Scaling AI Solutions Without Sacrificing Quality

I’ve had the chance to work across several #EnterpriseAI initiatives esp. those with human computer interfaces. Common failures can be attributed broadly to bad design/experience, disjointed workflows, not getting to quality answers quickly, and slow response time. All exacerbated by high compute costs because of an under-engineered backend. Here are 10 principles that I’ve come to appreciate in designing #AI applications. What are your core principles? 1. DON’T UNDERESTIMATE THE VALUE OF GOOD #UX AND INTUITIVE WORKFLOWS Design AI to fit how people already work. Don’t make users learn new patterns — embed AI in current business processes and gradually evolve the patterns as the workforce matures. This also builds institutional trust and lowers resistance to adoption. 2. START WITH EMBEDDING AI FEATURES IN EXISTING SYSTEMS/TOOLS Integrate directly into existing operational systems (CRM, EMR, ERP, etc.) and applications. This minimizes friction, speeds up time-to-value, and reduces training overhead. Avoid standalone apps that add context-switching or friction. Using AI should feel seamless and habit-forming. For example, surface AI-suggested next steps directly in Salesforce or Epic. Where possible push AI results into existing collaboration tools like Teams. 3. CONVERGE TO ACCEPTABLE RESPONSES FAST Most users have gotten used to publicly available AI like #ChatGPT where they can get to an acceptable answer quickly. Enterprise users expect parity or better — anything slower feels broken. Obsess over model quality, fine-tune system prompts for the specific use case, function, and organization. 4. THINK ENTIRE WORK INSTEAD OF USE CASES Don’t solve just a task - solve the entire function. For example, instead of resume screening, redesign the full talent acquisition journey with AI. 5. ENRICH CONTEXT AND DATA Use external signals in addition to enterprise data to create better context for the response. For example: append LinkedIn information for a candidate when presenting insights to the recruiter. 6. CREATE SECURITY CONFIDENCE Design for enterprise-grade data governance and security from the start. This means avoiding rogue AI applications and collaborating with IT. For example, offer centrally governed access to #LLMs through approved enterprise tools instead of letting teams go rogue with public endpoints. 7. IGNORE COSTS AT YOUR OWN PERIL Design for compute costs esp. if app has to scale. Start small but defend for future-cost. 8. INCLUDE EVALS Define what “good” looks like and run evals continuously so you can compare against different models and course-correct quickly. 9. DEFINE AND TRACK SUCCESS METRICS RIGOROUSLY Set and measure quantifiable indicators: hours saved, people not hired, process cycles reduced, adoption levels. 10. MARKET INTERNALLY Keep promoting the success and adoption of the application internally. Sometimes driving enterprise adoption requires FOMO. #DigitalTransformation #GenerativeAI #AIatScale #AIUX

𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗜𝘀𝗻’𝘁 𝗝𝘂𝘀𝘁 𝗔𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗠𝗼𝗱𝗲𝗹 — 𝗜𝘁’𝘀 𝗔𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲. In the age of Agentic AI, designing a scalable agent requires more than just fine-tuning an LLM. You need a solid foundation built on three key pillars: 𝟭. 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 → Use modular frameworks like 𝗔𝗴𝗲𝗻𝘁 𝗦𝗗𝗞, 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵, 𝗖𝗿𝗲𝘄𝗔𝗜, and 𝗔𝘂𝘁𝗼𝗴𝗲𝗻 to structure autonomous behavior, multi-agent collaboration, and function orchestration. These tools let you move beyond prompt chaining and toward truly intelligent systems. 𝟮. 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗠𝗲𝗺𝗼𝗿𝘆 → 𝗦𝗵𝗼𝗿𝘁-𝘁𝗲𝗿𝗺 𝗺𝗲𝗺𝗼𝗿𝘆 allows agents to stay aware of the current context — essential for task completion. → 𝗟𝗼𝗻𝗴-𝘁𝗲𝗿𝗺 𝗺𝗲𝗺𝗼𝗿𝘆 provides access to historical and factual knowledge — crucial for reasoning, planning, and personalization. Tools like 𝗭𝗲𝗽, 𝗠𝗲𝗺𝗚𝗣𝗧, and 𝗟𝗲𝘁𝘁𝗮 support memory injection and context retrieval across sessions. 𝟯. 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲 → 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗕𝘀 enable fast semantic search. → 𝗚𝗿𝗮𝗽𝗵 𝗗𝗕𝘀 and 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗚𝗿𝗮𝗽𝗵𝘀 support structured reasoning over entities and relationships. → Providers like 𝗪𝗲𝗮𝘃𝗶𝗮𝘁𝗲, 𝗣𝗶𝗻𝗲𝗰𝗼𝗻𝗲, and 𝗡𝗲𝗼𝟰𝗷 offer scalable infrastructure to handle large-scale, heterogeneous knowledge. 𝗕𝗼𝗻𝘂𝘀 𝗟𝗮𝘆𝗲𝗿: 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 → Integrate third-party tools via APIs → Use 𝗠𝗖𝗣 (𝗠𝘂𝗹𝘁𝗶-𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹) 𝘀𝗲𝗿𝘃𝗲𝗿𝘀 for orchestration → Implement custom 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 to enable task decomposition, planning, and decision-making Whether you're building a personal AI assistant, autonomous agent, or enterprise-grade GenAI solution—𝘀𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗱𝗲𝗽𝗲𝗻𝗱𝘀 𝗼𝗻 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝗳𝘂𝗹 𝗱𝗲𝘀𝗶𝗴𝗻 𝗰𝗵𝗼𝗶𝗰𝗲𝘀, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗯𝗶𝗴𝗴𝗲𝗿 𝗺𝗼𝗱𝗲𝗹𝘀. Are you using these components in your architecture today?

“We cannot afford to get locked in.” This refrain is becoming a common one in enterprises choosing their AI stack, and it’s not just talk. It shows up in how leading companies are architecting their AI capabilities today: - Every model is evaluated based on task performance, not vendor promises. - Routing is dynamic. Workflows adapt in real time to whichever model performs best. - Vendor loyalty is gone. Replaced by a cold, relentless focus on output. - Architectures are designed from the ground up for fast swapping and zero lock-in. This isn’t a philosophical stance. It’s a survival mechanism. AI is evolving too quickly for any single provider, framework, or foundation model to be the long-term answer. The model that outperforms today might fall behind in 90 days. Waiting for quarterly vendor updates or retraining internal teams is a luxury that high-performing enterprises can no longer afford. This is what it means to go ruthlessly multi-model. But here’s the deeper shift. Optionality is no longer an inefficiency. It’s strategy. Historically, having multiple tools for the same task was seen as overhead, a sign of organizational bloat. That logic breaks in the AI era. Optionality now means resilience, speed, and adaptability. It’s what allows companies to move at the pace of AI innovation, not be buried by it. There are critical implications for enterprise architecture here: 1/ Composable AI stacks are table stakes. Companies need to assume they’ll be plugging in and out different models, modalities, and tools constantly. 2/ Evaluation becomes a core competency. The companies that win will be those who build internal muscle around rapid, constant model benchmarking. Understanding which models are best at what tasks, on what data, and for which teams. 3/ Procurement and compliance need to catch up. A fast-switching architecture demands fast-switching contracts. Traditional enterprise procurement cycles of 60 to 90 day reviews, annual renewals and so on... simply don’t work when models improve weekly. Legal, security, and compliance teams must modernize for speed without compromising safety. 4/ Performance-based routing is the new normal. Just like the best data centers route traffic to where it can be served fastest and cheapest, AI workloads will increasingly be routed to the model that delivers the best outcome per task. Model-native load balancing is on the horizon. The ones who embrace this shift are not just experimenting with AI. They are operationalizing it. ♻️ Repost to share these insights! ➕ Follow Armand Ruiz for more

Scaling AI is less about model performance; it's about the infrastructure discipline and data maturity underneath it. One unexpected bottleneck companies often hit while trying to scale AI in production is “data lineage and quality debt.” Why it’s unexpected: Many organizations assume that once a model is trained and performs well in testing, scaling it into production is mostly an engineering and compute problem. But in reality, the biggest bottleneck often emerges from inconsistent, incomplete, or undocumented data pipelines—especially when legacy systems or siloed departments are involved. What’s the impact: Without robust data lineage (i.e., visibility into where data comes from, how it’s transformed, and who’s using it), models in production can silently drift or degrade due to upstream changes in data structure, format, or meaning. This creates instability, compliance risks, and loss of trust in AI outcomes in the regulated companies like Banking, Healthcare, Retail, etc. What’s the Solution: • Establish strong data governance frameworks early on, with a focus on data ownership, lineage tracking, and quality monitoring. • Invest in metadata management tools that provide visibility into data flow and dependencies across the enterprise. • Build cross-functional teams (Data + ML + Ops + Business) that own the end-to-end AI lifecycle, including the boring but critical parts of the data stack. • Implement continuous data validation and alerting in production pipelines to catch and respond to changes before they impact models. Summary: Scaling AI is less about model performance and more about the infrastructure discipline and data maturity underneath it.