Emerging Technology Adoption Hurdles

AI Adoption: Reality Bites After speaking with customers across various industries yesterday, one thing became crystal clear: there's a significant gap between AI hype and implementation reality. While pundits on X buzz about autonomous agents and sweeping automation, business leaders I spoke with are struggling with fundamentals: getting legal approval, navigating procurement processes, and addressing privacy, security, and governance concerns. What's more revealing is the counterintuitive truth emerging: organizations with the most robust digital transformation experience are often facing greater AI adoption friction. Their established governance structures—originally designed to protect—now create labyrinthine approval processes that nimbler competitors can sidestep. For product leaders, the opportunity lies not in selling technical capability, but in designing for organizational adoption pathways. Consider: - Prioritize modular implementations that can pass through governance checkpoints incrementally rather than requiring all-or-nothing approvals - Create "governance-as-code" frameworks that embed compliance requirements directly into product architecture - Develop value metrics that measure time-to-implementation, not just end-state ROI - Lean into understanability and transparency as part of your value prop - Build solutions that address the career risk stakeholders face when championing AI initiatives For business leaders, it's critical to internalize that the most successful AI implementations will come not from the organizations with the most advanced technology, but those who reinvent adoption processes themselves. Those who recognize AI requires governance innovation—not just technical innovation—will unlock sustainable value while others remain trapped in endless proof-of-concept cycles. What unexpected adoption hurdles are you encountering in your organization? I'd love to hear perspectives beyond the usual technical challenges.

Chief AI Officers and other tech leaders reveal challenges…. I recently moderated roundtable discussions with over 125 Chief AI officers and leaders responsible for AI across both regulated and unregulated industries. A few key themes surfaced around the barriers to successful AI adoption: • Budget constraints and demonstrating clear ROI • Executive buy-in: Leadership alignment remains a major hurdle • Setting realistic expectations: AI is not an overnight solution, but a long-term strategy • Employee fear: Concerns about AI’s impact on jobs create resistance • Data: Access, quality, and governance issues continue to slow progress • Governance and regulatory compliance: Navigating the complex landscape of rules and regulations presents additional challenges • Hype vs. reality: There is a lot of AI hype to combat, and managing expectations around what AI can truly deliver is essential It’s clear that the job for chief AI officers, CTOs, and others leading these efforts is extremely challenging, requiring a delicate balance of technical knowledge, leadership, and strategy. Despite these obstacles, the energy and innovation in the AI space are undeniable. What did we miss? #AIAdoption #ChiefAIOfficer #ArtificialIntelligence #AILeadership #EthicalAI #TechLeadership #AIInBusiness #AIInnovation #AIRegulation #DataGovernance #ExecutiveBuyIn #FutureOfAI #AITransformation #AIChallenges #AIForGood

UAE's #blockchain guide outlines several significant challenges facing the widespread adoption of blockchain technology. These challenges include: •Education and Capabilities: There is a lack of fundamental understanding of how blockchain works beyond its association with #cryptocurrency. This lack of awareness extends to lawmakers, hindering the development of constructive regulations. Furthermore, there is a shortage of talented enterprise-level blockchain software developers and a need for proper training programs at various levels. •Interoperability: A major concern arises from the multitude of different blockchain systems that exist, often using different languages, platforms, consensus mechanisms, and protocol schemes. The lack of a standard to ensure compatibility and harmonious operation between these different blockchains poses a significant challenge to the technology's development and adoption . This disconnection can lead to confusion and hesitation among decision-makers. •Scalability: Creating blockchain platforms that can adapt to the growing needs of companies and governments remains a critical challenge. Issues related to implementation, cost, and employee training need to be considered. The inherent technological challenge lies in the fact that every transaction adds a new block, increasing the blockchain's size and potentially leading to performance bottlenecks. Current systems like Bitcoin have significantly lower transaction processing capacities compared to traditional systems like Visa. While solutions like Sharding and off-chain transactions are being explored, no perfect solution currently exists. •Regulatory Clarity: The borderless nature of blockchain networks clashes with the lack of consistent regulatory clarity and differences between jurisdictions. As technology advances faster than regulations, risks and uncertainties persist. Many regulators lack a comprehensive understanding of blockchain and cryptocurrencies, hindering the application of cohesive regulatory approaches. The current cryptocurrency regulations are often inconclusive and scattered, with no unified international standards for cryptocurrencies or data ownership. The UAE's efforts with WEF to establish global standards are a positive step towards addressing this challenge . •Governance: Establishing policies and continuously monitoring their implementation within a blockchain network is complex, especially since it's a relatively new technology with no established "best recipe". The diverging interests of a network's stakeholders as they interact with and derive value from the network further complicate governance. Governments and industries need to be prepared to address change in a way that benefits all stakeholders without compromising the network. This includes decisions on consensus protocol changes, rules for network participation, block size adjustments, and the adoption of off-chain solutions10

Quantum Computing’s Roadblocks: The 3 Barriers Holding Back the Revolution ⸻ Why Quantum Isn’t Mainstream—Yet Quantum computing promises to revolutionize industries—from drug discovery to AI—by solving problems conventional computers can’t touch. Yet despite the buzz, practical quantum computing is not widely adopted. The reason? The field still faces three major barriers—technical, societal, and infrastructural—that must be overcome before it can fulfill its transformative potential. ⸻ The Three Major Barriers to Adoption 1. Technical Complexity • Qubit Stability: Qubits are highly sensitive to their environment and can lose coherence (i.e., stability) after mere milliseconds. • Error Rates: Even short computations often introduce significant errors, making output unreliable. • Scalability: While small-scale quantum devices exist, scaling them to thousands or millions of qubits with sufficient fidelity is a massive engineering challenge. 2. Security and Privacy Risks • Quantum Threat to Encryption: Once quantum computers are powerful enough, they could break today’s encryption standards—posing risks to global cybersecurity. • Need for Quantum-Safe Protocols: Organizations must invest now in post-quantum cryptography to protect long-term sensitive data. 3. Societal and Economic Integration • Workforce Gap: Few engineers and scientists are trained in quantum computing, creating a bottleneck for growth. • Infrastructure and Cost: Quantum computers often require ultra-low temperatures and specialized environments, making them expensive to develop and maintain. • Ethical and Regulatory Uncertainty: Societal impacts—such as AI acceleration and surveillance—raise questions that lack regulatory clarity. ⸻ Why It Matters: Timing the Leap For businesses and governments, the quantum era is not a question of “if,” but “when.” The race is on to develop applications and frameworks that will thrive once the barriers fall. Early movers who understand these challenges—and prepare accordingly—stand to gain outsized competitive advantages. Moreover, investments in workforce training, secure infrastructure, and ethical frameworks now will pay dividends as quantum breakthroughs emerge. The companies and countries best prepared for the coming quantum shift will define the future of technology, economics, and geopolitics. https://lnkd.in/gEmHdXZy

Last week, a customer said something that stopped me in my tracks: “Our data is what makes us unique. If we share it with an AI model, it may play against us.” This customer recognizes the transformative power of AI. They understand that their data holds the key to unlocking that potential. But they also see risks alongside the opportunities—and those risks can’t be ignored. The truth is, technology is advancing faster than many businesses feel ready to adopt it. Bridging that gap between innovation and trust will be critical for unlocking AI’s full potential. So, how do we do that? It comes down understanding, acknowledging and addressing the barriers to AI adoption facing SMBs today: 1. Inflated expectations Companies are promised that AI will revolutionize their business. But when they adopt new AI tools, the reality falls short. Many use cases feel novel, not necessary. And that leads to low repeat usage and high skepticism. For scaling companies with limited resources and big ambitions, AI needs to deliver real value – not just hype. 2. Complex setups Many AI solutions are too complex, requiring armies of consultants to build and train custom tools. That might be ok if you’re a large enterprise. But for everyone else it’s a barrier to getting started, let alone driving adoption. SMBs need AI that works out of the box and integrates seamlessly into the flow of work – from the start. 3. Data privacy concerns Remember the quote I shared earlier? SMBs worry their proprietary data could be exposed and even used against them by competitors. Sharing data with AI tools feels too risky (especially tools that rely on third-party platforms). And that’s a barrier to usage. AI adoption starts with trust, and SMBs need absolute confidence that their data is secure – no exceptions. If 2024 was the year when SMBs saw AI’s potential from afar, 2025 will be the year when they unlock that potential for themselves. That starts by tackling barriers to AI adoption with products that provide immediate value, not inflated hype. Products that offer simplicity, not complexity (or consultants!). Products with security that’s rigorous, not risky. That’s what we’re building at HubSpot, and I’m excited to see what scaling companies do with the full potential of AI at their fingertips this year!

My recent research, which examines the adoption of emerging technologies through a gender lens, illuminates continued disparities in women's experiences with Generative AI. Day after day we continue to hear about the ways GenAI will change how we work, the types of jobs that will be needed, and how it will enhance our productivity, but are these benefits equally accessible to everyone? My research suggests otherwise, particularly for women. 🕰️ The Time Crunch: Women, especially those juggling careers with care responsibilities, are facing a significant time deficit. Across the globe women spend up to twice as much time as men on care and household duties, resulting in women not having the luxury of time to upskill in GenAI technologies. This "second shift" at home is increasing an already wide divide. 💻 Tech Access Gap: Beyond time constraints, many women face limited access to the necessary technology to engage with GenAI effectively. This isn't just about owning a computer - it's about having consistent, uninterrupted access to high-speed internet and up-to-date hardware capable of running advanced AI tools. According to the GSMA, women in low- and middle-income countries are 20% less likely than men to own a smartphone and 49% less likely to use mobile internet. 🚀 Career Advancement Hurdles: The combination of time poverty and tech access limitations is creating a perfect storm. As GenAI skills become increasingly expected in the workplace, women risk falling further behind in career advancement opportunities and pay. This is especially an issue in tech-related fields and leadership positions. Women account for only about 25% of engineers working in AI, and less than 20% of speakers at AI conferences are women. 🔍 Applying a Gender Lens: By viewing this issue through a gender lens, we can see that the rapid advancement of GenAI threatens to exacerbate existing inequalities. It's not enough to create powerful AI tools; we must ensure equitable access and opportunity to leverage these tools. 📈 Moving Forward: To address this growing divide, we need targeted interventions: Flexible, asynchronous training programs that accommodate varied schedules Initiatives to improve tech access in underserved communities. Workplace policies that recognize and support employees with caregiving responsibilities. Mentorship programs specifically designed to support women in acquiring GenAI skills. There is great potential with GenAI, but also risk of leaving half our workforce behind. It's time for tech companies, employers, and policymakers to recognize and address these gender-specific barriers. Please share initiatives or ideas you have for making GenAI more inclusive and accessible for everyone. #GenderEquity #GenAI #WomenInTech #InclusiveAI #WorkplaceEquality

Some very interesting insights in this MIT Technology Review survey report on ‘Moving generative AI into production’. 💡 Generative AI Enthusiasm but Challenges and Disappointment. The vast majority of organizations (91%) expect generative AI applications to increase their productivity, however deployment remains slow. In Q3 2023, 79% of companies said they planned to deploy generative AI projects in the next year, but only 5% reported having use cases in production in May 2024. Two-thirds of business leaders are ambivalent or dissatisfied with progress on their AI deployments. 🚧 Key Barriers Slow Deployment. Businesses face significant hurdles in deploying generative AI, including poor output quality (72%), integration complexity (62%), and high costs for training (58%) and inferencing (58%). Overcoming these obstacles requires a strategic focus on building robust, adaptable AI stacks that address these technical and financial challenges directly. 🔄 Compound AI Systems Offer Scalable Solutions. Compound AI systems, which link specialized models and technologies, are emerging as a way to reduce costs and improve performance. Techniques like retrieval-augmented generation (RAG) and semantic caching enhance efficiency by optimizing data retrieval and reducing duplicate queries, cutting costs and latency. Over 50% of businesses are already using multi-step AI chains or planning to adopt them. 🚀 Open-Source Models and Flexible Stacks Are on the Rise. Companies increasingly favor open-source models, with 42% using cloud-based versions and 17% adopting on-prem solutions. Flexibility is key, as integrating different models and technologies allows businesses to adapt to evolving needs while controlling costs and enhancing security. APIs and tools like LangChain facilitate seamless model switching to maximize efficiency. 📉 Latency and Cost: Key Barriers to Real-Time AI Experiences. Latency has become a critical issue, with 56% of businesses citing it as a challenge. Complex AI stacks exacerbate this issue, as each additional model adds delay. Solutions such as semantic caching and high-speed data platforms help reduce lag and improve user experiences, but businesses must balance speed with cost. 📊 Quantifying ROI Remains Elusive but Crucial. Businesses struggle to measure the ROI of generative AI, with 48% relying on KPIs and 38% using bespoke frameworks. This uncertainty hinders broader adoption and investment. Clearer methodologies for assessing AI’s impact on productivity and revenue will be essential for scaling generative AI across industries. Link to report in comments.

In the world of commercial adoption, Artificial Intelligence has ignited enthusiasm and anticipation for revolutionary changes. However, beyond the hype, substantial challenges demand our attention. This article delves into these obstacles and underscores the significance of adopting a comprehensive approach. Quality Data Matters: AI thrives on pristine data. Data quality issues are a common stumbling block, as the effectiveness of AI models is intimately tied to the quality of the data they rely on. Building Organizational Capabilities: Integrating AI into an organization's culture and processes is a complex endeavor. It demands not just technological prowess but also a cultural shift within the organization. Navigating Employee Resistance: Employees may harbor concerns about AI threatening their job security. Managing the relationship between human workers and AI involves intricate considerations, from compensation to roles. Ethical Frontiers: AI brings forth intricate ethical questions. Determining accountability for AI decisions, addressing data privacy concerns, setting standards, and managing national security implications are vital components of AI adoption. AI isn't merely a technology; it's a platform that requires a comprehensive strategy. Success lies in a multifaceted approach. A steadfast, long-term commitment is essential to conquer these complexities. AI's potential is boundless, but so are the challenges – proceed with care, and you'll unlock its rewards. #ai

Researchers at the University of Victoria identified 7 obstacles to adopting GenAI tools in engineering organizations: 1. Fear of decreased skills. Developers worry about over-reliance on AI tools and the loss of learning opportunities. 2. Limited AI capabilities. AI tools often lack awareness of the operational environment and codebase, which limits their effectiveness. 3. Lack of prompting skill. Developers need to experiment with AI tools to get desired results, leading to potential frustration and decreased usage. 4. Potential judgment from others. Some fear being judged by peers for using AI tools, and this can hinder their adoption. 5. Not having a culture of sharing. Lacking a supportive culture for sharing AI tool practices can slow adoption.  6. Cost of tools. High costs and limited access to AI tools can be a barrier. 7. Lack of guidelines. Without clear guidelines and training, developers may struggle with how to use AI tools effectively. Addressing these challenges can improve the adoption and effective use of GenAI tools in engineering organizations. Read more findings from this study in today’s newsletter:

AI adoption isn’t just about technology. It’s about leadership. Many leaders want AI but struggle to drive change. They face resistance, ethics concerns, and unclear ROI. Here’s how leaders can overcome AI challenges: 1 - Lack of AI Expertise Leaders feel unprepared for AI decisions. Invest in AI literacy and expert guidance. 2 - Resistance to Change Teams fear AI will replace jobs. Communicate benefits and involve employees early. 3 - Integration with Existing Systems Legacy systems create adoption hurdles. Start small and phase AI into workflows. 4 - Managing Ethical Concerns Bias and transparency issues arise. Set AI guidelines and run regular audits. 5 - Balancing Innovation with ROI Short-term wins can slow long-term growth. Set clear goals and focus on scalability. 6 - Building a Collaborative AI Culture Silos slow AI adoption across teams. Foster alignment and celebrate progress. 7 - Navigating Rapid AI Advancements AI evolves faster than most businesses. Stay updated and invest in learning. Great AI leadership isn’t about knowing everything. It’s about creating a culture that embraces change. Found this helpful? Follow Arturo and repost