Innovations Driving AI Adoption in Enterprises

The State of AI: How Organizations Are Rewiring to Capture Value by McKinsey & Company, provides insights into how organizations are adopting and scaling generative AI (gen AI) to drive business value. Here are the key points: 1. Organizational Changes for AI Adoption Companies are redesigning workflows, elevating governance, and mitigating risks to capture value from gen AI. Larger companies (with $500M+ annual revenue) are leading the charge, with CEOs often overseeing AI governance. 21% of organizations have fundamentally redesigned workflows due to gen AI deployment. 2. Centralization of AI Deployment Organizations are selectively centralizing AI deployment, with risk and compliance, data governance, and AI strategy often centralized. Tech talent and AI solution adoption are more likely to follow a hybrid model, with some resources centralized and others distributed. 3. Risk Mitigation Organizations are increasingly addressing risks related to inaccuracy, cybersecurity, and intellectual property infringement. Larger organizations are more proactive in mitigating risks, particularly in cybersecurity and privacy. 4. Adoption and Scaling Best Practices Few organizations are fully implementing best practices for gen AI adoption and scaling. Tracking well-defined KPIs and establishing clear roadmaps for AI adoption are among the most impactful practices. Larger organizations are more likely to follow these practices, such as creating dedicated teams for AI adoption and role-based training. 5. Workforce Impact AI is shifting the skills organizations need, with high demand for data scientists, machine learning engineers, and AI compliance specialists. Many organizations are reskilling employees, and more reskilling is expected in the next three years. While some functions (e.g., service operations, supply chain) may see headcount reductions, others (e.g., software engineering, product development) may see increases. 6. AI Use and Value Creation AI use continues to climb, with 78% of organizations using AI in at least one business function, up from 55% a year earlier. Gen AI is most commonly used in marketing and sales, product development, and service operations. Organizations are seeing revenue increases and cost reductions in business units using gen AI, though enterprise-wide EBIT impact remains limited. 7. C-Level Engagement C-level executives are leading in personal use of gen AI, with 53% regularly using it at work. Executives are focusing on transformative, end-to-end solutions rather than incremental use cases. 8. Future Outlook The report emphasizes the importance of ambitious, transformative thinking for long-term competitive advantage. As AI becomes more embedded in organizations, leadership will shift focus to impact monitoring and talent development. The survey included 1,491 participants from 101 countries, representing a wide range of industries, company sizes, and regions. #AI

I was asked about Enterprise Ai Suite today… The introduction of the AMD Enterprise AI Suite is more than a product launch — it signals a major transformation in how organisations adopt, scale, and operationalise AI. For years, enterprises struggled with the same problem: AI pilots were easy. Production AI was not. That’s the gap this suite closes. 🔧 What makes it a game-changer? • End-to-end AI infrastructure — compute, orchestration, and AI frameworks combined into one enterprise-ready stack. • Pre-built inference services & solution blueprints — accelerating deployment from months to days. • Unified resource management — better GPU utilisation, predictable TCO, and efficient scaling. • Open, modular, vendor-agnostic architecture — giving enterprises flexibility without lock-in. • Production-grade governance & security — enabling private, sovereign, and regulated-industry AI deployments. 🌐 What does this mean for the industry? 1. AI becomes dramatically more accessible — even mid-sized enterprises can run advanced AI without an army of infrastructure engineers. 2. Faster time-to-value — organisations move from experimentation to real business impact much faster. 3. Rise of open ecosystems — a push away from closed, proprietary stacks toward interoperable, scalable frameworks. 4. Acceleration of sovereign AI — governments and regulated sectors can deploy AI securely, on-prem, and at scale. 5. Hardware + Software integration becomes the new norm — raising the bar for enterprise AI infrastructure. 📈 Why it matters now As AI becomes the backbone of productivity, automation, simulation, and decision-making, enterprises need reliable, scalable, cost-efficient platforms to turn ideas into outcomes. AMD’s approach brings that within reach for every sector — from manufacturing and logistics to healthcare, public services, and finance. This is the beginning of Enterprise AI 2.0: Open. Scalable. Production-ready. And designed for organisations that want to move fast — without breaking things. More details here: https://lnkd.in/ejPd98GB #AMD #EnterpriseAI #AIInfrastructure #DataCenter #AIInnovation #GPUs #AmdBrandAmbassador #Transformation #FutureOfAI #SovereignAI #AITech #HPC

𝟕𝟖% 𝐨𝐟 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞𝐬 𝐬𝐭𝐫𝐮𝐠𝐠𝐥𝐞 𝐭𝐨 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞 𝐀𝐈 𝐰𝐢𝐭𝐡 𝐥𝐞𝐠𝐚𝐜𝐲 𝐬𝐲𝐬𝐭𝐞𝐦𝐬. The problem is not the models. It’s decades of tightly coupled systems, rigid workflows, and data silos that AI was never meant to plug into. 𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐥𝐞𝐚𝐝𝐢𝐧𝐠 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞𝐬 𝐚𝐫𝐞 𝐝𝐨𝐢𝐧𝐠 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭𝐥𝐲 👇 They’re not ripping out legacy systems. They’re building smart layers around them. - Fixing data foundations before touching models - Introducing AI as a decision layer, not an execution engine - Using RAG instead of expensive fine-tuning - Orchestrating workflows without rewriting core code - Modernizing one high-impact workflow at a time - Embedding AI where teams already work - Keeping humans in the loop by default - Standardizing context, not replacing systems - Adding guardrails early to avoid chaos at scale The pattern is clear: Successful AI adoption is architectural, not experimental. AI doesn’t need new systems. It needs better integration strategies. If you’re working with legacy platforms and planning AI adoption in 2026, this mindset matters more than the model you choose. ♻️ Repost to help your network stay ahead ➕ Follow Prem N. for weekly AI insights built for business leaders, teams, and creators

🤔 As we are nearing end of 2024, it is that time when everyone looks for comparing “what really happened with enterprise AI adoption”. I read through this fascinating report from Menlo Ventures that validates many trends. The numbers are staggering - enterprise AI spending surged to $13.8B in 2024, a 6x jump from 2023! But what really caught my attention is a validation that how we have moved from experimentation to execution. Three trends particularly stand out to me: 1. The rise of AI agents is real - while most current implementations focus on augmenting human workflows, seeing early examples of autonomous AI systems managing complex end-to-end processes. - bottomline, this isn't just automation - it's transformation. 2. Technical departments still lead adoption (49% of spend), but what is exciting is seeing AI budgets flowing to every department - from Sales to HR to Legal. - this widespread adoption signals AI's transition from a tech tool to a fundamental business capability. 3. The multi-model approach is winning- organizations typically deploy 3+ foundation models in their AI stacks, choosing different models for different use cases. - interestingly, while OpenAI's share has decreased to 34%, Anthropic doubled its presence to 24% in the enterprise space. 4. RAG (retrieval-augmented generation) is dominating at 51% adoption, up from 31% last year. - but here's a surprise - only 9% of production models are fine-tuned. Real-world implementation looks different from the hype. 5. Implementation costs are the hidden gotcha- while only 1% worry about purchase price, implementation costs derailed 26% of failed pilots. 6. The incumbent advantage is cracking- while ~60% still prefer established vendors, 40% question if current solutions truly meet their needs. - that's a massive opportunity for innovative startups. 7. Vertical AI is having its moment- no surprise, this provides maximum value for highly regulated industries - healthcare is leading, followed by Financial Services. - I advocate for AI solutions tackling industry-specific workflows in regulated industries rather than just generic use cases. So, what fascinates me most? The pragmatism, really, - companies aren't fixated on price (only 1% cited it as a concern!) - they're focused on ROI and industry-specific customization. This is not just tech evolution, it is business-centric and high time for incumbents to hone in on domain strengths in solving for AI-powered transformation - get reading for 2025 🚀 And, well to me, that is a clear sign of a maturing market. 🔍 Source: "2024: The State of Generative AI in the Enterprise" by Menlo Ventures (November 2024) - https://lnkd.in/g6j-nPVp What trends are you seeing in enterprise AI adoption? Would love to hear your perspectives! #artificialintelligence #innovation #technology #reflectingonAIin2024

In this latest Forbes article, I draw a compelling line from Ada Lovelace’s 19th-century foresight to today’s AI-driven enterprise transformations. Lovelace envisioned machines augmenting human creativity—a vision now realized as #generativeAI reshapes industries. Accenture's experience with over 2,000 gen AI projects reveals that only 13% of companies achieve significant enterprise-wide value, while 36% are scaling AI for industry-specific solutions. Success in this new era hinges on more than just technology investment. Companies must also invest in their people, prioritize industry-specific AI applications, and embed responsible AI practices from the outset. Organizations adopting agentic architecture - digital teams comprising orchestrator, super, and utility agents—are 4.5 times more likely to realize enterprise-level value. Here are five key lessons we’ve learned: 1. Lead with value from the top: Executive sponsorship is crucial. Companies with CEO sponsorship achieve 2.5 times higher ROI from their #AI investments.  2. Invest in people, not just technology: Empower your workforce with the skills to harness AI. Organizations excelling in AI transformation invest in broad AI upskilling, adopt dynamic workforce models, and enable human + agent collaboration.  3. Prioritize industry-specific AI solutions: Tailor AI applications to your sector’s unique needs. Companies creating enterprise-level value are 2.9 times more likely to have a comprehensive data strategy to support their AI efforts.  4. Design and embed AI responsibly from the start: Ensure ethical and effective AI integration. Organizations creating enterprise-level value are 2.7 times more likely to have responsible AI principles and governance in place across the AI lifecycle.  5. Reinvent continuously: Stay adaptable in the face of ongoing change. Companies with advanced change capabilities are 2.1 times more likely to achieve successful transformations. These lessons should serve as a practical playbook for navigating the complexities of #AI integration and achieving sustainable growth. Please read the full article to explore how Lovelace’s visionary ideas are shaping the future of business through #generativeAI. https://lnkd.in/gEVzQeRA

McKinsey QuantumBlack's latest State of AI Global Survey just out! This is one of the best insights into the state of enterprise adoption. It shows heightened pace, but also early most companies are on the journey. Much of the report is framed around comparing "high performers" - organizations that attribute at least 20% of their EBIT to AI adoption - with others. Here are some of the most interesting findings: 🧠 AI adoption is near-universal but still shallow 88% of organizations now report regular AI use in at least one business function, up from 78% a year earlier, and 71% are using GenAI, but usage is usually in just three or so functions. 🚀 Stuck in pilot purgatory despite rapid experimentation Only 1% of executives describe GenAI rollouts as "mature" despite 71% organizational adoption, with fewer than one-third following the 12 scaling practices correlated with enterprise value. 🤖 Agentic AI is the next frontier Organizations view agentic AI as the innovation breakthrough beyond current GenAI experimentation. Over 30% of high performers are scaling AI agents in multiple functions, though this is less than 10% across all organizations. 💡 AI benefits cluster in efficiency, revenue, and innovation Cost/productivity gains are reported most often in service/ops, IT, and software. Revenue gains are in marketing, sales, and product. 64% say AI is enabling innovation in products/services/experiences. 🔄 Workflow redesign is the #1 value driver Workflow redesign has the biggest EBIT impact of 25 attributes tested, yet only 21% have fundamentally redesigned workflows and fewer than 20% track GenAI KPIs, the top-correlated practice for bottom-line results. 💰 Value realization lags far behind adoption Over 80% see no enterprise EBIT impact despite business-unit gains (63% revenue increases, majorities reporting cost reductions), with only 19% tracking KPIs and 1% calling rollouts "mature." The report also includes a list of "best practices" strongly correlated with high performers: ➡️ Human in the loop: Defined when model outputs must be checked by humans to ensure accuracy. ➡️ Technology infrastructure: Infra/architecture supports rolling out core AI with current tech. ➡️ Clearly defined AI road map: Road map of priority AI initiatives aligned with AI strategy. ➡️ Leadership alignment on value creation: Top leaders know where AI creates business value. ➡️ Rewiring business processes: AI embedded in processes, including frontline changes/UI. ➡️ Senior leadership engagement: Senior leaders actively drive and role-model AI adoption. ➡️ Product delivery: Agile org with clear, repeatable team delivery processes. ➡️ Strategic workforce planning: Workforce plan reflects AI-driven shifts in tech/nontech roles. ➡️ Iterative solution development: Standard build–improve process with guardrails for AI. ➡️ Rapid development cycles: Fast, adaptive AI efforts with quick decisions and iteration.

🔥 Hot off the press! Deloitte’s Q4 report on Generative AI in the Enterprise delivers a deep dive into adoption, scaling, and ROI. Are organizations truly unlocking the power of GenAI—or are they stuck in experimentation? Let’s look at the numbers: ✔️ 74% of advanced GenAI initiatives are meeting or exceeding ROI expectations. ✔️ Cybersecurity is leading the charge—44% of initiatives in this area surpassed ROI expectations. ✔️ 78% of enterprises plan to increase AI spending next year. ❌ Scaling remains a challenge: Over 66% of respondents say only 30% or fewer experiments will scale in the next 3–6 months. The top barriers include: Regulatory uncertainty (38%) Risk management (36%) Data quality issues (30%) 💡 What’s trending? 52% of companies are exploring Agentic AI—autonomous agents designed to accelerate value creation. Multiagent systems (45%) and multimodal capabilities (44%) are also priorities for the future. The verdict? GenAI is moving from hype to real, measurable value, but scaling success requires patience, governance, and disciplined execution. Agree?

Scaling AI Code Tooling at Enterprise Scale: Beyond the Hype & FOMO 🚀🤖💡 Deploying AI code generation across thousands of developers isn’t about chasing every shiny new feature; it’s about thoughtful, scalable implementation that delivers real value. I have discovered that actual enterprise-wide AI adoption hinges on these five critical pillars: 1. Seamless Existing IDE Integration Meet developers in their preferred and existing IDEs, don’t force a change of workflow. Embedding AI where teams already work maximises adoption. 2. Context Management Go beyond simple relevance tuning by focusing on robust context management. AI tooling must understand the developer’s immediate coding context, project history, and enterprise-specific patterns to minimise noise and maintain developer flow and productivity. 3. Structured Enablement Programs Roll out enablement programs with clear support channels so all 2,000+ developers can extract genuine value, not just experiment. Empower teams with training, documentation, and a fast feedback loop. 4. Enterprise-Grade Security, AI Governance & IP Protection Security isn’t just a checkbox. We embed cybersecurity, AI governance, and intellectual property safeguards into every layer, from robust data privacy and continuous monitoring to clear IP ownership and compliance. By handling these critical aspects centrally, we free our developers to focus on building great software. They don’t have to worry about security or compliance, as it’s built in! 5. Comprehensive Metrics Frameworks Measure what matters: completion rates, bug reduction, and time saved. Leveraging tools like the DX AI Measurement Framework has proven potent, providing deep and actionable insights into how AI code tooling impacts developer experience and productivity. These frameworks enable us to track real ROI, identify areas for improvement, and continuously refine our approach to maximise value. Successful adoption comes not from FOMO-driven adoption of every new AI feature but from consistent, pragmatic implementation that truly enhances developer productivity at scale. #ai #EnterpriseAI #DevEx #AICodeGeneration #TescoTechnology #Engineering #ArtificialIntelligence #DeveloperExperience

The thing that’s missing with AI adoption in enterprises: AI is a general-purpose technology. It’s akin to electricity but more specifically, it’s akin to human intelligence. The common approach to adopting AI is to treat it like an app. This mindset stems from the SaaS revolution, which popularised the idea that the solution to every problem was: ‘There’s an app for that.’ This is not the way to adopt AI. Here’s where Fred Taylor comes in: Fred was the pioneer of ‘Scientific Management,’ which laid the foundation for organising human intelligence to create the modern world. Scientific Management, combined with technological innovations such as the assembly line and the steam engine, enabled the mass manufacturing of nearly every household product we use today. The point is that new technology requires new ways of working. For Fred Taylor and Henry Ford, it was Scientific Management. For software development, it was Agile and Scrum. AI requires the same forward-thinking approach. Every business needs to recognise that this new technology necessitates new ways of working. What works well for AI adoption is now evident: - Treat it as an organisational change, not a technology change. - Extract ideas from the ground up; business users hold the keys to high-value ideas. - Focus on the micro—deliver value quickly and build AI capability and literacy to inform larger-scale wins. - Don’t buy new apps; focus on integrating AI-powered workflows into existing systems and processes. The path to success with AI is now well established. The question is, who will capitalise on that path first in your industry?

In the two years since ChatGPT's release catalyzed generative AI's Cambrian explosion, enterprise spend in the category has surged to $13.8 billion -- up more than 6x from $2.3 billion last year. In Menlo Ventures' 2024 State of Generative AI Report, my partners Tim Tully, Joff Redfern, and I surveyed 600 enterprise IT decision-makers to document the scope and scale of the transformation. Our second annual report found that: 1/ Generative AI has found screaming product-market fit in its first few breakout use cases: 🥇 Code copilots (51% adoption) - e.g., All Hands AI, Codeium, Harness 🥈 Support chatbots (31%) - e.g., Aisera, Decagon, Sierra 🥉 Enterprise search (28%) - e.g., Glean, Sana 2/ The foundation model landscape is shifting: Buoyed by the release of state-of-the-art models like Claude Opus, Sonnet, and Haiku, Anthropic doubled its enterprise share from 12% to 24% while OpenAI slipped from 50% to 34%. Closed-source models remained dominant vs open-source models (e.g., Llama) with 81% market share. 3/ Whatever your department, there's an app for that. Generative AI budgets are coming from every part of the organization: 🤝 Sales - Clay, Unify 📢 Marketing - Typeface, OfferFit 👔 HR - ConverzAI 💵 Accounting & finance - Numeric 4/ Vertical AI applications are especially gaining momentum. Companies like Abridge in healthcare and Casetext, Part of Thomson Reuters and Harvey in legal have already become the talk of the industry. The leading adopters today are: ⚕ Healthcare - $500M in genAI spend ⚖ Legal - $350M 🏦 Financial services - $100M 📽 Media & entertainment - $100M 5/ In the modern AI stack, RAG (retrieval-augmented generation) has dethroned simple prompting as the primary design pattern for AI apps, powering 51% of implementations (up from 31% last year) and driving the adoption of key infrastructure building blocks like Pinecone, unstructured.io, and Neon. Meanwhile, agentic designs are just emerging, already driving 12% of deployments. All this and more in our full report. Check it out: https://lnkd.in/gByCqFMB