Scaling AI Solutions In Enterprises

After deploying over 200+ AI POCs across my entire career and across a variety of industries, I learned a hard way truth! The biggest threat to AI success has nothing to do with technology — and everything to do with the people. Years ago, we built the perfect AI system. Cutting-edge models (for that time). Impeccable accuracy. Seamless deployment. And then… only 7% of the anticipated user base used it. It sat there — untouched — while the business teams quietly returned to their old, familiar excel and “phone a friend” processes. The system worked. But the people didn’t trust it, didn’t understand it, and didn’t see how it fit into their day-to-day reality. This is how so many organizations get stuck in “Perpetual POC Purgatory” (copyright 2025 Sol Rashidi) — where brilliant proofs of concept never make it into real, scalable use. The Real Lesson: Scale Comes from Adoption, Not Pushing a model into Production After overseeing hundreds of AI initiatives, I developed the 3E Framework — a practical approach to break out of POC purgatory and build AI solutions that people actually use. This framework is copyrighted: © 2025 Sol Rashidi. All rights reserved. 𝟭. 𝗘𝗻𝗴𝗮𝗴𝗲: Don't just announce AI—make stakeholders co-creators from day one. When marketing, operations, and finance help select use cases and metrics, they become invested gardeners rather than skeptical observers. 𝟮. 𝗘𝗱𝘂𝗰𝗮𝘁𝗲: Theory creates anxiety; hands-on experience builds confidence. This isn't about extensive technical training—it's about demystifying AI through guided exposure over months, not days. When done right, deployment day brings curiosity instead of resistance. 𝟯. 𝗘𝗺𝗯𝗲𝗱: The most successful implementations feel like natural extensions of how people already work. For example, integrate that new AI customer segmentation tool directly into the exact dashboards your teams already use daily. Scaling isn't about more sophisticated algorithms—it's about human adoption at every level. Think of AI systems like exotic trees in your organizational garden—you can select perfect specimens and use cutting-edge cultivation techniques, but if your local gardeners don't know how to nurture them, those trees will never flourish. The next time you face resistance to AI scaling, remember: technical hurdles are often the easiest to overcome. The real transformation happens when you nurture the human ecosystem around your AI. That is how you scale AI across the workforce.

Today, NIST released the initial preliminary draft of the Cybersecurity Framework Profile for Artificial Intelligence (Cyber AI Profile), a community profile built on NIST CSF 2.0 to help organizations manage cybersecurity risk in an AI-driven world. A key section of this draft is Section 2.1, which introduces three Focus Areas that explain how AI and cybersecurity intersect in practice: 1. Securing AI System Components (Secure) AI systems introduce new assets that must be secured; models, training data, prompts, agents, pipelines, and deployment environments. This focus area emphasizes treating AI components as first-class cybersecurity assets, integrating them into governance, risk assessments, protection controls, and monitoring processes. It reinforces that AI risk should not be siloed from enterprise cybersecurity risk management. 2. Conducting AI-Enabled Cyber Defense (Defend) AI is not just something to protect, it is also a powerful defensive capability. This area focuses on using AI to enhance detection, analytics, automation, and response across security operations. At the same time, it recognizes the risks of over-reliance on automation, model integrity concerns, and the need for human oversight when AI supports security decision-making. 3. Thwarting AI-Enabled Cyber Attacks (Thwart) Adversaries are increasingly using AI to scale phishing, evade detection, and automate attacks. This focus area addresses how organizations must anticipate and counter AI-enabled threats by building resilience, improving detection of AI-driven attack patterns, and preparing for a rapidly evolving threat landscape where AI is weaponized. Why This Matters Together, Secure, Defend, and Thwart provide a practical structure for aligning AI initiatives with existing cybersecurity programs. By mapping AI-specific considerations to CSF 2.0 outcomes (Govern, Identify, Protect, Detect, Respond, Recover), the Cyber AI Profile helps organizations integrate AI security into familiar risk management practices. This is a preliminary draft, and NIST is seeking public feedback through January 30, 2026. If your organization is building, deploying, or defending with AI, now is the time to review and contribute. 🔗 https://lnkd.in/e-ETZXH8

The 6-Layer AI Security Stack Every Organization Will Eventually Need Most companies don't have an AI strategy. They have an AI chatbot. And those are not the same thing. The companies that will struggle with AI over the next few years won't be the ones with the weakest models. They'll be the ones with the weakest security. Because securing AI isn't about adding one tool. It's about building an entire security stack. Here's what a modern AI Security Stack looks like: 1. Identity & Access Layer Control who can access models, agents, APIs, and sensitive AI workflows. Without identity controls, anyone with access can become your biggest risk. 2. Data Protection Layer Protect sensitive information before it ever reaches an LLM. • Mask PII • Encrypt data • Tokenize sensitive fields If your prompts contain confidential data, your security starts before inference. 3. Prompt & Input Security Layer AI models trust their inputs. Attackers know that. Defend against: • Prompt injection • Jailbreak attempts • Data extraction attacks Every prompt should be treated as untrusted input. 4. Governance & Compliance Layer Security isn't only technical. It's also accountability. Track: • Risk classifications • Audit trails • AI decisions • Regulatory compliance AI without governance becomes impossible to trust at scale. 5. Output Validation Layer Never assume the model is right. Validate every critical response for: • Hallucinations • Policy violations • Compliance issues • Unsafe recommendations Trust... but verify. 6. Monitoring & Observability Layer Deployment isn't the finish line. It's where security actually begins. Continuously monitor: • Model drift • Unusual behavior • Performance changes • Security events • Response quality You can't defend what you can't observe. The biggest misconception about AI security? People think it's one product. In reality, it's multiple security layers working together. Just like cloud security evolved from firewalls to full security architectures... AI security is following the same path. The organizations building these layers today won't just deploy AI faster. They'll deploy it with confidence. AI is becoming part of every business. AI security needs to become part of every architecture. Which layer do you think organizations are overlooking the most right now? Follow Marcel Velica for practical insights on AI Security, Cybersecurity, and Enterprise AI. If you found this useful, repost it so more security professionals can join the conversation.

The real challenge in AI today isn’t just building an agent—it’s scaling it reliably in production. An AI agent that works in a demo often breaks when handling large, real-world workloads. Why? Because scaling requires a layered architecture with multiple interdependent components. Here’s a breakdown of the 8 essential building blocks for scalable AI agents: 𝟭. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 Frameworks like LangGraph (scalable task graphs), CrewAI (role-based agents), and Autogen (multi-agent workflows) provide the backbone for orchestrating complex tasks. ADK and LlamaIndex help stitch together knowledge and actions. 𝟮. 𝗧𝗼𝗼𝗹 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 Agents don’t operate in isolation. They must plug into the real world:  • Third-party APIs for search, code, databases.  • OpenAI Functions & Tool Calling for structured execution.  • MCP (Model Context Protocol) for chaining tools consistently. 𝟯. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 Memory is what turns a chatbot into an evolving agent.  • Short-term memory: Zep, MemGPT.  • Long-term memory: Vector DBs (Pinecone, Weaviate), Letta.  • Hybrid memory: Combined recall + contextual reasoning.  • This ensures agents “remember” past interactions while scaling across sessions. 𝟰. 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 Raw LLM outputs aren’t enough. Reasoning structures enable planning and self-correction:  • ReAct (reason + act)  • Reflexion (self-feedback)  • Plan-and-Solve / Tree of Thought These frameworks help agents adapt to dynamic tasks instead of producing static responses. 𝟱. 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲 Scalable agents need a grounding knowledge system:  • Vector DBs: Pinecone, Weaviate.  • Knowledge Graphs: Neo4j.  • Hybrid search models that blend semantic retrieval with structured reasoning. 𝟲. 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝗘𝗻𝗴𝗶𝗻𝗲 This is the “operations layer” of an agent:  • Task control, retries, async ops.  • Latency optimization and parallel execution.  • Scaling and monitoring with platforms like Helicone. 𝟳. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 & 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 No enterprise system is complete without observability:  • Langfuse, Helicone for token tracking, error monitoring, and usage analytics.  • Permissions, filters, and compliance to meet enterprise-grade requirements. 𝟴. 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 & 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲𝘀 Agents must meet users where they work:  • Interfaces: Chat UI, Slack, dashboards.  • Cloud-native deployment: Docker + Kubernetes for resilience and scalability. Takeaway: Scaling AI agents is not about picking the “best LLM.” It’s about assembling the right stack of frameworks, memory, governance, and deployment pipelines—each acting as a building block in a larger system. As enterprises adopt agentic AI, the winners will be those who build with scalability in mind from day one. Question for you: When you think about scaling AI agents in your org, which area feels like the hardest gap—Memory Systems, Governance, or Execution Engines?

At Dallas Ignite last week, we walked Palo Alto Networks clients through what we call a unified approach to securing AI. One mandate. Two halves. Sharing here how each half maps to what we cover in our Defender's Guide to the Frontier AI Impact on Cybersecurity. Mandate One: Defending against frontier models being weaponized against your architecture. The familiar half. Adversaries with AI capability are accelerating everything. Faster recon. Faster exploitation. Faster lateral movement once they're in. Four actions that matter right now: 1️⃣ Find and fix vulnerabilities before adversaries do. Frontier AI has become the primary source of vulnerability discovery. Target a 72-hour patch cycle for criticals, with automated deployment where change risk allows. Start by scanning your own code and supply chain. 2️⃣ Aggressively reduce your attack surface. Run an external assessment now. Eliminate internet-reachable assets that shouldn't be reachable. Harden the ones that need to stay up. 3️⃣ Deploy unified protection across every layer. Endpoint, network, identity, cloud, application. Patchwork architectures (the average enterprise runs 80-plus tools) cannot operate at AI speed. Full stop. 4️⃣ Modernize security operations to detect and respond at machine speed. Single-digit minute MTTR is the target. That requires consolidated tooling and AI-assisted triage, not more analysts. Mandate Two: Securing the rapid deployment of AI apps and agents inside your own enterprise. The half most organizations are behind on. AI is showing up in four places: browser agents executing workflows, endpoint copilots and GenAI assistants, AI baked into vendor and internal apps, and enterprise agents taking autonomous actions across systems. Same four actions. Different operational specifics. 1️⃣ Find and inventory what's actually running. Do a structured discovery across all four areas. Most exercises surface years of accumulated deployments nobody centrally tracked, including AI shipped quietly through software updates. 2️⃣ Reduce the unauthorized AI footprint. Shut down deployments that bypassed governance. Then build the policy that prevents it from happening again. 3️⃣ Deploy governance across AI permissions and decision authority. Who can stand up an agent. Who approves what permissions. Who reviews what actions the agent has taken. This is the layer most enterprises have not built yet. 4️⃣ Modernize incident response for AI-specific failure modes. Compromised agents. Unauthorized actions. Permission escalation. Run a tabletop this quarter that includes at least one AI-agent scenario. Both mandates follow the same sequence: visibility first, then assessment, then protection. At PANW we frame this as Discover, Assess, Protect. The framework is consistent. The specifics are not. Where to start: if you haven't run a structured discovery across those four areas in the last six months, that's your first move. You cannot secure what you cannot see.

AI customer service will fail if brands treat it as a cost-cutting project. Gladys and I see agentic AI as a major shift from basic chatbots. These systems can interpret requests, make decisions and complete actions across connected workflows. Gartner predicts that agentic AI could resolve 80% of common customer service issues without human intervention by 2029. That figure will attract attention in boardrooms. The harder question is whether those resolutions will strengthen or weaken the customer relationship. An AI agent needs more than a polished interface. It needs: * Accurate product and customer data * Access to the right systems and workflows * Clear limits on the decisions it can make * A direct route to a person when judgement or empathy is required * A handover that includes the customer’s full context Without these foundations, AI becomes another layer customers must fight through. From a marketing perspective, every service interaction shapes the brand. A fast response has little value when it is incorrect, impersonal or difficult to resolve. The role of AI should be clear: handle routine, information-heavy work and give service teams more capacity for cases requiring judgement, care and accountability. Human handover should never feel like starting again. Customers should not have to repeat their issue, resend information or explain why the matter is urgent. The human agent should receive the history, relevant data and actions already taken. The strongest service model will assign each task to the resource best placed to handle it. AI for speed and scale. People for judgement and trust. For leaders investing in agentic AI, the real measure is not how many conversations are automated. It is how many customer problems are resolved without damaging the relationship. Are we using AI to remove customer effort, or simply moving that effort somewhere else? Sources: Azumo, Gartner and Zendesk ⭐ Co-created with Gladys Ng, Top 20 Creator in Marketing and Sales on LinkedIn Singapore.

13 national cyber agencies from around the world, led by #ACSC, have collaborated on a guide for secure use of a range of "AI" technologies, and it is definitely worth a read! "Engaging with Artificial Intelligence" was written with collaboration from Australian Cyber Security Centre, along with the Cybersecurity and Infrastructure Security Agency (#CISA), FBI, NSA, NCSC-UK, CCCS, NCSC-NZ, CERT NZ, BSI, INCD, NISC, NCSC-NO, CSA, and SNCC, so you would expect this to be a tome, but it's only 15 pages! It is refreshing to see that the article is not solely focused on LLMs (eg. ChatGPT), but defines Artificial Intelligence to include Machine Learning, Natural Language Processing, and Generative AI (LLMs), while acknowledging there are other sub-fields as well. The challenges identified (with actual real-world examples!) are: 🚩 Data Poisoning of an AI Model: manipulating an AI model's training data, leading to incorrect, biased, or malicious outputs 🚩 Input Manipulation Attacks: includes prompt injection and adversarial examples, where malicious inputs are used to hijack AI model outputs or cause misclassifications 🚩 Generative AI Hallucinations: generating inaccurate or factually incorrect information 🚩 Privacy and Intellectual Property Concerns: challenges in ensuring the security of sensitive data, including personal and intellectual property, within AI systems 🚩 Model Stealing Attack: creating replicas of AI models using the outputs of existing systems, raising intellectual property and privacy issues The suggested mitigations include generic (but useful!) cybersecurity advice as well as AI-specific advice: 🔐 Implement cyber security frameworks 🔐 Assess privacy and data protection impact 🔐 Enforce phishing-resistant multi-factor authentication 🔐 Manage privileged access on a need-to-know basis 🔐 Maintain backups of AI models and training data 🔐 Conduct trials for AI systems 🔐 Use secure-by-design principles and evaluate supply chains 🔐 Understand AI system limitations 🔐 Ensure qualified staff manage AI systems 🔐 Perform regular health checks and manage data drift 🔐 Implement logging and monitoring for AI systems 🔐 Develop an incident response plan for AI systems This guide is a great practical resource for users of AI systems. I would interested to know if there are any incident response plans specifically written for AI systems - are there any available from a reputable source?

97% of orgs faced AI breaches in 2025 had zero access controls in place. Not weak; Not outdated controls. Zero [Source: IBM] Meanwhile, 35% of real-world AI security incidents came from simple prompts some causing $100K+ in losses without a single line of code [Source: Adversa] The gap between AI deployment speed and security implementation is only widening. Hence I am sharing 10 security checkpoints every AI agent needs before touching production systems: ✅ Output Validation → Middleware that verifies decisions against rules before execution. Traffic lights for AI actions. ✅ Access Control → Least privilege enforcement. Role-based permissions that limit what agents can touch. ✅ Credential Safety → Secrets management that keeps API keys away from prompts and logs. Store them like vault keys, not sticky notes. The other 7 checks are in the carousel including rate limiting that prevents runaway loops and human approval for high-stakes decisions 👇 Most teams rush deployment. Security becomes an afterthought until something breaks. Tell me your story: what security measure has prevented a disaster in your AI system? Follow me, Bhavishya Pandit, for practical AI production insights from the trenches 🔥 #ai #security #agents

Scaling AI agents is not just about using a bigger model. It is about designing the system so agents can handle more users, more tools, more context, and more tasks without breaking latency, cost, or reliability. As agent workloads grow, the bottleneck moves from “can the model answer?” to: Can the system route requests correctly? Can it reuse repeated work? Can tools run in parallel? Can context stay small and relevant? Can failures be observed and recovered? Here are 9 agent scaling strategies every AI engineer should understand: 𝗛𝗼𝗿𝗶𝘇𝗼𝗻𝘁𝗮𝗹 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 Distribute traffic across multiple agent instances to improve throughput and resilience. 𝗠𝗼𝗱𝗲𝗹 𝗥𝗼𝘂𝘁𝗶𝗻𝗴 Send each task to the right model based on complexity, cost, speed, and modality. 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 Reuse repeated responses, retrieval results, or computations to reduce latency and model calls. 𝗤𝘂𝗲𝘂𝗲-𝗕𝗮𝘀𝗲𝗱 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 Place tasks in queues so workers can process workloads reliably and asynchronously. 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲 Run independent tools at the same time, such as search, database calls, APIs, or code tools. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Trim, summarize, and prioritize only the most relevant context before sending it to the model. 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗢𝗳𝗳𝗹𝗼𝗮𝗱𝗶𝗻𝗴 Use RAG to fetch only the knowledge needed instead of loading everything into the agent context. 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗗𝗲𝗹𝗲𝗴𝗮𝘁𝗶𝗼𝗻 Assign specialized tasks to research, coding, analyst, or reviewer agents coordinated by a manager. 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗔𝘂𝘁𝗼𝘀𝗰𝗮𝗹𝗶𝗻𝗴 Track latency, errors, tokens, tool failures, and demand so capacity adjusts automatically. The real lesson: Agents do not scale only through intelligence. They scale through architecture. Routing, caching, queues, parallelism, retrieval, monitoring, and autoscaling turn agent demos into production systems. Which scaling strategy do you think matters most for enterprise agents?

Metacognition is the master capability. A study of 250 employees of a technology consulting firm showed what made the difference in how AI was used, and how to increase the value of AI in the organization. "We find that generative AI can indeed boost employee creativity, but the gains are not universal. Specifically, employees with stronger metacognition—the ability to plan, evaluate, monitor, and refine their thinking—are more likely to experience creative gains from using generative AI, because they can use it more effectively to acquire the cognitive job resources that fuel creativity." Here are the pragmatic approaches suggested by the four action points in the article: 1️⃣ Help employees use AI to expand the cognitive job resources that fuel creativity. Encourage employees to use AI to gather broader information, test multiple angles, and offload routine tasks that drain mental bandwidth. Set the expectation that AI should be used to widen thinking and create space for higher-value creative problem-solving. 2️⃣ Raise awareness that metacognition is the engine of AI-supported creativity. Teach employees to question, test, and refine AI outputs rather than accept the first answer they receive. Reinforce habits of checking assumptions, probing for alternatives, and treating AI responses as inputs to improve, not endpoints to adopt. 3️⃣ Build metacognitive skills through targeted and scalable training. Provide practical training that helps employees plan how to use AI, monitor the quality of outputs, and evaluate what to keep or change. Use real examples, short exercises, and simple checklists to build stronger day-to-day habits of reflective AI use. 4️⃣ Design workflows that promote active, iterative engagement with AI. Redesign workflows so employees use AI across multiple rounds of idea generation, comparison, critique, and refinement. Build in prompts, discussions, and review steps that require people to engage actively with AI instead of relying on default answers. ------ For more insights into the edge of value from Humans + AI join Humans + AI Explorers community for free https://lnkd.in/gmhxvikq