How to Align AI Projects with Business Innovation Goals

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

AI projects follow a common flow. And it is fundamentally wrong. The common project cadence follows exactly how data flows in the computer. Project 1: Curate data. Project 2: Implement forcasting. Project 3: Optimize and turn forecasts into decisions. It seems very natural to do it in this order. But what it means is that you will likely find out in the end that you expensively curated data that does not advance forecasts which do not help the optimization which provides plans that cannot be executed. Moreover, only the last step of turning information into decisions creates ROI. Yet, in many companies we find that data scientists are having so much fun creating models that step 3 is not taken at all. Instead, teams focus on generalizable data warehouses, feature stores, and very expensive infrastructure. All for the vague promise of « insights » that never move the needle. So, what should you do to create ROI quickly, while making sure that all your efforts are laser focussed on adding value? Analyze your business. Find the biggest lever where better decisions would lead to measurably better outcomes, like increased revenue or higher profit margins or less working cash. Then, ask what information and what understanding of the (eco)system are needed to make good decisions. Then, work backwards and take the last step first! 1. Implement the optimization algorithm, based on whatever data and forecasts are readily available. You immediately see if you can get an end-to-end connection from information to execution and you create ROI within months! 2. Next, quantify to what extent tighter forecasts would lead to better outcomes. Invest in better ML *if* it is worthwhile. 3. As a last step, see what additional curated data sets would improve the critical forecasts. Then, if it is worthwhile, and only then, invest in data infrastucture. Good luck deploying your AI!

Best and Worst Practices: GenAI Strategy and Implementation Since 2015, I’ve been immersed in the world of AI, representing the world’s largest law firm, speaking on AI Ethics for the ABA, founding a GenAI company in 2022 aimed at improving critical thinking, and teaching AI Ethics at UC Berkeley. I’ve spoken with hundreds of CEOs in 15 countries and analyzed nearly every major GenAI study over the past five years. Top 5 Mistakes Companies Make 1. Rushing Under Pressure CEOs, driven by board pressure, launch GenAI initiatives without a clear strategy, creating misalignment 2. Delegating to IT When GenAI is seen purely as a technical tool, IT leads often default to pilots and vendor solutions (often Microsoft-based), missing strategic and cultural integration. 3. Over-Reliance on Consultants Consultants often offer predictable playbooks, task automation, short-term cost savings that fail to drive lasting value. 4. Ineffective Pilots Many companies spend $3–5 million on slide shows and pilots that statistically fail 70–85% of of the time.¹ 5. AI-First Announcements Companies issue AI-focused press releases to signal innovation while lacking a a real plan Top 5 Best Practices 1. CEO-Led Initiatives The most successful GenAI transformations are led directly by CEOs who recognize the need to lead from the front² 2. Strategic and Cultural Shift Effective leaders see GenAI not as a tech project, but as a communications and organizational challenge that demands alignment from top to bottom.³ 3. Inclusive, Cross-Functional Engagement By involving legal, compliance, operations, and frontline teams from the outset, successful companies create a shared sense of purpose and resilience against resistance.⁴ 4. Preserving Customer and Brand Integrity Visionary companies avoid placing bots between their organization and its most valuable assets, customers and brand reputation. 5. Holistic Transformation Winning strategies integrate GenAI/ML into open-source, multi-LLM hybrid platforms that unify ecosystems, refine structured and unstructured data, not just to cut costs, but to drive revenue, and enable long-term advantage. Leadership. First Principles. Cross-Functional Inclusion. Technology as a platform. A combined automation and augmentation approach. And aggressive CEO communications and thought leadership. Generative AI doesn’t need to be a fear-driven event. Done right, it’s an opportunity to put the organization first and set a foundation for long-term success. ******************************************************************************** The trick with technology is to avoid spreading darkness at the speed of light Stephen Klein is Founder & CEO of Curiouser.AI, the only Generative AI platform and advisory focused on augmenting human intelligence through strategic coaching, reflection, and values-based decision-making. He also teaches AI Ethics at UC Berkeley. Learn more at curiouser.ai or connect via Hubble https://lnkd.in/gphSPv_e

AI needs a lot less conversation and more action. Most businesses don’t have an AI Action Plan, so they’re stuck in endless planning cycles and proof of concept purgatory. An AI Action Plan details: Opportunities the business is working on. These are use cases that align with the business and operating models. I recommend a 1:1 ratio of productivity/efficiency to revenue growth initiatives. Most internal efficiency AI should be bought vs. built. AI costs are dropping, and vendors serve internal use cases for less than the business can. Customer-facing products generate much higher returns. Monetization strategy. Define the critical pieces of AI go-to-market: Pricing, customer adoption, design, scaling, and optimization. Set the expectation that costs and returns must be estimated upfront. Break the “we won’t know until we build it” cycle that leads to proof of concept purgatory. Data and basic analytics make accurate, upfront opportunity size, cost, and return estimation feasible. Product roadmap. Break big initiatives into features that can be delivered quarterly. The one good thing about PoCs is rapid delivery and feedback cycles. Build products with that approach, and returns show up rapidly, too. Align feature delivery to develop cohesive products that support a use case, workflow, process of work, or customer need. I wrote a how-to guide for building AI Action Plans with a template you can use here: https://lnkd.in/gmJZ63Cf

While focusing on use cases / tasks is the fastest way to pilot AI and create efficiencies in your organization, taking a Problem-Based Model approach accelerates innovation and growth, and creates greater long-term impact on enterprise value. In the Problem-Based Model, you have a known pain point or challenge that may be solved more efficiently, and at scale, with AI. You start with clearly defined problem statements. A strong problem statement includes a value statement that establishes what it’s worth to solve the problem. This helps ensure that the project is worth the investment of resources, and is essential to prioritizing which problems to pursue. * * * * *  Example 1: Audience [Problem] Our new subscriber growth is down 20% over the same quarter last year. [Value] Subscribers are valued at $100 each in our valuation model, so a drop of 10,000 subscribers is $1,000,000 in revenue. Example 2: Innovation [Problem] Our growth in existing verticals has stalled, and we need to identify new markets and product ideas that can unlock massive value for the organization. [Value] Based on historical data and market research, we believe there are two new verticals that could generate $10M+ each over the next 3 - 5 years. Example 3: Churn [Problem] We saw a dramatic spike in customer churn last quarter, resulting in an MRR drop for the first time in two years. [Value] The quarterly loss was $80,000 MRR, or $960,000 ARR. * * * * * This model can be applied to any problem type in your organization, such as: - Audience - Awareness - Churn - Costs - Decisioning - Efficiency - Forecasting - Innovation - Leads - Loyalty - Personalization - Pricing - Productivity - Revenue - Sales ProblemsGPT is built to help you craft and refine your problem statements, and then draft preliminary strategic plans to build a smarter, AI-forward business. I originally released v1.0 in fall 2024. I made a few tweaks this weekend and updated to v2.0. See an example thread in the images below, and then try it out for yourself (link in comments). Happy problem solving (and Happy Father’s Day)!

A recent MIT Media Lab report found that a staggering 95% of generative AI investments have produced zero measurable returns. Is AI overhyped? Not exactly. The problem is strategic. Many leaders are falling into the 'AI Experimentation Trap,' repeating the same mistakes made during the early days of digital transformation. Here’s how to avoid it: 1. Stop Funding Scattered Pilots: The primary pitfall is investing in isolated AI experiments that aren't tied to core business value. These projects may look impressive in a demo but fail to scale or impact the bottom line. It's a classic case of activity without progress. 2. Shift from Hype to Customer Problems: As Gartner suggests, gen AI is entering the 'trough of disillusionment.' Instead of chasing hype, disciplined leaders focus on solving high-intensity, high-frequency customer problems. AI should be a tool to serve a purpose, not the purpose itself. 3. Design for Scale with Empowered Teams: Successful AI implementation isn't about massive, top-down projects. It’s about running low-cost, iterative experiments led by small, empowered 'ninja' teams. These teams are tasked with not just proving a concept, but designing it for scale from the outset. Takeaway: View AI as one component in your larger shift toward becoming a digitally driven organization. The goal isn't to 'do AI,' but to use technology, including AI, to transform your operations and create better customer outcomes. True ROI comes from disciplined strategy, not technological tourism. Source: Harvard Business Review https://lnkd.in/e6hV5cDT #AI #BusinessStrategy #Leadership #DigitalTransformation #Innovation

If your team is looking to deploy AI Agents or integrate AI more deeply across the enterprise, your business analysis practices need to evolve. Here are a few essentials: 🔹 Think end-to-end, not application by application 🔹 Treat the work as a change initiative, not a technical project 🔹 Measure success by behavior change, not speed 🔹 Focus on human and AI collaboration, not just automation 🔹 Be deliberate in process analysis; not every step is a good fit for probabilistic technology, but some can benefit greatly Business analysis of the full process, data flow, and user journey is a must if AI is going to deliver real value. 👉 What would you add to this list? I dive deeper into this in my new LinkedIn Learning course: Agentic AI for Business Analysis: https://lnkd.in/gm47bKh3 And, discover how AI Agents and AI projects are changing the BA role with other BA Managers in my BA Manager Community. www.BA-Cube.mn.co

Why do AI initiatives tend to fail? As painful as it may sound, business AI projects are still failing far more often then they succeed. It's frustrating, and uncalled for, but it's true. Here are the 4 biggest reasons, and suggested remedies: 1. Misaligned Objectives - Projects frequently commence without a clear understanding of the business problem they aim to solve.  Solution: Start with the business problem, not the algorithm. Collaborate across business and data teams to define precise use cases with measurable KPIs. 2. Prediction Without Action - AI models might provide accurate forecasts, such as identifying potential customer churn. However, without integrating these insights into actionable business strategies, the predictions remain underutilized. Solution: Design AI systems with "decision-backward" thinking: first define the action the model should drive, then build models that connect seamlessly into operational workflows and trigger automated or guided interventions. 3. Data Quality and Accessibility Issues - The efficacy of AI models heavily depends on the quality and availability of data. Inadequate, biased, or siloed data can compromise model performance and outcomes. Solution: Invest early in data infrastructure, governance, and enrichment. Prioritize use cases where relevant historical data exists. Embed automation tools together with domain experts to accelerate the path from raw data to AI-ready datasets. 4. Organizational Resistance and Change Management - Resistance from stakeholders and insufficient change management can hinder adoption and integration of AI. Solution: Involve end-users from the start, communicate benefits clearly and provide training. Read more here: https://lnkd.in/dwrJS7q5