How to Adopt AI in Development

SMBs are facing a critical challenge: how to maximize efficiency, connectivity, and communication without massive resources. The answer? Strategic AI implementation. Many small business owners tell me they're intimidated by AI. But the truth is you don't need to overhaul your entire operation overnight. The most successful AI adoptions I've seen follow these six straightforward steps: 1️⃣ Identify Immediate Needs: Look for quick wins where AI can make an immediate impact. Customer response automation is often the perfect starting point because it delivers instant value while freeing your team for higher-value work. 2️⃣ Choose User-Friendly Tools: The best AI solutions integrate seamlessly with your existing technology stack. Don't force your team to learn entirely new systems. Find tools that enhance what you're already using. 3️⃣ Start Small, Scale Gradually: Begin with focused implementations in 1-2 key areas. This builds confidence, demonstrates value, and creates organizational momentum before expanding. 4️⃣ Measure and Adjust Continuously: Set clear KPIs from the start. Monitor performance religiously and be ready to refine your AI configurations to optimize results. 5️⃣ Invest in Team Education: The most overlooked success factor? Proper training. When your team understands both the "how" and "why" behind AI tools, adoption rates soar. 6️⃣ Look Beyond Automation: While efficiency gains are valuable, the real competitive advantage comes from AI-driven insights. Let the technology reveal patterns in your business processes and customer behaviors that inform better strategic decisions. The bottom line: AI adoption doesn't require disruption. The most effective approaches complement your existing workflows, enabling incremental improvements that compound over time. What's been your experience implementing AI in your business? I'd love to hear what's working (or not) for you in the comments below. #SmallBusiness #AI #BusinessStrategy #DigitalTransformation

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.

We hear all about the amazing progress of AI BUT, enterprises are still struggling with AI deployments - latest stats say 78% of AI deployments get stall or canceled - sounds like we’re still buying tools and expect transformation. But those that have succeeded? They don’t just license AI, they redesign work around them. Because adoption isn’t about the tool. It’s about the people who use it. Let’s break this down: 😖 Buying AI tools just adds to your tech stack. Nothing more, nothing less! Stat you can’t ignore: 81% of enterprise AI tools go unused after purchase. (Source: IBM, 2024) 🙌🏼 But adoption, adoption requires new workflows, new roles, and new routines - this means redesigning org charts, updating SOPs, and rethinking “a day in the life.” Why? Because AI should empower decisions—not just automate tasks. It should amplify human strengths—not quietly sideline them. That’s where the 65/35 Rule comes in! 65% of a successful AI deployment is redesigning business processes and preparing the workforce. Only 35% is tools and infrastructure. But most companies still do the reverse. They invest 90% in tech and 10% in training… and wonder why they’re stuck in “perpetual POC purgatory” (my term for things that never make production. It’s like buying a Formula 1 car and expecting your team to win races—without ever learning to drive. Here’s the better way: Step 1: Start with the “day in the life” Map how work actually gets done today. Not hypothetically. Not aspirationally. Just reality. Step 2: Identify friction points Where do delays, errors, or bad decisions happen? Step 3: Redesign with intent Now—and only now—do you introduce AI. Not to replace the human. But to support and strengthen them. Recommendation #1: Design AI solutions with your workforce, not just for them. Co-create roles, rituals, and reviews. Recommendation #2: Adopt the 65/35 Rule as your north star. If your AI strategy doesn’t spend more time on people and process than tools and tech… it’s not ready. ⸻ AI doesn’t fail because it’s flawed. It fails because the org using it is unprepared. #AI #FutureOfWork #DigitalTransformation #Leadership #OrgDesign #HumanInTheLoop #AIAdoption #DataDrivenDecisions #Innovation >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Sol Rashidi was the 1st “Chief AI Officer” for Enterprise (appointed back in 2016). 10 patents. Best-Selling Author of “Your AI Survival Guide”. FORBES “AI Maverick & Visionary of the 21st Century”. 3x TEDx Speaker

Great AI-assisted development does not start with prompts. It starts with structure. This “Claude Code Project Structure” visual highlights something many teams overlook when adopting AI for engineering workflows: If your repository is messy, your AI output will be messy too. What stands out here is the intentional design: - a clear project context layer (CLAUDE.md) - reusable skills for repeated workflows like code review, refactoring, and release support - hooks for guardrails and automation - dedicated docs for architecture, decisions, and runbooks - modular src/ ownership for focused implementation context This is bigger than just repo hygiene. It is about building an environment where AI can operate with: clarity, consistency, safety, and scale. As AI becomes part of the software delivery lifecycle, the winning teams will be the ones that treat: - context as infrastructure - prompts as reusable assets - governance as a built-in capability - modularity as an accelerator That is how you move from one-off AI experiments to repeatable engineering systems. I especially like the reminder around best practices: keep context minimal, prompts modular, decisions documented, and workflows reusable. That is not just good for Claude or any coding assistant. That is good software engineering discipline, period. The future of AI-enabled development will belong to teams that know how to combine: architecture + workflows + governance + developer experience How are you structuring AI context and reusable workflows inside your engineering projects today?

Somebody has to say it: some AI tools are causing more harm than good. Not because the technology is bad. Not because people are resisting change. But because we keep rolling out tools without guidance, training, or context and calling it “innovation.” When employees are expected to figure it out on their own, confusion replaces confidence. Work slows down. Trust erodes. AI at work doesn’t fail loudly. It quietly creates friction when enablement is missing. If we want better outcomes, we have to design for adoption, not just deployment. If you’re rolling out AI at work and want it to actually help, here’s a simple place to start: 1. Start with the “why,” not the tool ✅ Be clear about the problem AI is meant to solve. Productivity, quality, speed, decision-making. If people don’t understand the purpose, they won’t trust the tool. 2. Define when and when not to use it ✅ Ambiguity creates hesitation. Give real examples of appropriate use cases and clear boundaries so employees aren’t guessing. 3. Train for workflows, not features ✅ Skip the generic demos. Show how the tool fits into existing day-to-day work, step by step. 4. Equip managers first ✅ If managers can’t explain or model usage, adoption stalls. Enable leaders before expecting teams to follow. 5. Build feedback loops early ✅ Create space for questions, friction, and adjustments. Early feedback prevents quiet frustration from turning into resistance. 6. Treat adoption as ongoing, not a launch event ✅ AI enablement isn’t a one-time rollout. It’s reinforcement, iteration, and support over time. AI works best when people feel prepared, not pressured. ——— ✦ ——— 🌱 More on AI + Workforce Development → Janet Perez

The latest DORA report on AI-assisted software development contains a finding that every CTO should pay attention to. AI is an amplifier. It magnifies whatever you already have. The strengths of high-performing organizations and the dysfunctions of struggling ones. The research is based on nearly 5,000 technology professionals. 90% are using AI at work. Over 80% believe it increased their productivity. But here's the issue. AI adoption improves delivery throughput, but it also increases delivery instability. Teams are going faster. Their systems haven't adapted to manage that speed safely. It's like installing a racing engine in a car but keeping the original brakes and suspension. Sure, you can hit higher speeds going in a straight line, but the first sharp turn becomes a crisis waiting to happen. In well-aligned organizations, AI amplifies flow. In fragmented ones, it exposes pain points. The technology reflects back the true state of your capabilities. The report is clear about what actually matters. The greatest returns on AI investment come not from the tools themselves, but from the underlying organizational system. The quality of your internal platform. The clarity of your workflows. The alignment of your teams. Without these foundations, you're just accelerating into chaos. The research identified seven foundational practices that amplify AI's positive impact. They're not purely technical. They include having a clear AI policy, a healthy data ecosystem, and a user-centric focus. These are cultural practices, not just engineering ones. Value stream management emerged as critical. Without it, local productivity gains will only create more downstream bottlenecks. You speed up one part of the system and the rest can't cope. What strikes me most is this: AI adoption is nearly universal, but 30% of people report little to no trust in the code it generates. That gap between adoption and trust tells you everything about where we are. We're in the "move fast and hope" phase when we need to be in the "fix foundations first" phase. If you're a technology leader looking at AI adoption, the question isn't "Which tools should we buy?" It's "Is our system healthy enough to amplify?" Because AI will amplify whatever you've got. Make sure that's something you want more of. https://lnkd.in/efUwhgAQ #aiassisteddev, #aiadoption, #DORA Parallaxis

Adopting Claude Code in the Enterprise: -----Lessons and Best Practices---- For enterprises considering adopting Claude, the opportunity is significant but so are the responsibilities. Implementing it well requires thinking beyond the tool itself & focusing on governance, architecture, security, & developer enablement. Why Enterprises Are Looking at Claude? Because it combines strong reasoning capabilities with a large context window, allowing it to: Understand complex repositories Plan multi-file refactors Assist with debugging & architecture reviews; and Generate tests & documentation automatically In practice, this means developers can spend less time on repetitive tasks & more time on design, architecture, & innovation. Organizations that adopt it successfully are seeing improvements in: Developer velocity Code quality & documentation Faster onboarding for new engineers Reduced technical debt But unlocking these benefits requires a thoughtful deployment model. 1. Start with Clear Governance AI-assisted development should never bypass existing engineering discipline. Enterprises should define: AI usage policies Code review requirements; and Ownership & accountability for AI-generated code A simple rule I’ve seen work well: AI to propose code, humans to approve it. 2. Cybersecurity Embedded from Day One Data Protection Prevent sensitive code or credentials from being exposed in prompts Implement secure API gateways and monitoring Secure Model Access Use enterprise authentication (IAM integration) Role-based access for development environments Auditability Log AI interactions for compliance Maintain traceability for generated code Dependency & Vulnerability Scanning Automatically scan AI-generated code Integrate with existing SAST/DAST pipelines Without these safeguards, AI coding tools can unintentionally introduce data leakage risks or insecure code patterns. 3. Define the Right Architecture A typical Claude enterprise architecture includes: Developer Environment → Claude Code Interface → Secure AI Gateway → Model API → Enterprise Code Repositories → CI/CD Pipeline → Security & Compliance Monitoring 4. Invest in Developer Skills The most successful teams focus on: Prompt engineering for developers Knowing how to ask the right questions. System thinking AI accelerates coding architecture decisions become even more important. Competitive Advantage of Claude Code 1. Faster Software Delivery Teams can iterate faster & reduce development cycles significantly. 2. Reduced Technical Debt AI can identify outdated patterns & suggest improvements. Where This Is Heading The most forward-thinking companies are moving toward a model where AI becomes a standard layer in the software development stack. In the same way that: Git transformed version control CI/CD transformed deployment Real competitive advantage won’t come from simply adopting AI, it will come from how well organizations integrate it into their culture. #claudecode

𝐀𝐈 𝐀𝐝𝐨𝐩𝐭𝐢𝐨𝐧 𝐢𝐧 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞𝐬: 𝐅𝐨𝐮𝐫 𝐋𝐞𝐯𝐞𝐥𝐬 𝐟𝐫𝐨𝐦 𝐂𝐮𝐫𝐢𝐨𝐬𝐢𝐭𝐲 𝐭𝐨 𝐀𝐈-𝐍𝐚𝐭𝐢𝐯𝐞 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 Most companies think they are further along in AI adoption than they actually are.  This framework maps four distinct levels and being honest about where you sit is the first step to moving up. LEVEL 1: INDIVIDUAL USAGE (Curiosity-Driven) Goal: Individuals experiment with AI to save time. • Quick Tasks: Used for emails, brainstorming, and summaries • No AI Strategy: No formal company policy or direction • Personal Tools: Employees use different AI tools individually • Manual Workflows: Outputs are copied manually between tools • Early Exploration: High curiosity but inconsistent results • No Data Governance: Sensitive data may be shared without safeguards LEVEL 2: TEAM-LEVEL EXPERIMENTATION (Process Exploration) Goal: Teams begin applying AI to real work processes. • AI Content Creation: Used for emails, posts, reports, and documents • Meeting Automation: AI summarizes meetings and extracts action items • Workflow Automation: Simple AI chains automate repetitive tasks • AI Research Support: Helps analyze competitors and summarize reports • Tool Consolidation: Teams narrow down to a few preferred AI tools • Manager-Driven Adoption: Leaders encourage AI adoption LEVEL 3: DEPARTMENTAL AI INTEGRATION (Structured + Scalable) Goal: AI use becomes standardized across teams. • AI Playbooks: Defined workflows for each department • Data Pipelines: Clean, structured data feeds AI systems • Prompt Libraries: Shared prompts ensure consistent results • AI Team Champions: Each team has someone responsible for AI adoption • Security Controls: Data protection policies and tool vetting in place • ROI Tracking: Teams measure productivity gains and cost savings LEVEL 4: AI-NATIVE OPERATIONS (Autonomous + Self-Improving) Goal: AI is embedded in every workflow and continuously improves. • AI-Driven Decisions: AI guides strategy, hiring, pricing, forecasting • Connected AI: AI systems across teams work together automatically • Self-Learning: Models improve continuously using new data • AI Governance: Policies ensure ethical and secure AI use • Custom Models: Internal data trains specialized AI models • Revenue from AI: AI creates new products and services MY RECOMMENDATION At Level 1: Establish an AI strategy and basic data governance immediately. At Level 2: Consolidate tools and appoint AI champions per team. At Level 3: Build data pipelines and prompt libraries before scaling further. At Level 4: Focus on connected AI systems and self-learning loops. Which level best describes your organization right now? ♻️ Repost this to help your network get started ➕ Follow Anurag(Anu) Karuparti for more PS: If you found this valuable, join my weekly newsletter where I document the real-world journey of AI transformation. ✉️ Free subscription: https://lnkd.in/exc4upeq #EnterpriseAI #AgenticAI #AIGovernance

AI in software development is about enabling our engineers to focus on high-value work, not replacing them. At Tesco Technology, we use AI as a copilot to boost productivity and streamline routine tasks. Here’s how we make it practical across teams: For leaders: Invest in structured AI training and upskilling. Set clear expectations about what AI can and cannot do, so teams use it with confidence and trust. For platform owners: Integrate AI seamlessly into workflows. Automate repetitive steps, display confidence scores, and create clear support/tutorials to help every engineer use AI as a transparent assistant, not a black box. For enterprise scale: Begin with small pilots and collect feedback. Plan for scaling, measure productivity improvements, share successes, and iterate so the adoption endures and provides value at every stage. AI copilots aren’t just for coding. We’ve enhanced documentation and user guides, reduced tedious work, and improved quality across materials. Success comes from integrating AI into our usual practices, supported by clear guidance and feedback mechanisms (Tools like DX are invaluable for this!). Every developer can benefit, and every team is empowered to progress more quickly. If you’re rolling out AI tooling and copilots, focus on practical wins and clear measurement. Let’s drive developer productivity together! #tescotechnology #devex #ai #teamwork #ArtificialIntelligence #Technology #Innovation #DeveloperExperience #Leadership #Productivity #DigitalTransformation #FutureOfWork #TechLeadership #EnterpriseAI

𝗧𝗵𝗲 𝗲𝗮𝘀𝗶𝗲𝘀𝘁 𝘄𝗮𝘆 𝘁𝗼 𝗸𝗶𝗹𝗹 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗮𝗻𝗱 𝟱 𝘀𝘁𝗲𝗽𝘀 𝘁𝗼 𝗳𝗶𝘅 𝗶𝘁 Most organisations fall into this trap: they give people access to AI tools, experiment, and run isolated pilots without any underlying structure. After months of activity, nothing meaningful has changed. AI hasn't met expectations, and people don't see any tangible results. Organisations only achieve success with AI when they build systems instead of launching disconnected experiments. They have a scalable, repeatable framework and approach for AI projects and driving AI adoption from the start. They create • repeatable processes • clarify ownership  • integrate AI into existing workflows,  • measure progress in a structured way • answers the questions how work and roles will change with AI. As a result, AI becomes useful in daily work. Tools may change rapidly, but well-designed systems create a lasting competitive advantage. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝟱 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝘀𝘁𝗲𝗽𝘀 𝘁𝗼 𝘀𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗔𝗜 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗮𝗻𝗱 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻 (𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗼 𝗮𝘃𝗼𝗶𝗱): 1️⃣ 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗮 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄, 𝗻𝗼𝘁 𝗮 𝘁𝗼𝗼𝗹 Ask: “𝘞𝘩𝘦𝘳𝘦 𝘥𝘰𝘦𝘴 𝘸𝘰𝘳𝘬 𝘤𝘶𝘳𝘳𝘦𝘯𝘵𝘭𝘺 𝘴𝘭𝘰𝘸 𝘥𝘰𝘸𝘯?” Map the flow → identify friction → define where AI supports humans. Avoid: Plugging AI into random tasks because it “sounds useful.” 2️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝗮 𝗿𝗲𝗽𝗲𝗮𝘁𝗮𝗯𝗹𝗲 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 A system answers: • How do we design a use case? • How do we validate value? • Who owns what? • How do we measure success? Avoid: Treating every use case as a one-off experiment. 3️⃣ 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗼𝗼𝗹𝘀 𝙡𝙖𝙨𝙩, 𝗻𝗼𝘁 𝗳𝗶𝗿𝘀𝘁 Once the workflow, roles and value are clear, the right tool naturally emerges. Avoid: Shopping for tools before you even know the problem. 4️⃣ 𝗧𝗿𝗮𝗶𝗻 𝗽𝗲𝗼𝗽𝗹𝗲 𝗼𝗻 𝗛𝗢𝗪 𝘁𝗼 𝘄𝗼𝗿𝗸 𝘄𝗶𝘁𝗵 𝗔𝗜 Skills matter more than tools: prompting, reviewing, judgment, escalation. Answer the question of what working with AI looks like. Avoid: “Tool training” without improving thinking and decision-making. 5️⃣ 𝗞𝗲𝗲𝗽 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗹𝗶𝗴𝗵𝘁𝘄𝗲𝗶𝗴𝗵𝘁 𝗮𝗻𝗱 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 Simple rules. Clear roles. Fast approvals. Governance should provide safety for people, not bureaucracy. Avoid: Heavy paperwork that kills momentum before it starts. If you want AI to succeed, stop chasing tools. Start building systems that make AI work 𝗲𝘃𝗲𝗿𝘆 𝗱𝗮𝘆, not just in pilot mode. Do you already have a repeatable AI system in place, or are you still testing tools? Curious to hear where you are today.