Challenges Enterprises Face With Genai Integration

Today's update on #GenAI adoption and enterprise #scaling raises some critical issues around infrastructure and cybersecurity that often get ignored in the shiny glitz of ever-evolving foundation models and their fancy valuations. --- AI Enterprise Scaling: The Infrastructure Reality Check Beneath the hype, the foundational pillars of enterprise AI—infrastructure, strategy, and security—are cracking under the strain of real-world deployment, preventing organizations from capturing promised value. Update 1: The Infrastructure Preparedness Crisis Challenge: Critical infrastructure gaps are leaving enterprises unprepared for AI workloads. Details: A Cisco analysis reveals only 13% of enterprises are fully prepared to support AI at scale. The issue is not a lack of ambition but a fundamental architectural mismatch; most data centers were not designed for the GPU-dense, data-hungry pipelines that demand high-throughput, low-latency traffic across heterogeneous stacks. Source: https://lnkd.in/gCUDVtV4 Update 2: The Strategic ROI Disconnect Challenge: A massive perception gap on AI strategy is undermining ROI. Details: Research shows that while 73% of executives believe their AI approach is strategic, only 47% of the workforce agrees. This disconnect suggests enterprises are misapplying AI to "old" problems instead of targeting the "dark" business processes where automation can unlock true value—the historically invisible, manual workflows. Source: https://lnkd.in/gUbTuJtR Update 3: Security Governance in the Dark Challenge: Pervasive visibility and control gaps are exposing firms to major AI-driven risks. Details: A staggering 90% of enterprises are unprepared for AI-driven cyberattacks. This is compounded by the fact that only 21% of organizations have visibility into all AI tools being used, and 77% lack AI-specific security practices to protect their models, pipelines, and data from compromise. Source: https://lnkd.in/graipKgU Key Takeaway The path to scalable AI is not paved with better models, but with foundational redesigns of infrastructure, strategy, and security to match the complex operational reality of the enterprise. --- In my upcoming book on Cognitive Chasm, I build upon my research by addressing the "how" of GenAI adoption, i.e., how could the enterprises systematically adopt GenAI and avoid falling into the #cognitivechasm that seems to be rampant in the industry, and "95% failure rate" seems to have been accepted as the de facto constant of cognitive adoption! As I often joke in my talks, most industries, and not just companies, would get outlawed if they even had a 20% failure rate. Think of an airline that says 20% of our flights don't land or reach some other destination!....will you ever travel with them?

Your GenAI pilot worked perfectly. That success may be masking a hidden risk. Because a successful demo creates momentum. Momentum can make teams want to skip steps. And skipped steps are where enterprise AI initiatives stall. After helping Fortune 500 companies move GenAI from pilot to production, here's what we keep seeing: 𝗔 𝗰𝗼𝗺𝗺𝗼𝗻 𝗿𝗲𝗮𝘀𝗼𝗻 𝗚𝗲𝗻𝗔𝗜 𝗶𝗻𝗶𝘁𝗶𝗮𝘁𝗶𝘃𝗲𝘀 𝘀𝘁𝗮𝗹𝗹? 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗴𝗮𝗽𝘀. Often it's not model accuracy or hallucinations. It's process and oversight. A common pattern: GenAI projects stall at the pilot stage because governance structures weren't built alongside the technology. Here are 5 warning signs your initiative is in trouble: • No clear ownership connecting AI outcomes to business results • Projects jump from POC to production with no documented go/no-go criteria • Success is defined as "we'll know it when we see it" • Legal and compliance review is scheduled for "later" • One vendor, no exit plan, no fallback architecture 𝗧𝗵𝗲 𝗴𝗼𝗼𝗱 𝗻𝗲𝘄𝘀? These failures are often preventable with the right structure. Organizations that treat GenAI as a 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆-𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗲𝘅𝗲𝗿𝗰𝗶𝘀𝗲 instead of a technology procurement tend to see stronger outcomes. That means: ✓ Stage-gate frameworks with clear decision points ✓ Evaluation that goes beyond lab benchmarks ✓ Operational readiness before launch, not after ✓ Talent strategies that value domain expertise over "AI engineer" titles ✓ Runbooks and rollback/fallback plans documented in advance The path forward isn't more caution or more speed. It's more 𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲. We published a full analysis covering failure taxonomies, stage-gate frameworks, operational checklists, and build/buy/partner/wait decision models. Link in comments. Want a quick self-assessment? I'll drop a one-page GenAI Governance Checklist in the comments. Feel free to use it with your team. What's the biggest obstacle you've seen in getting GenAI from pilot to production? Read the full article here: https://lnkd.in/g-VdCuNF #GenAI #AIGovernance #EnterpriseAI #Leadership #AIStrategy

The GenAI Divide: 7 data-driven insights for AI success according to the MIT. AI is failing due to poor execution, not poor technology. This is a reality for most organizations today. Despite billions invested in Generative AI, the gap between promise and profit remains wide. According to Massachusetts Institute of Technology's Project Nanda, the challenge lies in the implementation. Here are 7 data-driven insights that can help decision-makers bridge the GenAI divide: 1. Most GenAI investments aren’t paying off. Despite $30-40 billion in enterprise investment, 95% of organizations report zero measurable return. Only 5% of integrated AI pilots are extracting millions in value, while the vast majority remain stuck with no measurable P&L impact. 2. The core problem is a "learning gap," not a technology gap. The primary factor keeping organizations stuck is that most GenAI solutions are poorly designed: they do not retain feedback, adapt to context, or improve over time. For complex projects, users prefer a human colleague over AI by a 9-to-1 margin, signaling a profound lack of trust for mission-critical work. 3. Value is being created outside official channels. A “shadow AI economy” is thriving. Over 90% of employees use personal AI tools at work, even though only 40% of companies have official subscriptions. These tools boost individual productivity, but rarely move the needle on P&L. 4. Technology partners dramatically outperform internal teams. Pilots built with experienced technology partners are 2x as likely to reach full deployment. Organizations that partner externally see a 67% success rate, compared to just 33% for internal development efforts. 5. Investment is misaligned: back-office automation delivers the highest ROI. While most GenAI budgets target Sales and Marketing, the biggest cost savings come from automating back-office functions — think $2-10M in annual BPO savings and a 30% reduction in agency spend. 6. Customization and integration are non-negotiable. Executives prioritize vendors who understand their workflows and propose solutions that integrate seamlessly with existing tools. If a GenAI solution disrupts daily operations, adoption will stall. 7. The window for competitive advantage is closing fast. Only Tech and Media have seen real structural change so far, but that won’t last. Over the next 18 months, more enterprises will form strategic partnerships to drive cognitive transformation — and those who move first will win. The full MIT Project Nanda report is available in the PDF attached. What’s the biggest challenge your organization faces in turning GenAI investment into measurable impact? Where have you seen success (or frustration) on this journey? If these insights resonate, feel free to share. #AIFirst

When MIT released its latest report on generative AI adoption, the takeaways weren’t surprising. Despite all the headlines about large language models (LLMs), enterprise adoption is less about model power and more about embedding AI into business processes. Both the report and our own observations suggest this mismatch explains why many companies remain stuck at POCs. Shadow AI vs. Enterprise AI This tension is clearest in “shadow AI.” Enterprise users default to consumer models when official tools lack flexibility. One respondent put it simply: “Our purchased AI tool provided rigid summaries with limited customization.” MIT’s findings echo what we see: AI tools built on the legacy SaaS playbook - quick onboarding, low customization, little support - don’t survive where workflows are deeply specific. For enterprises, AI’s value lies less in raw capability and more in how well it fits existing processes. SaaS vs. Services GenAI startups often squeeze into the SaaS mold. Subscription pricing and minimal support worked for cloud software - but not here. As MIT notes: “Some GenAI startups struggle with outdated SaaS playbooks, while others win by customizing aggressively and aligning to real pain points.” Enterprises don’t buy “categories.” They buy outcomes. If delivering outcomes means heavier integration and bespoke handholding, so be it. Front office vs. back office The first GenAI wave hit sales/marketing - budgets were easy to justify, upside tied to revenue. MIT shows ~70% of AI budgets skew here. But in sales, AI-driven scale - automated emails, prospecting bots, SDRs - faces diminishing returns. As everyone adopts them, advantages erode into noise. The more durable opportunity may be in the back office - harder to automate, but solving it reduces real effort. Build or buy? GenAI tools are easy to prototype but hard to productionize. Any tech team can spin up a POC in weeks. Moving to production needs sustained engineering, data/process tuning, and evaluation. MIT notes external partnerships deliver 2x the success rate over internal builds. Vendors who integrate and specialize are better placed to bridge the demo-to-deployment gap. The bigger picture Enterprise adoption is already showing fault lines. SaaS-like uniformity collides with business-specific workflows. DIY POCs rarely scale. Trajectory is clear: For GenAI to move beyond POCs, it must act more like a service and go deeper into business process. Most enterprises don’t care about models or categories, they care about results. Srinivas K Sirish Kosaraju Oscar D'Coutho Aksharkumar Hegde Sheen K Shahjahan T

Everyone is rushing to adopt Generative AI and they should — because companies that don’t build a GenAI strategy today 𝐰𝐢𝐥𝐥 𝐟𝐚𝐥𝐥 𝐛𝐞𝐡𝐢𝐧𝐝 𝐭𝐡𝐞𝐢𝐫 𝐜𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐢𝐨𝐧. But there’s a mistake many organizations are making right now. In the rush to democratize AI, companies are putting GenAI tools into the hands of everyone without first building the 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞, 𝐠𝐮𝐚𝐫𝐝𝐫𝐚𝐢𝐥𝐬, 𝐚𝐧𝐝 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐝 𝐭𝐨 𝐮𝐬𝐞 𝐢𝐭 𝐞𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞𝐥𝐲. When people who don’t understand how these systems work behind the scenes start applying them everywhere, the outcome is predictable: • Failed experiments • Frustrated teams • Finger pointing between business and technology • Loss of trust in AI initiatives Another blind spot: 𝐓𝐡𝐞 𝐦𝐚𝐭𝐡 𝐛𝐞𝐡𝐢𝐧𝐝 𝐆𝐞𝐧𝐀𝐈 𝐜𝐨𝐬𝐭𝐬. Unlike traditional enterprise software, GenAI runs on usage — tokens, prompts, model calls, and compute. Without proper architecture and cost governance, what starts as innovation can quickly turn into 𝐮𝐧𝐬𝐮𝐬𝐭𝐚𝐢𝐧𝐚𝐛𝐥𝐞 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐞𝐱𝐩𝐞𝐧𝐬𝐞𝐬. The organizations that will truly win with GenAI are not just the fastest adopters. They are the ones that: ✓ Build a clear AI strategy tied to business value ✓ Establish governance and guardrails early ✓ Educate teams on how GenAI actually works ✓ Control early adoption before scaling broadly ✓ Track and optimize cost from day one GenAI is transformational but like any powerful technology, 𝐝𝐢𝐬𝐜𝐢𝐩𝐥𝐢𝐧𝐞 𝐢𝐧 𝐭𝐡𝐞 𝐞𝐚𝐫𝐥𝐲 𝐬𝐭𝐚𝐠𝐞𝐬 𝐝𝐞𝐭𝐞𝐫𝐦𝐢𝐧𝐞𝐬 𝐥𝐨𝐧𝐠-𝐭𝐞𝐫𝐦 𝐬𝐮𝐜𝐜𝐞𝐬𝐬. #adoption #genai #artificialintelligence #enterpriseai

95% of enterprise #GenAI pilot projects are failing. Eyeball grabbing headline, and even caused some turmoil in the market. I found the report (attached). While it offers valuable insights, I believe its narrative warrants a more balanced view. The study's small sample size and methodology of public results raise questions about broad applicability. It’s important to separate technology failure from organizational challenges, which the report rightfully highlights as the primary barriers. Key takeaway? 1. Massive investment, minimal return: Companies have collectively spent $30–40 billion on Gen AI, but only 5% of pilots have made it into production. 2. The "#GenAIDivide": The report coins this term to describe the stark split between the small number of successful AI initiatives and the vast majority that have stalled. 3. #Pilotpurgatory: A huge number of projects remain stuck in the pilot phase, unable to scale or demonstrate a positive impact on P&L. 4. A "#LearningGap": The core problem is not insufficient infrastructure or talent, but rather the inability of many current AI systems to adapt and learn over time within complex enterprise workflows. The real story isn’t that AI tools underperform - individual usage of LLMs and AI tools is widespread, with 90% of employees using them regularly to boost productivity. The bigger issue is, only 40% of organizations have officially adopted or purchased AI solutions for their teams. This glaring gap points to change management deficiencies, and misalignment between AI capabilities and day-to-day workflows as critical obstacles. The purgatory or gap creates hidden costs, such as the “verification tax,” where employees spend time validating AI outputs, neutralizing expected efficiency gains. So, what can organizations do: 1. Focus on organizational readiness: Invest in change management and executive sponsorship to foster AI adoption. 2. Prioritize workflow integration and adaptive AI tools that learn from feedback and context. 3. Leverage external partnerships instead of building everything in-house to accelerate deployment. 4. Empower frontline teams to pilot and refine AI use cases for faster, measurable impact. My personal experience is: #AITechnology is ready and delivering value; and organizations are adapting their culture and processes to cross the GenAI Divide. And I am excited about being on the journey of adopting Gen AI at scale.

The Reason 95% of GenAI Pilots Fail Has Nothing to Do With the Technology It’s how it’s being sold into businesses by vendors and consultants. Time to stop panicking and regain our senses. Over the past two years, Generative AI has been marketed as the ultimate automation tool. The promise is seductive: replace workers, cut costs, boost margins. This is actually a hangover from the industrial revolution and Taylorism; this is where consultants don't envision technology as an augmentation tool because that requires vision, but see it as an automation process optimization tool GenAI is not good at that. It requires partnership with humans (for one thing it's way too error prone and unreliable to replace anyone) But CFOs love it because it looks like it will immediately increase ROI and improves operating margin. CEOs love it (not anymore) because they can stand in front of their Boards and say, “We’re AI-First,” announce layoffs, and issue a press release their consultants wrote for them. It sounds compelling. But it fails almost every time. The Data A new MIT Sloan study found that 95% of in-house GenAI pilots fail to generate measurable ROI【1】. McKinsey reports that while 72% of companies are experimenting with GenAI, only 11% have scaled it across the enterprise【2】. Gartner estimates that 80% of AI projects never make it past the pilot stage【3】. Deloitte shows that the barriers are not technical but organizational: lack of leadership vision, siloed execution, and unrealistic cost-savings expectations【4】. The Problem When leaders view GenAI as a quick cost-savings project, they doom it to failure. The Solution The CEO-leads the vision. GenAI transformation must come from the top. The CEO cannot outsource it. The CEO drives thought leadership and takes control It needs the support of the organization to work or people will sabotage it and politics will destroy it (cross-functional infighting) Think augmentation, not replacement. Position GenAI as a productivity amplifier, not a headcount reduction tool. Adopt a multi-LLM ecosystem. Use open + closed, ML + GenAI, to balance risk, cost, and innovation. Not one closed source model. Secure cross-functional buy-in. Legal, HR, compliance, operations, and marketing must all be at the table. Generative AI isn’t failing because the models are weak. It’s failing because companies were sold the wrong story, cost-savings instead of transformation, press releases instead of purpose, power points instead of augmentation. Now is the time to get it right because this technology is amazing when implemented correctly and can turn any company into a rocket ******************************************************************************** The trick with technology is to avoid spreading darkness at the speed of light I’m the Founder & CEO of Curiouser.AI. I also teach at Berkeley. (FOOTNOTES IN COMMENTS)

The 𝗠𝗜𝗧 "𝗚𝗲𝗻𝗔𝗜 𝗗𝗶𝘃𝗶𝗱𝗲" 𝗿𝗲𝗽𝗼𝗿𝘁 confirms what I've learned deploying AI at Boomi: 𝟵𝟱% 𝗼𝗳 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗽𝗶𝗹𝗼𝘁𝘀 𝗳𝗮𝗶𝗹—but not for the reasons you think. After 18 months building AI solutions for our internal teams, one pattern is clear: successful GenAI isn't about model sophistication. It's about organizational readiness. The 5% who succeed share three characteristics: 1️⃣ 𝗧𝗵𝗲𝘆 𝘀𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘂𝗻𝘀𝗲𝘅𝘆 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 Our recent MQL Agent win wasn't a flashy customer-facing chatbot. It involves automating inbound lead routing and engagement with mostly deterministic workflows —cutting response time from 55 hours to 5 minutes. Same Salesforce instance, same team, but AI creating and orchestrating the messaging and handoffs with Boomi Integration in the middle. 2️⃣ 𝗧𝗵𝗲𝘆 𝘁𝗿𝗲𝗮𝘁 𝗔𝗜 𝗮𝘀 𝗺𝗶𝗱𝗱𝗹𝗲𝘄𝗮𝗿𝗲, 𝗻𝗼𝘁 𝗺𝗮𝗴𝗶𝗰 The best deployments I've seen don't replace systems—they connect them. Our MQL Agent works because it fits between Salesforce, Snowflake, and Slack without disrupting how sales actually operates. 3️⃣ 𝗧𝗵𝗲𝘆 𝗮𝘀𝘀𝗶𝗴𝗻 𝗣&𝗟 𝗼𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽, 𝗻𝗼𝘁 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝘁𝗵𝗲𝗮𝘁𝗲𝗿 Every successful pilot has an operator who owns the outcome, not just the implementation. Someone whose bonus depends on that 55 becoming a 5. 💡 𝗧𝗵𝗲 𝘂𝗻𝗰𝗼𝗺𝗳𝗼𝗿𝘁𝗮𝗯𝗹𝗲 𝘁𝗿𝘂𝘁𝗵 𝗳𝗼𝗿 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝘁𝗲𝗮𝗺𝘀: Your GenAI failure isn't a technology problem. It's likely:  • Scattered ownership across IT and business units  • Use cases defined by vendors, not operators  • Integration treated as an afterthought The enterprises winning with AI aren't the ones with the biggest models or budgets. They're the ones who understand that GenAI is an operational challenge first, technical second. For CTOs/CIOs asking "𝘸𝘩𝘺 𝘢𝘳𝘦𝘯'𝘵 𝘰𝘶𝘳 𝘱𝘪𝘭𝘰𝘵𝘴 𝘴𝘤𝘢𝘭𝘪𝘯𝘨?"—look at your integration strategy before your AI strategy. 𝘞𝘩𝘢𝘵'𝘴 𝘺𝘰𝘶𝘳 𝘦𝘹𝘱𝘦𝘳𝘪𝘦𝘯𝘤𝘦? 𝘈𝘳𝘦 𝘺𝘰𝘶 𝘪𝘯 𝘵𝘩𝘦 5% 𝘰𝘳 𝘧𝘪𝘨𝘩𝘵𝘪𝘯𝘨 𝘵𝘰 𝘨𝘦𝘵 𝘵𝘩𝘦𝘳𝘦?

GenAI is easy to start but hard to scale. Too many companies are stuck in endless pilots. Here’s what it takes to build GenAI capability. McKinsey has recently published their findings from working with 150+ companies on their GenAI programs over two years. Two hurdles stand out: 𝟭. 𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝘁𝗼 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗲: Teams waste time on duplicate experiments, wait on compliance processes, and solve problems that don’t matter. 30% - 50% of innovation time is spent trying to meet compliance - not building. 𝟮. 𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝘁𝗼 𝘀𝗰𝗮𝗹𝗲: Even when a prototype works, most companies can’t get it into production. Risk, security, and cost barriers overwhelm teams, leading to stalled or cancelled deployments. According to McKinsey the most successful GenAI platforms contains three core components: 𝟭. 𝗔 𝘀𝗲𝗹𝗳-𝘀𝗲𝗿𝘃𝗶𝗰𝗲 𝗽𝗼𝗿𝘁𝗮𝗹: To support both innovation and scale, companies need a secure, centralized portal that gives teams easy access to pre-approved gen AI tools, services, and documentation. It should enable developers to quickly build with reusable patterns, while also offering governance features like observability, cost controls, and access management. The best portals promote contribution and reuse across the organization, reducing friction and accelerating development at scale. 𝟮.𝗔𝗻 𝗼𝗽𝗲𝗻 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝘁𝗼 𝗿𝗲𝘂𝘀𝗲 𝗚𝗲𝗻𝗔𝗜 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀: Scaling GenAI requires modular, open architecture that enables teams to reuse services, application patterns, and data products across use cases. Leading companies build libraries of common components (like RAG, embeddings, or chat workflows) and focus on integration via APIs - not vendor lock-in. Infrastructure and policy as code ensure changes can propagate quickly and securely across the platform, reducing cost and accelerating deployment. 𝟯. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱, 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀: To scale safely, GenAI platforms must embed automated governance that enforces compliance, manages risk, and tracks costs. This includes microservices that audit prompts, detect policy violations (like sharing sensitive personal data or generating inaccurate responses), and attribute usage to specific teams. A centralized AI gateway enforces access controls, logs interactions, and routes traffic through security filters - allowing flexibility where needed. These guardrails accelerate approval processes, reduce setup time, and let teams focus on building value - not managing risk manually. 𝗪𝗵𝗮𝘁’𝘀 𝘆𝗼𝘂𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲? Source: McKinsey & Company 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/dkqhnxdg

At IBM we sponsored a survey with 1,000+ U.S.-based enterprise AI developers, to uncover the hurdles they face when working with generative AI. Here’s what we found: 𝟭/ 𝗦𝗸𝗶𝗹𝗹𝘀 𝗚𝗮𝗽𝘀: Only 24% of app developers surveyed consider themselves experts in GenAI. Fast innovation cycles and a lack of standardized development frameworks are major obstacles. 𝟮/ 𝗧𝗼𝗼𝗹 𝗢𝘃𝗲𝗿𝗹𝗼𝗮𝗱: Developers juggle between 5–15 tools (or more!) to create enterprise AI apps. Yet the most critical tool qualities - performance, flexibility, ease of use, and integration - are also the rarest. 𝟯/ 𝗧𝗿𝘂𝘀𝘁 𝗮𝗻𝗱 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆: As enterprises explore agentic AI, trustworthiness and seamless integration with broader IT systems emerge as critical concerns. The consequences are clear: overly complex AI stack enterprise investments and slow innovation. So, what’s the solution? ⭐ SIMPLIFICATION ⭐ Developers need tools that are easy to master and enhance productivity. At IBM, we’re focused on empowering developers with tools and strategies to cut through that complexity. You can learn more about the survey conducted by Morning Consult here: https://lnkd.in/gXDuwTaS IBM Blog: https://lnkd.in/gsMVMmXX