Understanding Strategic and Reactive AI Innovation

AI in IT Ops is splitting into two camps - and your strategy decides your outcomes. 1. Reactive / Assistive AI Adds intelligence inside existing workflows: ticket triage, summarization, alert deduping, faster RCA. It accelerates humans and trims MTTD/MTTR - but only after something breaks or a user raises a hand. 2. Proactive / Autonomous AI Continuously watches telemetry, spots weak signals, predicts incidents, auto-remediates drift, tunes capacity before users notice. It reduces tickets altogether, not just handles them faster. Why it matters: - Fewer outages > Faster fixes - Prevented tickets free cycles for strategic work - Continuous optimization lowers infra & licensing waste - Better employee experience (issues “never happen”) Question for IT leaders: What % of your current “AI” effort is still reactive? Shift even 10–20% of that energy to proactive and measure avoided incidents, not just closed ones.

In a world where access to powerful AI is increasingly democratized, the differentiator won’t be who has AI, but who knows how to direct it. The ability to ask the right question, frame the contextual scenario, or steer the AI in a nuanced direction is a critical skill that’s strategic, creative, and ironically human. My engineering education taught me to optimize systems with known variables and predictable theorems. But working with AI requires a fundamentally different cognitive skill: optimizing for unknown possibilities. We're not just giving instructions anymore; we're co-creating with an intelligence that can unlock potential. What separates AI power users from everyone else is they've learned to think in questions they've never asked before. Most people use AI like a better search engine or a faster typist. They ask for what they already know they want. But the real leverage comes from using AI to challenge your assumptions, synthesize across domains you'd never connect, and surface insights that weren't on your original agenda. Consider the difference between these approaches: - "Write a marketing plan for our product" (optimization for known variables) - "I'm seeing unexpected churn in our enterprise segment. Act as a customer success strategist, behavioral economist, and product analyst. What are three non-obvious reasons this might be happening that our internal team would miss?" (optimization for unknown possibilities) The second approach doesn't just get you better output, it gets you output that can shift your entire strategic direction. AI needs inputs that are specific and not vague, provide context, guide output formats, and expand our thinking. This isn't just about prompt engineering, it’s about developing collaborative intelligence - the ability to use AI not as a tool, but as a thinking partner that expands your cognitive range. The companies and people who master this won't just have AI working for them. They'll have AI thinking with them in ways that make them fundamentally more capable than their competition. What are your pro-tips for effective AI prompts? #AppliedAI #CollaborativeIntelligence #FutureofWork

The Strategic Power of AI Partnerships Several friends have pointed out that I’ve carved out a niche in AI partnerships—and they’ve asked me to explain what that actually means. It’s a space with no single definition across industry or academia, so I’ve taken a stab at outlining what’s emerging and why it matters. You'll see this space heat up and many organizations forming their own AI partnerships team. You may disagree, and that’s welcome. Defining AI Partnerships Based on my research, AI partnerships are collaborative relationships between organizations that combine complementary strengths to develop, deploy, or enhance AI solutions. These partnerships can take many forms: - Big companies joining forces to build the AI stack - Startups using cloud platforms to launch AI applications - Governments investing in public infrastructure - Research labs working with industry to commercialize innovation This builds on what Satya Nadella, Microsoft and others have identified as three distinct evolutionary phases: Bespoke Phase – Custom alliances between firms for mutual gain. Data remains siloed. Platform Democratization Phase – Wider access to AI tools and infrastructure for developers and end users. Agent Ecosystem Phase – Rich, dynamic collaborations modeled on multi-agent systems and natural ecosystems. Industry Perspectives Definitions vary depending on who you ask. IBM emphasizes access—AI partnerships as a way to democratize tools and skills. PwC sees AI partnerships as a necessity in a complex global ecosystem. MIT Sloan breaks them down structurally into: - Bilateral collaborations - AI-driven ecosystems - Vendor relationships - Research consortia - Data-sharing networks Then you have groups like the Partnership on AI, a nonprofit consortium focused on ethical frameworks. Strategic Purposes From my research, five core strategic drivers keep coming up: Resource Access – Data, talent, and compute that one party alone lacks. Cost Sharing – AI infrastructure is expensive. Collaboration spreads the load. Market Reach – Especially in regulated sectors, partnerships help crack new verticals. Reputation & Risk – Ethics, transparency, and compliance are best handled with allies. Acceleration – Shared expertise speeds up innovation without everyone reinventing the wheel. The Strategic Advantage Very few organizations can build AI alone. These systems require layered competencies—data, algorithms, infra, domain knowledge—and partnerships are how firms plug their gaps. Cloud players are partnering with startups. Car companies are teaming with AI labs. Pharma firms are linking up with research universities. These aren’t side projects—they’re core strategies. Looking Ahead The winners in AI won’t just be the ones with the most powerful models. They’ll be the ones with the most powerful networks—the best ecosystem of partners. That’s the strategic edge. More details: https://lnkd.in/g8JRsE-G

Some of the best AI breakthroughs we’ve seen came from small, focused teams working hands-on, with structured inputs and the right prompting. Here’s how we help clients unlock AI value in days, not months: 1. Start with a small, cross-functional team (4–8 people) 1–2 subject matter experts (e.g., supply chain, claims, marketing ops) 1–2 technical leads (e.g., SWE, data scientist, architect) 1 facilitator to guide, capture, and translate ideas Optional: an AI strategist or business sponsor 2. Context before prompting - Capture SME and tech lead deep dives (recorded and transcribed) - Pull in recent internal reports, KPIs, dashboards, and documentation - Enrich with external context using Deep Research tools: Use OpenAI’s Deep Research (ChatGPT Pro) to scan for relevant AI use cases, competitor moves, innovation trends, and regulatory updates. Summarize into structured bullets that can prime your AI. This is context engineering: assembling high-signal input before prompting. 3. Prompt strategically, not just creatively Prompts that work well in this format: - “Based on this context [paste or refer to doc], generate 100 AI use cases tailored to [company/industry/problem].” - “Score each idea by ROI, implementation time, required team size, and impact breadth.” - “Cluster the ideas into strategic themes (e.g., cost savings, customer experience, risk reduction).” - “Give a 5-step execution plan for the top 5. What’s missing from these plans?” - “Now 10x the ambition: what would a moonshot version of each idea look like?” Bonus tip: Prompt like a strategist (not just a user) Start with a scrappy idea, then ask AI to structure it: - “Rewrite the following as a detailed, high-quality prompt with role, inputs, structure, and output format... I want ideas to improve our supplier onboarding process with AI. Prioritize fast wins.” AI returns something like: “You are an enterprise AI strategist. Based on our internal context [insert], generate 50 AI-driven improvements for supplier onboarding. Prioritize for speed to deploy, measurable ROI, and ease of integration. Present as a ranked table with 3-line summaries, scoring by [criteria].” Now tune that prompt; add industry nuances, internal systems, customer data, or constraints. 4. Real examples we’ve seen work: - Logistics: AI predicts port congestion and auto-adjusts shipping routes - Retail: Forecasting model helps merchandisers optimize promo mix by store cluster 5. Use tools built for context-aware prompting - Use Custom GPTs or Claude’s file-upload capability - Store transcripts and research in Notion, Airtable, or similar - Build lightweight RAG pipelines (if technical support is available) - Small teams. Deep context. Structured prompting. Fast outcomes. This layered technique has been tested by some of the best in the field, including a few sharp voices worth following, including Allie K. Miller!

The path ahead is not just about asking what #AI can do for us but about reorienting our approach to how we can strategically design, deploy, and empower our teams to thrive with AI. For those focusing on "Tailored Technologies" (unique to each organization and its context), it's imperative to delve deep into the specific strategic operations that drive your organization, tailoring AI solutions that amplify these areas to achieve peak effectiveness. On the other hand, "Pervasive Proficiencies" (ubiquitous across all employees) demands a democratization of AI knowledge, ensuring that every employee is equipped and empowered to elevate their work. By embracing this dual approach - addressing both #TailoredTechnologies and #PervasiveProficiencies - leaders can unlock a future where AI does not replace humans but instead, expands human potential exponentially. A Dual Approach to AI Integration Tailored Technologies: How can we strategically tailor AI solutions to seamlessly integrate with and enhance our core business functions while addressing specific organizational goals? Pervasive Proficiencies: How do we cultivate an environment where every employee, regardless of their role, is equipped and motivated to utilize AI tools to enhance their productivity and innovation? #GenAI #ChangeSuccess #ChangeManagement #AIAdoption