According to Verizon’s CX Annual Insights report, the future of CX isn’t just about implementing AI, but about strategically integrating it to amplify human connections and address core customer frustrations. The report reveals a critical disconnect: Human Touch Still Reigns Supreme: A striking 88% of consumers are satisfied with interactions handled mostly or fully by human agents, while only 60% feel the same about interactions driven by AI. This preference highlights a fundamental truth: AI’s efficiency cannot replace the empathy and trust that a human provides. The Biggest Frustration: The Human Hand-Off: The single biggest source of consumer frustration with automated interactions is the inability to speak or chat with a live human agent when needed. 47% of all consumers cited this as their top annoyance. Brands themselves recognize this, with a similar percentage of executives reporting this as the main complaint they receive about AI-enabled interactions. The Personalization Paradox: Despite personalization being a top AI use case for brands, most consumers aren’t seeing the benefits. In fact, more consumers said personalization has detracted from their overall experience (30%) than improved it (26%). A significant factor is data privacy, with 65% of executives stating that data privacy rules limit their ability to use AI for personalization. This is a critical issue as 54% of consumers report a decline in their trust in companies to use their personal data properly. Real-World Examples of AI That Works: The Power of Proactive Help: As outlined in the Insights Report, energy utility company Exelon is a prime example. During the COVID-19 lockdowns, the company used AI and predictive analytics to identify middle-income households that might have trouble paying their energy bills. This enabled them to proactively reach out with personalized recommendations for assistance programs, earning customer appreciation and proving that AI can solve real-world problems with a human-centric approach. AI as an Agent’s Assistant: Instead of being used to replace human agents, AI is being used to make them more effective. Exelon is piloting generative AI to help its customer service representatives handle calls more efficiently by providing the right data at the right time and summarizing calls, which eases the agent’s burden. This aligns with the report’s finding that companies are now equally prioritizing investments in both human and AI-driven CX improvements.
Sentient’s The GRID offers the world’s largest open-source AI network, letting developers monetize and coordinate artificial intelligence agents across real-world environments
Open-source AI development platform provider Sentient Foundation has launched The GRID, a “network of intelligence” that is designed to let developers monetize and coordinate artificial intelligence agents across real-world environments. The GRID varies from proprietary marketplaces from the likes of OpenAI and AWS Inc. by being developer-led, open to all and designed to let builders monetize and orchestrate intelligent agents across open, real-world environments. At launch, The GRID features more than 40 specialized agents, 50 data sources and more than 10 models. The agents include generative graphics engine Napkin and fast-growing search startup Exa Inc., as well as ecosystem agents deployed across multiple blockchains, such as Base, BNB, Polygon, Arbitrum, Celo and Near. The GRID also lays the foundation for composability between agents, which Sentience says allows for multiple requests to be routed via numerous AI agents. The agents and data sources are accessible through Sentient Chat, a consumer interface that allows users to discover, invoke and compose agents and data sources into task workflows. The agents on The GRID can perform real tasks, not just wrap prompts, whether coordinating calendar actions, generating code visualizing wallet data, or synthesizing search results. The new service offers developers full transparency, monetization pathways and distribution. Builders can plug in their own agents, models, or tools and earn token-based rewards as users stake against their favorite agents, data sources, training libraries and models and interact with them in real time. For developers, The GRID offers more than just agent distribution by also supporting a growing ecosystem of open AI “artifacts,” including models, datasets, compute resources and tooling, that can all be integrated, composed and monetized. The GRID also benefits users, who gain access to a diverse ecosystem of AI agents with clear provenance and the ability to customize, compose, or swap components. Sentient’s staking mechanism introduces a feedback loop: the more conviction users have in a given agent, the more that agent is funded and surfaced, democratizing both innovation and economic upside.
Institutions boost mainstream crypto adoption via tokenization, cross-chain connectivity, and regulatory clarity—transforming finance with integrated platforms, Ethereum staking, and blockchain-enabled innovation
1) Mainstream crypto adoption gains momentum: The path to mainstream crypto adoption is accelerating as institutions embrace tokenization, cross-chain connectivity and real-time verification to modernize financial systems. Sergey Nazarov, co-founder of Chainlink Labs, sees regulatory clarity, global liquidity and blockchain-based infrastructure driving this shift into the next era of on-chain finance. 2) Institutional-grade crypto integration: Talos Global Inc. is transforming institutional access to digital assets through its one-stop platform, connecting clients to exchanges, OTC desks, custodians and post-trade services in a single integration. With its recent Coin Metrics Inc. acquisition, Talos strengthens its data capabilities while accelerating global adoption, said Anton Katz, co-founder and chief executive officer of Talos. 3) Incubating innovation at the intersection of Web3, AI and biotech: At YZi Labs, mainstream crypto adoption is just one part of a bigger vision that blends blockchain with AI and biotech to create transformative, mission-driven companies. 4) Ethereum staking advantage for institutions: Andrew Keys, co-founder and chairman of The Ether Machine, sees a massive gap in how institutional investors can fully capitalize on Ethereum’s yield potential. By enabling 100% staking capacity, layering in restaking and engaging in DeFi, his approach aims to outperform ETFs and offer a cleaner, more flexible vehicle for Ether exposure. 5) Innovation fueling the next wave: Momentum for mainstream crypto adoption is accelerating as institutions, enterprises and even global brands explore tokenization, custom L1 blockchains and blockchain-enabled economies. 6) Blockchain meets AI in a new data economy: OpenLedger is redefining how AI models are built by creating a transparent, blockchain-powered marketplace for proprietary, domain-specific data. This “payable AI” approach rewards contributors whose data improves models, enabling decentralized intelligence to flourish across industries, explained Ram Kumar, core contributor at OpenLedger, a blockchain AI company. 7) Entrepreneurship surges as clarity fuels crypto’s next wave: With regulatory clarity restoring confidence, innovation in digital assets is accelerating, drawing top founders and investors back into the fold. Diogo Monica, co-founder and executive chairman of Anchorage, the wholly-owned subsidiaries of Anchor Labs Inc., and general partner at Haun Ventures Management LP, says mainstream crypto adoption is now propelled by stablecoin growth, strategic M&A and renewed institutional engagement. 8) Institutional crypto security and expansion: Ledger SAS is evolving from its roots in consumer crypto wallets to delivering enterprise-grade security, governance and trading solutions for the institutional wave reshaping digital finance. With integrations for custody, payments, staking and multi-chain treasury management, the company is meeting global demand, says Sebastien Badault, vice president of enterprise at Ledger. 9) Decentralized infrastructure powering the next wave of adoption: As digital assets evolve on decentralized infrastructure, mainstream crypto adoption is accelerating with powerful new platforms enabling developers to build at scale.
Morgan Stanley: AI-powered “agents” could transform everyday chat apps into services that can shop, book taxis and handle basic tasks without users ever leaving the conversation screen
Mobile messengers such as WeChat, LINE and Kakao are preparing to embed AI-powered “agents” into their platforms, which analysts say could transform everyday chat apps into services that can shop, book taxis and handle basic tasks without users ever leaving the conversation screen. Morgan Stanley says messaging apps in Asia are likely to be the first mainstream platforms to bring AI agents to the masses. These tools would go beyond chatbots, acting more like digital assistants deeply embedded into existing services such as shopping or payments. MS analysts say messaging apps already a daily habit for hundreds of millions in Asia are in a stronger position than standalone AI apps to drive mass adoption. If successful, these platforms could become “superapps” with new revenue streams from third-party developers joining their ecosystems. Tencent is expected to tap its own AI model to power new services on WeChat, the region’s largest messaging platform, while LINE and Kakao are working with OpenAI. The transformation is still in early stages, but Morgan Stanley says the upside is not yet priced into shares of Kakao or LY Corp, which is owner of LINE. It raised its price target on Kakao and reiterated its overweight view on Tencent, adding that LY Corp’s AI potential remains largely ignored by the market. Risks include poor user adoption, competition from large internet companies, or global AI players bypassing messaging apps entirely. But if the integration is seamless, AI agents could push messaging services well beyond simple text, triggering the next phase in how users interact with their phones and how platforms make money.
Enterprise AI struggles due to fragmented data, poor governance, and infrastructure limits, amplifying errors and bias that erode trust and misinform business decisions
Across boardrooms, enterprise AI has become the biggest line item in the innovation budget — yet it’s also become the biggest source of anxiety. Andrew Frawley, CEO of Data Axle, believes the major problem begins before even a single line of code is written. “The real issue isn’t the technology itself, but the foundation,” he told me. “Companies are obsessing over models while neglecting or under-nurturing the one thing those models rely on: data.” Fragmented records and siloed systems have become default conditions in most enterprises. AI only exposes those fractures faster and at scale. “Some brands, blinded by AI’s possibilities and potential, rush for immediate deployment while bypassing the crucial, foundational work of establishing a data infrastructure,” he explained. “The most critical steps — which include establishing data ownership, building governance into workflows and enforcing quality standards — often get pushed aside in the interest of speed.” But that, according to Frawley, always results in misfires that damage trust. Udo Foerster, CEO of German consultancy Advan Team, sees similar dysfunction among the businesses he advises. For all the talk of algorithms, it’s the invisible plumbing beneath AI that’s doing the damage. Ken Mahoney, CEO of Mahoney Asset Management, flagged another overlooked bottleneck: The physical limits of AI’s appetite for energy and infrastructure. Frawley says that without clear strategy and clean data, models confidently push the wrong action. “Deploying AI on fragmented or inaccurate data is an act of self-sabotage,” he said. “It will amplify existing flaws, erode the quality of analytics and introduce a false sense of confidence in misinformed decisions. With fragmented or inaccurate data, they amplify errors and bias at speed, autonomously executing actions, pushing a business further in the wrong direction before the problem can be detected.”
Corporate ESG backlash has created three leadership camps: paralyzed, compliant, and those who see sustainability as a lens for business resilience; businesses need radical sustainability to move past incrementalism toward impact-focused resilience and long-term adaptation
A recent global survey Sustainability at a Crossroads by ERM, GlobeScan, and Volans reveals that 93% of sustainability experts say the current agenda is no longer fit for purpose, and more than half call for a radical overhaul. This is more than a policy or branding challenge. It’s a convergence of environmental breakdown, social regression, and governance backlash – an interlinked crisis with direct consequences for stability, markets, and public trust. According to Louise Kjellerup Roper, chief executive of Volans, the growing complexity of the sustainability agenda, and the backlash surrounding it, has led to fragmentation at the highest levels of leadership into three broad camps. Roughly a third appear paralyzed, unsure how to respond as ESG becomes a political flashpoint and global regulations tighten. Another third continues with a familiar playbook: annual reporting, compliance-focused governance, and reputational risk management. But the final third (perhaps the most interesting group) has accelerated their sustainability efforts, with one major shift: they’ve reframed sustainability not as a set of obligations, but as a lens on resilience and business continuity. This reframing is quietly transformative. It takes sustainability out of the realm of “nice-to-have” and into the core of business strategy. It positions climate risk, supply chain fragility, social license, and institutional trust as interdependent threats – and invites companies to build toward stability and adaptation rather than chasing perfection or PR wins. Crucially, it turns challenge into opportunity. “This might be the moment that takes us from threat to transformation,” says Roper. The future of sustainability may well be grounded in designing for resilience, long-term coherence and real-world impact, not just disclosures and defensive positioning. The survey’s framework points to four mindsets emerging among sustainability professionals: the traditionalists, who seek improvement within existing models; the institutionalists, who believe in coordinated reform; the pathfinders, who look for innovation within market systems; and the radicals, who advocate for transformational change across the board. Perhaps the most powerful insight is that the future won’t belong to any single mindset but instead to those willing and able to bridge them.
Hugging Face: 5 To slash AI costs, enterprises should adopt task-specific distilled models, batch optimization, energy-efficient ratings, behavioral nudges, and rethink brute-force compute needs
Right-size the model to the task : A task-specific model uses 20 to 30 times less energy than a general-purpose one. Distillation is key here; a full model could initially be trained from scratch and then refined for a specific task. DeepSeek R1, for instance, is “so huge that most organizations can’t afford to use it” because you need at least 8 GPUs. By contrast, distilled versions can be 10, 20 or even 30X smaller and run on a single GPU. This is the next frontier of added value. Make efficiency the default: Adopt “nudge theory” in system design, set conservative reasoning budgets, limit always-on generative features and require opt-in for high-cost compute modes. In cognitive science, “nudge theory” is a behavioral change management approach designed to influence human behavior subtly. Optimize hardware utilization: Use batching; adjust precision and fine-tune batch sizes for specific hardware generation to minimize wasted memory and power draw. Going from one batch size to plus-one can increase energy use because models need more memory bars. Incentivize energy transparency: Hugging Face earlier this year launched AI Energy Score. It’s a novel way to promote more energy efficiency, utilizing a 1- to 5-star rating system, with the most efficient models earning a “five-star” status. It could be considered the “Energy Star for AI,” and was inspired by the potentially-soon-to-be-defunct federal program, which set energy efficiency specifications and branded qualifying appliances with an Energy Star logo. Hugging Face has a leaderboard up now, which it plans to update with new models (DeepSeek, GPT-oss) and continually do so every 6 months or sooner as new models become available. The goal is that model builders will consider the rating as a “badge of honor.” Rethink the “more compute is better” mindset: Instead of chasing the largest GPU clusters, begin with the question: “What is the smartest way to achieve the result?” For many workloads, smarter architectures and better-curated data outperform brute-force scaling. Instead of simply going for the biggest clusters, enterprises have to rethink the tasks GPUs will be completing and why they need them, how they performed those types of tasks before, and what adding extra GPUs will ultimately get them.
UK government retreats from demanding Apple backdoor, preserving user encryption and safeguarding digital privacy against expansive state surveillance
The government of the UK has agreed to stop demanding Apple provide backdoor access to user data, according to the U.S. Director of Intelligence, Tulsi Gabbard. U.S. spy chief Tulsi Gabbard claimed to have worked with partners in the UK, as well as President Donald Trump and VP J.D. Vance, on the UK’s backdoor mandate. After months of work, Gabbard says that the UK has dropped the mandate affecting Apple. Gabbard adds that the UK’s attempt to get access to Apple’s customer data could have potentially given access to the data of American citizens and “encroached on our civil liberties.” Neither the U.S. nor UK governments have made any formal announcement about the matter. Given the secretive way the UK has tried to handle the matter, there may not even be any confirmation on that side of the Atlantic. Ostensibly, the UK wanted Apple to create a backdoor into its encryption, not just turn off end-to-end encryption, so that government officials could read any data they deemed necessary in criminal investigations. What the UK asked for was worldwide access, which is part of what enraged the US. Arguably what the UK really wanted was for Apple to do what it did and remove certain protections from the UK. If so, it got what it wanted, but there are ramifications. With UK data such as messaging less protected than anywhere else in the world, it’s harder for anywhere else in the world to deal with the UK. In February 2025, US Intelligence Services said that they were considering a reduction or a full stop on sharing data with the UK. It’s not yet clear if Apple will turn end-to-end encryption back on. It seems likely that it will not.
Tokenizing private equity unlocks a $15 trillion opportunity by enabling fractional, programmable ownership and broader investor access beyond 80% of retail exclusion barriers
Tokenization, a technology that has gained prominence in the cryptocurrency industry, has the potential to revolutionize finance by redefining access to capital. Currently, private markets remain less transparent, more expensive to access, and off-limits to over 80% of investors. Tokenizing private equity could remake capital formation, unlocking a massive new level of financial inclusion. Today’s system limits access to high-growth private companies to accredited investors and institutions, leaving retail investors locked out of early-stage growth opportunities. Blockchain infrastructure can represent ownership digitally and enable programmable transfers, making it possible to securely fractionalize, trade, and settle these assets without the friction of traditional intermediaries. This would lower the cost and complexity of fundraising while unlocking the door for everyday investors to participate in their growth. By the end of 2025, private markets will represent a projected $15-trillion-walled-off opportunity, dwarfing public equities’ growth potential. Enabling companies to tokenize shares before $300 million in revenues would give millions of people access to innovation-stage companies that have historically been the domain of VCs and hedge funds. Tokenization doesn’t mean throwing out safeguards; more transparency results in better outcomes, and blockchain technology offers that. Access is the ultimate asset, and tokenizing private equity could rewrite the rules of participation, opening a massive new addressable market for companies and dismantling a system where only accredited investors are trusted to take risks. It also creates a two-way unlock: startups can tap new global capital sources, and investors worldwide can participate in economic growth from day one. Tokenized private equity could be one of the biggest democratizations of wealth creation in history, shifting the center of gravity from a handful of gatekeepers to a global network of contributors.
AI agents evolve how automated customer service works- deplolying automated work assignment through ServiceNow Task Intelligence and integrated GenAI capabilities is allowing engineers to resolve problems 36% to 38% faster without forwarding calls
Scott Steele, CEO of Thrive, discusses the use of AI in contact center operations, focusing on end-user support. The company uses ServiceNow as its primary workflow engine to maintain data across various platforms, ensuring accuracy and alignment across the business. Steele emphasizes the importance of policy, governance, and management in AI strategy to ensure successful implementation. Thrive’s main uses for AI in the contact center include process management, which involves understanding bottlenecks and improving customer experience. They have deployed automated work assignment through ServiceNow Task Intelligence and integrated GenAI capabilities, allowing engineers to resolve problems 36% to 38% faster without forwarding calls. This has reduced the time spent on the phone with customers and improved the route of calls. However, some agents still want to remain call center agents and are being moved to other call centers. As the industry continues to evolve, it is expected that autonomous AI agents will take on more decision-making responsibility. Steele predicts that in five years, AI will become the exoskeleton for individuals, making them bigger, faster, and stronger. As AI becomes more digital-oriented, chat will improve, reducing the need for voice-side assistance. However, Steele acknowledges that getting away from humans 100% may be difficult. Automation has already saved hundreds of thousands of hours and improved efficiency and cost structure, making agentic AI an opportunity for businesses to drive efficiency and cost structure.