Ways to Drive Continuous Improvement in AI Innovation

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.

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!

As a Global Capability Center(GCC) Leader, the Onus Is on You—Will You Drive AI Transformation or Get Left Behind? Most GCCs were not designed with AI at their core. Yet, AI is reshaping industries at an unprecedented pace. If your GCC remains focused on traditional service delivery, it risks becoming obsolete. The responsibility to drive this transformation does not sit with IT teams or innovation labs alone—it starts with you. As a GCC leader, you must push beyond cost efficiencies and position your center as a strategic AI hub that delivers business impact. How to Transform an Existing GCC into an AI-Native GCC This shift requires clear, measurable objectives. Here are five critical OKRs (Objectives & Key Results) to guide your AI transformation. 1. Embed AI in Core Business Processes Objective: Move beyond AI pilots and integrate AI into everyday decision-making. Key Results: • Automate 20 percent or more of manual workflows within 12 months. • Deploy AI-powered analytics in at least three business-critical functions. • Reduce operational decision-making time by 30 percent using AI insights. 2. Reskill and Upskill Talent for AI Readiness Objective: Develop an AI-fluent workforce that can build, deploy, and manage AI solutions. Key Results: • Train 100 percent of employees on AI fundamentals. • Upskill at least 30 percent of engineers in MLOps and GenAI development. • Establish an internal AI guild to drive AI innovation and best practices. 3. Build AI Infrastructure and MLOps Capabilities Objective: Create a scalable AI backbone for your organization. Key Results: • Implement MLOps pipelines to reduce AI model deployment time by 50 percent. • Establish a centralized AI data lake for enterprise-wide AI applications. • Deploy at least five AI use cases in production over the next year. 4. Shift from AI as an Experiment to AI as a Business Strategy Objective: Ensure AI initiatives drive measurable business value. Key Results: • Ensure 50 percent of AI projects are directly linked to revenue growth or cost savings. • Develop an AI governance framework to ensure responsible AI use. • Integrate AI-driven customer experience enhancements in at least three markets. 5. Change the Operating Model: From Service Delivery to Co-Ownership Objective: Position the GCC as a leader in AI-driven transformation, not just an execution arm. Key Results: • Rebrand the GCC internally as a center of AI-driven innovation. • Secure C-level sponsorship for AI-driven initiatives. • Establish at least three AI innovation partnerships with startups or universities. The question is not whether AI will reshape your GCC. It will. The time to act is now. Are you ready to drive the AI transformation? Let’s discuss how to accelerate your GCC’s AI journey. Zinnov Mohammed Faraz Khan Namita Dipanwita ieswariya Mohammad Mujahid Karthik Komal Hani Amita Rohit Amaresh