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

Is your GenAI strategy missing a key ingredient? Successful AI adoption is about change on three fronts: 1) operational development, 2) people, and 3) tech change, not just tech upgrades. Successful AI adoption needs a two-pronged approach LLM + HLM (Large Language Model + Large Human Model): 1. Operational Development Change: Adapt workflows, processes, and IT infrastructure for AI. Think of it as preparing soil for a new plant. Examples: streamline data collection, redesign workflows, train employees on AI tools, and upgrade IT systems. 2. Cultural Change: Shift mindsets to embrace AI. Create an environment where people are comfortable and excited about AI. Examples: address employee concerns, communicate benefits, and foster a culture of experimentation and learning. >> Why Both Matter: Implementing the latest AI tech alone won’t guarantee success. Your operations, including IT infrastructure, must support it. Without employee buy-in, AI investments may go to waste. Think of it as building a house: Operational changes lay the foundation. While cultural changes ensure employees feel comfortable and fully utilize AI. Both are essential for successful AI adoption. Thoughts? ------------------------------- 👋 I'm Mariana Saddakni. I help businesses grow with AI by enhancing business efficiency and keeping teams up-to-date with tech evolution.

Throwing AI tools at your team without a plan is like giving them a Ferrari without driving lessons. AI only drives impact if your workforce knows how to use it effectively. After: 1-defining objectives 2-assessing readiness 3-piloting use cases with a tiger team Step 4 is about empowering the broader team to leverage AI confidently. Boston Consulting Group (BCG) research and Gilbert’s Behavior Engineering Model show that high-impact AI adoption is 80% about people, 20% about tech. Here’s how to make that happen: 1️⃣ Environmental Supports: Build the Framework for Success -Clear Guidance: Define AI’s role in specific tasks. If a tool like Momentum.io automates data entry, outline how it frees up time for strategic activities. -Accessible Tools: Ensure AI tools are easy to use and well-integrated. For tools like ChatGPT create a prompt library so employees don’t have to start from scratch. -Recognition: Acknowledge team members who make measurable improvements with AI, like reducing response times or boosting engagement. Recognition fuels adoption. 2️⃣ Empower with Tiger Team Champions -Use Tiger/Pilot Team Champions: Leverage your pilot team members as champions who share workflows and real-world results. Their successes give others confidence and practical insights. -Role-Specific Training: Focus on high-impact skills for each role. Sales might use prompts for lead scoring, while support teams focus on customer inquiries. Keep it relevant and simple. -Match Tools to Skill Levels: For non-technical roles, choose tools with low-code interfaces or embedded automation. Keep adoption smooth by aligning with current abilities. 3️⃣ Continuous Feedback and Real-Time Learning -Pilot Insights: Apply findings from the pilot phase to refine processes and address any gaps. Updates based on tiger team feedback benefit the entire workforce. -Knowledge Hub: Create an evolving resource library with top prompts, troubleshooting guides, and FAQs. Let it grow as employees share tips and adjustments. -Peer Learning: Champions from the tiger team can host peer-led sessions to show AI’s real impact, making it more approachable. 4️⃣ Just in Time Enablement -On-Demand Help Channels: Offer immediate support options, like a Slack channel or help desk, to address issues as they arise. -Use AI to enable AI: Create customGPT that are task or job specific to lighten workload or learning brain load. Leverage NotebookLLM. -Troubleshooting Guide: Provide a quick-reference guide for common AI issues, empowering employees to solve small challenges independently. AI’s true power lies in your team’s ability to use it well. Step 4 is about support, practical training, and peer learning led by tiger team champions. By building confidence and competence, you’re creating an AI-enabled workforce ready to drive real impact. Step 5 coming next ;) Ps my next podcast guest, we talk about what happens when AI does a lot of what humans used to do… Stay tuned.

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 isn’t a magic fix. If the processes are broken and the data is messy, AI will only accelerate the chaos. That’s why over 80% of organizations aren’t seeing clear ROI from GenAI (McKinsey report, 2025). The risk is even greater in the construction sector. Because in most firms, data is still: - Siloed across teams - Buried in spreadsheets - Entered inconsistently (or not at all) As I spoke with Amine Nabi, CTO of BNMA, who has 30+ years of experience building software solutions for Fortune 500 and SMEs, here’s how you can build a solid foundation and prepare the data for real AI adoption and future ROI: 1. 𝐄𝐬𝐭𝐚𝐛𝐥𝐢𝐬𝐡 𝐚 𝐒𝐢𝐧𝐠𝐥𝐞 𝐒𝐨𝐮𝐫𝐜𝐞 𝐨𝐟 𝐓𝐫𝐮𝐭𝐡 (𝐒𝐒𝐎𝐓) This should be a system, a one place, where all key data is stored (either pick one, or build one). Relying on three systems that all say something slightly different will lead to confusion aand decisions based on incomplete or conflicting information. Define where your project, schedule, or delivery data lives, and make sure everyone is referencing the same source. 2. 𝐂𝐫𝐞𝐚𝐭𝐞 𝐂𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭 𝐃𝐚𝐭𝐚 𝐄𝐧𝐭𝐫𝐲 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐬 If one person writes “Project A" and another writes “Tower-A,” automation will break. Some examples of consistent data entry standards: - naming conventions - formats - required fields - regular update intervals Consistency makes your data usable and reliable. 3. 𝐄𝐬𝐭𝐚𝐛𝐥𝐢𝐬𝐡 𝐃𝐚𝐭𝐚 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 𝐑𝐮𝐥𝐞𝐬 Good data starts at the front door. Data needs to be entered correctly and consistently. Some examples of these rules: - required fields must be filled out (you can use the pre-filled options for similar fields) - drop-downs instead of free text - date and currency formats enforced - duplicate entries flagged in real time The benefit: validation rules will save you time from cleaning up later. 4. 𝐑𝐮𝐧 𝐑𝐞𝐠𝐮𝐥𝐚𝐫 𝐃𝐚𝐭𝐚 𝐀𝐮𝐝𝐢𝐭𝐬 (𝐀𝐈 𝐜𝐚𝐧 𝐡𝐞𝐥𝐩 𝐡𝐞𝐫𝐞) Use AI to detect anomalies, catch duplicates, or flag inaccuracies. You don’t need a massive team to clean your data, you just need visibility and structure. 5. 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞 𝐀𝐥𝐥 𝐘𝐨𝐮𝐫 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 Data should flow seamlessly across your systems. Your ERP, project management tool, and field systems should talk to each other. AI only works when it can “see” across your workflows. Whether you use off-the-shelf integrations or build a custom software layer, the goal is clear: Your systems should share data, not hoard it. _________________ TL;DR: If you want to future-ready your organization for AI adoption, it's crucial to start with the foundation first by having: 1. Clean, connected, consistent data 2. Clear workflows that tech can actually support 3. One version of the truth Once your data and workflows are aligned, AI adoption becomes not just possible, but far more likely to deliver real, measurable ROI. Agree? #enterprisesoftware #construction