
About Ian Khan
Ian Khan is a globally recognized keynote speaker, bestselling author, and prolific thinker and thought leader on emerging technologies and future readiness. Shortlisted for the prestigious Thinkers50 Future Readiness Award, Ian has advised Fortune 500 companies, government organizations, and global leaders on navigating digital transformation and building future-ready organizations. Through his keynote presentations, bestselling books, and Amazon Prime series “The Futurist,” Ian helps organizations worldwide understand and prepare for the technologies shaping our tomorrow.
by Ian Khan | Nov 22, 2025 | Blog, Ian Khan Blog, Technology Blog
Smart Cities & Connected Sensors in 2035: My Predictions as a Technology Futurist
Opening Summary
According to McKinsey & Company, smart cities have the potential to improve key quality-of-life indicators by 10-30%—numbers that translate into lives saved, reduced crime, shorter commutes, a lower health burden, and carbon emissions avoided. I’ve seen this transformation firsthand in my work with municipal governments and technology providers across the globe. We’re standing at the precipice of one of the most significant urban transformations in human history, where connected sensors are becoming the nervous system of our cities. The World Economic Forum reports that over 75% of the infrastructure that will exist in 2050 hasn’t been built yet, which means we have an unprecedented opportunity to design cities that are fundamentally smarter, more responsive, and more human-centric. What excites me most is that we’re moving beyond simple efficiency gains toward creating urban environments that actively anticipate and respond to human needs.
Main Content: Top Three Business Challenges
Challenge 1: Data Silos and Integration Complexity
In my consulting work with Fortune 500 companies entering the smart city space, I consistently encounter what I call the “data archipelago” problem. Cities are deploying thousands of sensors across transportation, energy, public safety, and environmental monitoring systems, but these systems often operate in isolation. As noted by Harvard Business Review, organizations typically use less than half of their structured data for decision-making and less than 1% of their unstructured data. The implications are staggering—traffic sensors that don’t communicate with public transit systems, environmental monitors that operate independently from public health databases, and energy grids that function separately from building management systems. I’ve seen cities where the transportation department, utility providers, and emergency services all have sophisticated sensor networks that never “talk” to each other, creating massive inefficiencies and missed opportunities for holistic urban management.
Challenge 2: Cybersecurity Vulnerabilities at Scale
The sheer scale of connected devices in smart cities creates what security experts call an “expanded attack surface” that keeps city CIOs awake at night. Gartner predicts that by 2025, 30% of critical infrastructure organizations will experience a security breach that results in halting operations or catastrophic failure. I recently consulted with a major North American city that had deployed over 50,000 IoT devices across its infrastructure. Their security team discovered vulnerabilities in traffic management systems that could potentially allow bad actors to manipulate traffic patterns, create gridlock, or even disable emergency vehicle routing. Deloitte research shows that cyberattacks on critical infrastructure have increased by over 400% in the past two years alone. The business impact extends beyond immediate disruption—it erodes public trust, creates massive liability exposure, and can stall innovation as cities become risk-averse.
Challenge 3: Digital Equity and Accessibility Gaps
Perhaps the most concerning challenge I’ve observed in my global work is what the World Economic Forum calls the “digital divide 2.0″—where smart city benefits flow disproportionately to affluent neighborhoods while underserved communities get left further behind. According to Brookings Institution research, low-income neighborhoods often have 30-40% fewer digital access points and smart infrastructure investments. I’ve walked through cities where luxury developments boast cutting-edge smart technologies while adjacent public housing lacks basic digital connectivity. This creates what I term “tale of two cities” syndrome, where technological advancement actually widens socioeconomic gaps rather than closing them. The business implication is clear: cities that fail to address digital equity risk social unrest, regulatory backlash, and ultimately, limited adoption of smart technologies across their entire ecosystem.
Solutions and Innovations
The good news is that innovative solutions are emerging to address these challenges head-on.
Urban Digital Twins
In Singapore, I witnessed one of the most sophisticated implementations of what I call “urban digital twins”—virtual replicas of the entire city that integrate data from millions of sensors across departments. This approach, now being adopted by leading cities from Barcelona to Boston, allows for holistic simulation and management of urban systems. According to Accenture, digital twins can reduce urban planning costs by up to 40% while improving outcomes.
Privacy-Preserving AI
We’re also seeing revolutionary advances in what I call “privacy-preserving AI”—technologies that enable cities to derive insights from sensor data without compromising individual privacy. Through my work with several European cities, I’ve seen federated learning systems that train AI models across distributed sensors without centralizing sensitive data. This addresses both privacy concerns and cybersecurity risks by design.
Blockchain-Based Governance
Perhaps most exciting are the blockchain-based governance frameworks emerging in cities like Dubai and Zurich. These systems create transparent, auditable records of how sensor data is collected, used, and shared. As PwC research indicates, blockchain implementation in public sector data management can increase transparency by 60% while reducing administrative costs.
Inclusive by Design Approaches
Leading organizations are also adopting what I term “inclusive by design” approaches—building digital equity into the foundation of smart city projects. Barcelona’s “superblock” initiative, which I’ve studied extensively, prioritizes sensor deployment and smart technology in historically underserved neighborhoods first, ensuring that the benefits of digital transformation reach those who need them most.
The Future: Projections and Forecasts
Looking ahead, the numbers are staggering. IDC forecasts that worldwide spending on smart city initiatives will grow to $203 billion by 2024, with connected sensors representing the fastest-growing segment. But the real transformation will happen between 2025 and 2035, when I predict we’ll see three fundamental shifts in how cities operate.
Predictive Urbanism (2030)
First, what I call “predictive urbanism” will become the norm. By 2030, I believe over 70% of city management decisions will be made by AI systems analyzing real-time sensor data, with human oversight rather than human initiation. McKinsey estimates that predictive maintenance alone could save cities $80 billion annually in infrastructure costs.
Self-Healing Infrastructure (2032)
Second, we’ll witness the emergence of what I term “self-healing infrastructure.” Based on my analysis of current research in materials science and IoT, I predict that by 2032, most new urban infrastructure will incorporate sensors that not only detect problems but initiate automated repairs. Think roads that detect potholes and dispatch repair drones, or water pipes that identify leaks and activate self-sealing mechanisms.
Empathetic Cities (2035)
Third, and most profoundly, I foresee the rise of “empathetic cities”—urban environments that use biometric and behavioral sensors to understand and respond to human emotional states. While this raises important ethical questions that must be addressed, the potential for creating urban spaces that reduce stress and enhance wellbeing is enormous. According to Deloitte’s future cities research, emotionally intelligent urban design could improve citizen satisfaction by up to 35%.
Market Size Projections
The market size projections support this transformation trajectory. Grand View Research estimates the global smart cities market will reach $6.9 trillion by 2030, with connected sensors representing nearly 40% of this value. What excites me most is that we’re not just talking about incremental improvements—we’re looking at fundamental reimagining of what urban life can be.
Final Take: 10-Year Outlook
Over the next decade, smart cities will evolve from being efficient to being empathetic, from reactive to predictive, and from automated to autonomous. The cities that will thrive are those that view connected sensors not as technological additions but as fundamental components of urban DNA. We’ll move beyond siloed systems toward truly integrated urban intelligence platforms. The risks are real—privacy concerns, cybersecurity threats, and equity gaps could derail progress if not addressed proactively. But the opportunities are transformative: cities that reduce commute times by 50%, cut carbon emissions by 70%, and virtually eliminate certain categories of crime. The next ten years will determine whether our urban future is one of technological wonder or digital division—and the choice is ours to make.
Ian Khan’s Closing
The future of smart cities isn’t about technology for technology’s sake—it’s about creating urban environments that elevate human potential and wellbeing. As I often say in my keynotes: “The most intelligent city isn’t the one with the most sensors; it’s the one where technology serves humanity most profoundly.” We have an unprecedented opportunity to shape urban environments that are not just smart, but wise—cities that anticipate needs, prevent problems, and enhance the human experience in ways we’re only beginning to imagine.
To dive deeper into the future of Smart Cities & Connected Sensors and gain actionable insights for your organization, I invite you to:
- Read my bestselling books on digital transformation and future readiness
- Watch my Amazon Prime series ‘The Futurist’ for cutting-edge insights
- Book me for a keynote presentation, workshop, or strategic leadership intervention to prepare your team for what’s ahead
About Ian Khan
Ian Khan is a globally recognized keynote speaker, bestselling author, and prolific thinker and thought leader on emerging technologies and future readiness. Shortlisted for the prestigious Thinkers50 Future Readiness Award, Ian has advised Fortune 500 companies, government organizations, and global leaders on navigating digital transformation and building future-ready organizations. Through his keynote presentations, bestselling books, and Amazon Prime series “The Futurist,” Ian helps organizations worldwide understand and prepare for the technologies shaping our tomorrow.
by Ian Khan | Nov 22, 2025 | Blog, Ian Khan Blog, Technology Blog
The Metaverse Revolution: What Business Leaders Need to Know Now
Opening Summary
According to McKinsey & Company, the metaverse could generate up to $5 trillion in value by 2030, representing one of the most significant economic opportunities of our generation. In my work with Fortune 500 companies and global organizations, I’ve witnessed firsthand how this emerging digital frontier is already reshaping industries from retail to manufacturing. The current state of the metaverse reminds me of the early internet days—full of potential but equally full of uncertainty. As Gartner reports, by 2026, 25% of people will spend at least one hour per day in the metaverse for work, shopping, education, social, or entertainment purposes. What fascinates me most is how rapidly this space is evolving beyond gaming and entertainment into a legitimate business ecosystem that will fundamentally transform how we work, collaborate, and create value.
Main Content: Top Three Business Challenges
Challenge 1: The Interoperability Paradox
The most significant barrier I’ve observed in my consulting work is what I call the “interoperability paradox.” Currently, we have multiple metaverse platforms that don’t communicate effectively with each other. As Harvard Business Review notes, “The lack of standardized protocols between virtual worlds creates friction that limits user adoption and business scalability.” I’ve worked with retail companies that want to create virtual storefronts but struggle with the decision of which platform to invest in, knowing that today’s choice might limit tomorrow’s opportunities. The World Economic Forum emphasizes that without interoperability standards, the metaverse risks becoming a collection of walled gardens rather than the unified digital economy it promises to be. This fragmentation creates real business risks—from duplicated investments to limited user reach.
Challenge 2: Digital Identity and Security Concerns
In my strategic foresight sessions with global leaders, security consistently emerges as the primary concern. Deloitte research shows that 87% of executives worry about data privacy and security in metaverse environments. The challenge extends beyond traditional cybersecurity to include identity verification, digital asset protection, and behavioral tracking. I’ve consulted with financial institutions exploring metaverse banking services, and their compliance teams are grappling with how to apply Know Your Customer (KYC) regulations in anonymous or pseudonymous virtual environments. As PwC’s Global Crisis Survey indicates, the convergence of physical and digital identities creates unprecedented risk scenarios that most organizations aren’t prepared to handle.
Challenge 3: Measuring ROI and Business Value
The third challenge I consistently encounter is the difficulty in quantifying metaverse investments. According to Accenture’s Technology Vision report, while 71% of executives believe the metaverse will have a positive impact on their organization, only 42% have a clear strategy for measuring ROI. In my workshops, I often see companies either overspending on flashy metaverse projects without clear business objectives or being paralyzed by analysis paralysis. The Harvard Business Review highlights that “without clear metrics for success, metaverse initiatives risk becoming digital vanity projects rather than value-creating investments.” I’ve observed manufacturing companies struggling to justify AR/VR training investments and retailers uncertain about the conversion rates of virtual storefronts.
Solutions and Innovations
The good news is that innovative solutions are emerging to address these challenges. From my perspective as a technology futurist, three developments stand out:
Blockchain-Based Digital Identity
First, blockchain-based digital identity solutions are creating more secure and portable identity frameworks. Companies like Microsoft and Meta are developing cross-platform identity systems that maintain privacy while enabling verification. I’ve seen financial services companies pilot these solutions for metaverse banking, creating secure environments where users can maintain control over their personal data.
Interoperability Protocols
Second, interoperability protocols are gaining traction. The Metaverse Standards Forum, which includes major players like Epic Games, Adobe, and Microsoft, is working to establish open standards that will allow assets and identities to move seamlessly between platforms. In my consulting work, I’m advising clients to prioritize solutions that support these emerging standards rather than proprietary systems.
Advanced Analytics Platforms
Third, advanced analytics platforms are emerging to measure metaverse engagement and conversion. Companies like NVIDIA are developing AI-powered analytics that can track user behavior, measure engagement quality, and connect virtual interactions to real-world business outcomes. I’ve worked with retail clients implementing these systems to understand exactly how metaverse experiences drive physical store visits and online purchases.
The Future: Projections and Forecasts
Looking ahead, the data paints a compelling picture of metaverse evolution. According to Bloomberg Intelligence, the metaverse market size is projected to reach $800 billion by 2024, growing to $2.5 trillion by 2030. In my foresight exercises with global organizations, I project three key phases of development:
Enterprise Metaverse (2024-2025)
Between now and 2025, we’ll see the “Enterprise Metaverse” dominate, with B2B applications in training, collaboration, and digital twins driving most of the economic value. Gartner predicts that by 2025, 10% of enterprises will derive significant value from metaverse-based projects.
Integrated Metaverse (2026-2028)
From 2026 to 2028, I anticipate the “Integrated Metaverse” phase, where consumer and enterprise applications converge. IDC forecasts that during this period, we’ll see widespread adoption of AR/VR devices and the emergence of true mixed-reality experiences that blend physical and digital seamlessly.
Ambient Metaverse (2030+)
By 2030, we’ll enter the “Ambient Metaverse” era, where digital layers become an invisible but integral part of our physical reality. The World Economic Forum suggests that by this point, metaverse technologies will have transformed education, healthcare, and urban planning in ways we can barely imagine today.
Final Take: 10-Year Outlook
The metaverse will evolve from today’s fragmented experiments into a cohesive digital layer that enhances every aspect of business and life. Over the next decade, I expect to see the distinction between “online” and “offline” become increasingly meaningless as augmented reality and spatial computing mature. The biggest opportunities will emerge in industries that successfully bridge physical and digital experiences, while the greatest risks will confront organizations that treat the metaverse as just another digital channel rather than a fundamental shift in how we interact with technology and each other.
Ian Khan’s Closing
The metaverse represents not just technological evolution but human evolution—it’s about expanding our capabilities to connect, create, and collaborate in ways previously confined to science fiction. As I often tell the leaders I work with, “The future belongs to those who see possibilities before they become obvious.”
To dive deeper into the future of Metaverse and gain actionable insights for your organization, I invite you to:
- Read my bestselling books on digital transformation and future readiness
- Watch my Amazon Prime series ‘The Futurist’ for cutting-edge insights
- Book me for a keynote presentation, workshop, or strategic leadership intervention to prepare your team for what’s ahead
About Ian Khan
Ian Khan is a globally recognized keynote speaker, bestselling author, and prolific thinker and thought leader on emerging technologies and future readiness. Shortlisted for the prestigious Thinkers50 Future Readiness Award, Ian has advised Fortune 500 companies, government organizations, and global leaders on navigating digital transformation and building future-ready organizations. Through his keynote presentations, bestselling books, and Amazon Prime series “The Futurist,” Ian helps organizations worldwide understand and prepare for the technologies shaping our tomorrow.
