AI Development Approaches

→ 𝐓𝐡𝐞 𝐡𝐢𝐝𝐝𝐞𝐧 𝐀𝐈 𝐫𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐢𝐧 𝐬𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐢𝐬 𝐡𝐚𝐩𝐩𝐞𝐧𝐢𝐧𝐠 𝐪𝐮𝐢𝐞𝐭𝐥𝐲, 𝐲𝐞𝐭 𝐢𝐭 𝐢𝐬 𝐫𝐞𝐬𝐡𝐚𝐩𝐢𝐧𝐠 𝐞𝐯𝐞𝐫𝐲 𝐬𝐭𝐞𝐩 𝐨𝐟 𝐭𝐡𝐞 𝐥𝐢𝐟𝐞𝐜𝐲𝐜𝐥𝐞. Most developers and managers focus on coding alone, but the real transformation starts much earlier and continues long after the first line of code is written. 𝐇𝐞𝐫𝐞’𝐬 𝐚 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐦𝐚𝐩 𝐨𝐟 𝐡𝐨𝐰 𝐀𝐈 𝐢𝐬 𝐞𝐧𝐡𝐚𝐧𝐜𝐢𝐧𝐠 𝐞𝐚𝐜𝐡 𝐬𝐭𝐚𝐠𝐞 𝐨𝐟 𝐬𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐝����𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭: • Requirements Gathering & Analysis AI can analyze stakeholder inputs, previous project data, and user feedback to generate precise requirements. Tools like Jira with AI plugins, Aha!, and Receptive AI help teams avoid ambiguous specs and reduce rework. • Project Planning & Management AI optimizes resource allocation, predicts project timelines, and flags potential risks. Tools like ClickUp AI, Monday.com AI, and Asana AI assist PMs in creating realistic roadmaps and improving team efficiency. • UI/UX Design AI generates design prototypes, predicts user behavior, and suggests improvements based on analytics. Figma with AI plugins, Adobe Firefly, and Uizard help designers create intuitive and data-driven interfaces. • Coding & Development From auto-completing code to generating boilerplate functions, AI accelerates development while reducing errors. Popular tools include GitHub Copilot, Tabnine, and CodeWhisperer. • Quality Assurance & Testing AI-driven testing predicts high-risk areas, auto-generates test cases, and identifies anomalies faster than humans. Tools like Testim, Mabl, and Applitools enhance test accuracy and speed. • Monitoring & Maintenance AI monitors application performance, predicts failures, and recommends fixes proactively. Dynatrace, New Relic, and Moogsoft empower teams to maintain high availability and user satisfaction. The reality is clear: every stage of the software lifecycle is now influenced by intelligent automation. Ignoring AI today could mean falling behind tomorrow. Follow Sandeep Bonagiri for more insights

The more I engage with organizations navigating AI transformation, the more I’m seeing a number of “flavors” 🍦 of AI deployment. Amidst this variety, several patterns are emerging, from activating functionality of tools embedded in daily workflows to bespoke, large-scale systems transforming operations. Here are the common approaches I’m seeing: A) Small, Focused Add-On to Current Tools: Many teams start by experimenting with AI features embedded in familiar tools, often within a single team or department. This approach is quick, low-risk, and delivers measurable early wins. Example: A sales team uses Salesforce Einstein AI to identify high-potential leads and prioritize follow-ups effectively. B) Scaling Pre-Built Tools Across Functions: Some organizations roll out ready-made AI solutions across entire functions—like HR, marketing, or customer service—to tackle specific challenges. Example: An HR team adopts HireVue’s AI platform to screen resumes and shortlist candidates, reducing time-to-hire and improving consistency. C) Localized, Nimble AI Tools for Targeted Needs: Some teams deploy focused AI tools for specific tasks or localized needs. These are quick to adopt but can face challenges scaling. Example: A marketing team uses Jasper AI to rapidly generate campaign content, streamlining creative workflows. D) Collaborating with Technology Partners: Partnering with tech providers allows organizations to co-create tailored AI solutions for cross-functional challenges. Example: A global manufacturer collaborates with IBM Watson to predict equipment failures, minimizing costly downtime. E) Building Fully Custom, Organization-Wide AI Solutions: Some enterprises invest heavily in custom AI systems aligned with their unique strategies and needs. While resource-intensive, this approach offers unparalleled control and integration. Example: JPMorgan Chase develops proprietary AI systems for fraud detection and financial forecasting across global operations. F) Scaling External Tools Across the Enterprise: Organizations sometimes deploy external AI tools organization-wide, prioritizing consistency and ease of adoption. Example: ChatGPT Enterprise is integrated across an organization’s productivity suite, standardizing AI-powered efficiency gains. G) Enterprise-Wide AI Solutions Developed Through Partnerships: For systemic challenges, organizations collaborate with partners to design AI solutions spanning departments and regions. Example: Google Cloud AI works with healthcare networks to optimize diagnostics and treatment pathways across hospital systems. Which approaches resonate most with your organization’s journey? Or are you blending them into something uniquely yours? With so many ways for this technology to transform jobs, processes, and organizations, it’s important we get clear about what flavor we’re trying 🍨 so we know how to do it right. #AIAdoption #ChangeManagement #AIIntegration #Leadership

All valuable work will increasingly be done by Human-AI hybrids. An insightful research paper identifies both challenges and good practices from multiple case studies to propose an overall framework. The authors propose that generating effective human-AI hybrids is divided into two phases: Construction - in which Technical implementers design the architecture of the hybrid - and Execution - where Organizational implementers facilitate how participants engage and interact. They suggest 3 primary success factors: 🔧 Interface and Technical Design focuses on making AI systems accessible and reliable through code-free interfaces. The technical architecture should allow rapid testing of different approaches while being supported by effective data curation strategies. 🧠 Human Capability Development prepares people to work effectively with AI systems through training, in critical assessment and prompting techniques. Employees must understand AI's capabilities and limitations, and develop skills to integrate AI into existing workflows. 🤝 The Collaboration Framework structures successful human-AI interaction through aligned mental models and clear role definitions. It emphasizes improving underperforming areas rather than disrupting successful processes, while ensuring both human and AI agents contribute their unique strengths to achieve optimal outcomes.

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?

🚀 The Rise of Agentic AI in XR Development For years, AI in development mainly meant generating content or assisting developers — things like writing code, generating textures, or creating NPC dialogue. But a new paradigm is emerging: Agentic AI — AI systems that can plan, decide, and execute tasks autonomously. Instead of just helping developers with small tasks, AI agents can now participate in the development workflow itself. This is especially powerful in XR development (AR / VR / MR) where workflows involve multiple steps such as scene creation, scripting, physics setup, interaction design, testing, and optimization. ⸻ 🧠 What Makes AI Agentic? A true agentic system can: ✔ Plan tasks ✔ Execute multi-step workflows ✔ Use external tools ✔ Iterate and improve results ✔ Work toward a defined goal autonomously In simple terms: You define the goal → AI determines the steps. ⸻ 🛠 Agentic AI Tools Emerging in XR Development 1️⃣ Bezi AI An AI-powered platform focused on 3D and XR scene generation. Example prompt: “Create a VR showroom with interactive product displays.” Bezi can automatically generate: • 3D environment layout • Object placement • Basic interactions • Export-ready scenes for game engines. ⸻ 2️⃣ Model Context Protocol (MCP) MCP is an emerging AI integration standard that allows agents to interact with development tools and software environments. For XR pipelines this could allow AI agents to: • Access Unity or Unreal projects • Modify scripts and assets • Trigger builds • Run automated testing • Optimize scenes This enables AI agents to operate inside the development pipeline. ⸻ 3️⃣ NVIDIA Omniverse AI Agents Within digital twins and simulation workflows, AI agents can assist in building and managing complex 3D environments. Use cases include: • Smart scene assembly • Simulation automation • Industrial XR training environments • Digital twin interaction systems ⸻ 4️⃣ Convai (Agentic NPC Systems) Convai enables autonomous AI characters that can: • Perceive environment context • Hold conversations • Perform tasks based on goals • Act as intelligent training or simulation charact This is especially powerful for VR training, simulations. ⸻ ⏱ Impact on XR Development Agentic AI is starting to change XR workflows: • 30–50% faster prototyping • Reduced manual scene setup • Automated scripting assistance • Intelligent simulation agents Developers increasingly shift from manually building everything to guiding intelligent systems. ⸻ 🌍 The Future XR Pipeline XR development is evolving through stages: Manual Development ↓ AI-Assisted Development ↓ Agentic XR Workflows Soon developers may simply describe an experience like: “Create a VR safety training simulation for factory workers.” And AI agents will help orchestrate the pipeline — from scene creation to interaction logic and testing #AgenticAI #XRDevelopment #Unity3D #UnrealEngine #ImmersiveTech #VR #AR #AIagents #FutureOfWork

How to Built #EnterpriseAI? Building enterprise AI requires a structured approach that blends clear business alignment, robust data systems, the right technology, security, compliance, and ongoing human oversight. 1. Start by identifying the most valuable problems that AI can solve for the business and ensuring these initiatives fit the organization’s strategic goals. Next, establish a strong data foundation — this means gathering, processing, and securing data for both quality and compliance. 2. Carefully evaluate and select appropriate AI technologies, whether off-the-shelf models or custom solutions, and design the AI system to scale and integrate with existing processes. 3. Finally, prioritize trust, responsible deployment, and continuous improvement as the AI system is launched and scaled across the enterprise. Actionable Steps to #BuildEnterpriseAI : - Align AI With Business Strategy - Identify key business problems and objectives where AI can provide measurable impact. - Ensure each AI project is connected to core organizational KPIs and existing business goals moveworks. - Build a Data Foundation. - Collect and assess the quality, relevancy, and structure of enterprise data, ensuring all data is clean, comprehensive, and accessible. - Implement robust data pipelines and governance frameworks for security and compliance.. - Choose the Right Technology: Select suitable AI tools, platforms, and algorithms based on business needs and data complexity. - Design a scalable infrastructure — decide between cloud, on-premises, or hybrid models — ready for growth and integration. - Design and Train AI Models. - Develop models guided by clear evaluation criteria and business outcomes; iterate continuously for accuracy. - Integrate #explainableAI features to build user trust and transparent decision-making. - Integrate With Enterprise Systems. - Connect AI models to existing IT systems (CRM, ERP, etc.) and workflows for seamless user adoption. - Ensure secure, observable integrations with backend systems and build intuitive, user-focused interfaces. - Implement Governance and Security: Enforce data privacy, access controls, and regulatory compliance throughout development and deployment. - Include a “kill switch” or fail-safe processes for critical deployments to manage risk. - Pilot, Monitor, and Scale: Start with controlled pilots; monitor performance and iterate rapidly based on user and system feedback. - Set up dashboards for metrics, observability, and ongoing monitoring. - Continuous Improvement: Embed AI management and change processes into normal business operations for ongoing refinement and value delivery. Using these actionables ensures enterprise AI initiatives are not only technically sound, but also strategically valuable, ethically governed, and positioned for continual business impact.

It's February 2026 and most executives still don't know the difference between the three types of AI coding. Here is the only framework you need. AI coding is no longer experimental. It's the default for high-performing product teams. But there are three distinct approaches, each built for different situations. 1/ Vibe Coding (Non-Tech Level) Describe what you want. AI builds it. No programming skills required. Best for: → Validating product ideas before committing budget → Building stakeholder demos fast → Letting business teams prototype without engineering Skip it for production systems. ROI: Prove market fit before writing a single line of real code. Tools: Lovable, Bolt, Replit, V0, Make, Stagewise 2/ AI-Assisted Development (Mid-Level) Your developers write code. AI amplifies them. Real-time completions, suggestions, and error detection while they work. Best for: → Everyday engineering tasks → Eliminating repetitive boilerplate → Raising code quality across the team ROI: 20 to 25% individual developer productivity gain. Tools: Cursor, GitHub Copilot, Google Antigravity, Continue, Kiro The key concept: context engineering. Multiple AI calls orchestrated while the developer stays in control. 3/ Agentic Development (Advanced Level) You define the outcome. AI plans, writes, tests, and ships. Minimal supervision. Maximum throughput. Best for: → Legacy system migrations → Large-scale codebase updates → Multi-step engineering work with clear specs Skip it when requirements are vague. ROI: 2x delivery speed on legacy modernisation. Tools: Claude Code, OpenAI Codex, Gemini CLI, Devin The smartest teams are not picking one. They match the approach to the problem. Vibe Coding to validate before investing. AI-Assisted to accelerate existing talent. Agentic to delegate well-scoped modernisation. Which one are you missing? We are building a newsletter to go deeper: Insights on building AI-native organisations. Subscribe Free Here: https://lnkd.in/ep5VBW-k ♻️ Repost this to share with your network. ➕ Follow me, Sasha Astapenka, CEO & Founder of ENDGAME

How to Implement AI in Any Product Seamlessly 1. Problem Definition Identify the Problem: Clearly define the problem or task that the AI solution will address. Desired Outcome: Specify the desired outcome and performance criteria for the AI system. 2. Data Collection and Preparation Collect Relevant Data: Gather the necessary data from various sources. Data Preprocessing: Clean, preprocess, and annotate the data to ensure it’s suitable for training. Data Splitting: Divide the data into training, validation, and test sets. 3. Model Selection and Algorithm Development Choose AI Technique: Select the appropriate AI technique (e.g., machine learning, deep learning) for the task. Develop Algorithm: Choose or develop a suitable algorithm or model architecture. Configure Parameters: Set up model parameters and hyperparameters for optimal performance. 4. Model Training Feed Data into Model: Train the model using the training dataset. Adjust Weights: Adjust the model’s weights to minimize the loss function. Monitor Performance: Use the validation data to monitor and evaluate the model’s performance. 5. Model Evaluation Test on Unseen Data: Evaluate the trained model on unseen test data. Performance Assessment: Assess the model’s performance using predefined metrics. Identify Improvements: Identify areas for improvement or potential biases in the model. 6. Model Fine-Tuning and Optimization Adjust Hyperparameters: Fine-tune hyperparameters or model architecture for better performance. Feature Engineering: Perform feature engineering or data augmentation as needed. Retrain Model: Retrain the model and iteratively evaluate its performance. 7. Model Development Integrate Model: Integrate the trained model into the target application. Monitor in Real-World Scenarios: Continuously monitor the model’s performance in real-world scenarios. Update Model: Update the model with new data or techniques as needed to maintain its effectiveness. 8. Model Maintenance Ensure Fairness and Transparency: Maintain the AI system’s fairness, accountability, and transparency. Address Biases: Identify and address potential biases and unintended consequences. Data Privacy and Security: Follow guidelines for data privacy and security to protect user information. This framework provides a structured approach to implementing AI in any product, ensuring that the solution is effective, reliable, and continuously improving.

Are you building your AI strategy on quicksand? This week on the Product Thinking Podcast, I spoke with Dr. Maryam Ashoori, PhD from IBM Watson X about a critical blind spot in AI product development. Her insight hit me: "The world is changing so fast and by the time that your product gets out, there's a good chance that these underlying technology is already outdated." Think about it. Companies are building entire product strategies around specific AI models or tool vendors. But what happens when GPT-7 launches, or a your chosen tool is discontinued? You're stuck with expensive rebuilds instead of seamless upgrades. Maryam's solution? Build with architectural abstraction. Separate your business logic from the AI technology through layers that treat models and external tools as interchangeable components. This approach prevents technical debt and ensures strategic survival. I've seen enterprises trapped by their own tech choices, unable to leverage breakthrough advances because they didn't plan for change. The cost of switching becomes prohibitive, so they fall behind. The companies winning in AI aren't necessarily picking the best models today. They're building systems that can adapt to whatever comes next. How technology-agnostic is your AI architecture? Are you ready for the next breakthrough, or locked into yesterday's choices?

Designing #AI applications and integrations requires careful architectural consideration. Similar to building robust and scalable distributed systems, where principles like abstraction and decoupling are important to manage dependencies on external services or microservices, integrating AI capabilities demands a similar approach. If you're building features powered by a single LLM or orchestrating complex AI agents, a critical design principle is key: Abstract your AI implementation! ⚠️ The problem: Coupling your core application logic directly to a specific AI model endpoint, a particular agent framework or a sequence of AI calls can create significant difficulties down the line, similar to the challenges of tightly coupled distributed systems: ✴️ Complexity: Your application logic gets coupled with the specifics of how the AI task is performed. ✴️ Performance: Swapping for a faster model or optimizing an agentic workflow becomes difficult. ✴️ Governance: Adapting to new data handling rules or model requirements involves widespread code changes across tightly coupled components. ✴️ Innovation: Integrating newer, better models or more sophisticated agentic techniques requires costly refactoring, limiting your ability to leverage advancements. 💠 The Solution? Design an AI Abstraction Layer. Build an interface (or a proxy) between your core application and the specific AI capability it needs. This layer exposes abstract functions and handles the underlying implementation details – whether that's calling a specific LLM API, running a multi-step agent, or interacting with a fine-tuned model. This "abstract the AI" approach provides crucial flexibility, much like abstracting external services in a distributed system: ✳️ Swap underlying models or agent architectures easily without impacting core logic. ✳️ Integrate performance optimizations within the AI layer. ✳️ Adapt quickly to evolving policy and compliance needs. ✳️ Accelerate innovation by plugging in new AI advancements seamlessly behind the stable interface. Designing for abstraction ensures your AI applications are not just functional today, but also resilient, adaptable and easier to evolve in the face of rapidly changing AI technology and requirements. Are you incorporating these distributed systems design principles into your AI architecture❓ #AI #GenAI #AIAgents #SoftwareArchitecture #TechStrategy #AIDevelopment #MachineLearning #DistributedSystems #Innovation #AbstractionLayer AI Accelerator Institute AI Realized AI Makerspace