How to Deploy AI Systems in Enterprises

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

🎯 The CIO's Organizational Playbook for the AI Era... I recently spoke with a CIO friend about how IT teams are changing. Our discussion made me think about what sets apart IT teams that succeed with AI from those that don’t. I looked over my research and reviewed my interviews with other leaders. This information is too valuable not to share: ✓ Build AI-Ready Capabilities 🟢 Establish continuous learning programs focused on practical AI applications 🟢 Implement cross-functional training to bridge technical/business gaps 🟢 Prioritize hands-on AI workshops over theoretical certifications ✓ Master AI Risk Management 🟢 Develop processes to identify and mitigate technical failures early 🟢 Create a strategic AI roadmap with clear risk contingency protocols 🟢 Align all AI initiatives with broader business objectives ✓ Drive Stakeholder Engagement 🟢 Build a cross-functional AI coalition (executives, HR, business units) 🟢 Communicate AI initiatives with transparency to reduce resistance 🟢 Document tangible benefits to secure continued buy-in ✓ Implement with Agility 🟢 Replace waterfall approaches with iterative AI development 🟢 Focus on quick prototyping and real-world testing 🟢 Ensure infrastructure scalability supports AI growth ✓ Lead with AI Ethics 🟢 Train teams on bias identification and mitigation techniques 🟢 Establish clear governance frameworks with accountability 🟢 Make responsible AI deployment non-negotiable ✓ Transform Your Talent Strategy 🟢 Enhance IT roles to integrate AI responsibilities 🟢 Create peer mentoring programs pairing AI experts with domain specialists 🟢 Cultivate an AI-positive culture through early wins ✓ Measure What Matters 🟢 Set specific AI KPIs that link directly to business outcomes 🟢 Implement continuous feedback loops for ongoing refinement 🟢 Track both technical metrics and organizational adoption rates The organizations mastering these elements aren't just surviving the AI transition—they're thriving because of it. #digitaltransformation #changemanagement #leadership #CIO

80% of enterprise AI projects are draining your budget with zero ROI. And it's not the technology that's failing: It's the hidden costs no one talks about. McKinsey's 2025 State of AI report reveals a startling truth: 80% of organizations see no tangible ROI impact from their AI investments. While your competitors focus on software licenses and computing costs, five hidden expenses are sabotaging your ROI: 1/ The talent gap: ↳ AI specialists command $175K-$350K annually. ↳ 67% of companies report severe AI talent shortages. ↳ 13% are now hiring AI compliance specialists. ↳ Only 6% have created AI ethics specialists. When your expensive new hire discovers you lack the infrastructure they need to succeed, they will leave within 9 months. 2/ The infrastructure trap: ↳ AI workloads require 5-8x more computing power than projected. ↳ Storage needs can increase 40-60% within 12 months. ↳ Network bandwidth demands can surge unexpectedly. What's budgeted as a $100K project suddenly demands $500K in infrastructure. 3/ The data preparation nightmare: ↳ Organizations underestimate data prep costs by 30-40%. ↳ 45-70% of AI project time is spent on data cleansing (trust me, I know). ↳ Poor data quality causes 30% of AI project failures (according to Gartner). Your AI model is only as good as your data. And most enterprise data isn't ready for AI consumption. 4/ The integration problem: ↳ Legacy system integration adds 25-40% to implementation costs. ↳ API development expenses are routinely overlooked. ↳ 64% of companies report significant workflow disruptions. No AI solution can exist in isolation. You have to integrate it with your existing tech stack, or it will create expensive silos. 5/ The governance burden: ↳ Risk management frameworks cost $50K-$150K to implement. ↳ New AI regulations emerge monthly across global markets. Without proper governance, your AI can become a liability, not an asset. The solution isn't abandoning AI. It's implementing it strategically with eyes wide open. Here's the 3-step framework we use at Avenir Technology to deliver measurable ROI: Step 1: Define real success metrics: ↳ Link AI initiatives directly to business KPIs. ↳ Build comprehensive cost models including hidden expenses. ↳ Establish clear go/no-go decision points. Step 2: Build the foundation first: ↳ Assess and upgrade infrastructure before deployment. ↳ Create data readiness scorecards for each AI use case. ↳ Invest in governance frameworks from day one. Step 3: Scale intelligently: ↳ Start with high-ROI, low-complexity use cases. ↳ Implement in phases with reassessment at each stage. Organizations following this framework see 3.2x higher ROI. Ready to implement AI that produces real ROI? Let's talk about how Avenir Technology can help. What AI implementation challenge are you facing? Share below. ♻️ Share this with someone who needs help implementing. ➕ Follow me, Ashley Nicholson, for more tech insights.