How to Optimize Technology Implementations

Companies implementing AI without business process expertise waste 47% of their investment. Here's why understanding your business DNA matters first: • Transform operations by aligning AI with existing workflows, not forcing workflows to match AI capabilities - IBM research shows this approach reduces implementation time by 38%. • Leverage domain expertise to identify high-impact automation opportunities that preserve critical human judgment and institutional knowledge - preserving 82% of institutional knowledge according to Deloitte. • Build AI systems that speak your company's language - Genpact's research shows 3x better adoption when AI tools match existing business terminology and 57% faster time-to-value. • Deploy solutions that evolve with your processes - McKinsey reports 65% of successful AI implementations start with business logic mapping, resulting in 41% higher ROI. • Create feedback loops between AI systems and business users to continuously refine and improve outcomes - organizations with structured feedback mechanisms achieve 73% higher AI performance metrics. • Integrate AI gradually with proper change management - Harvard Business Review found companies taking this approach see 2.5x higher employee satisfaction with new technology. The difference between AI success and failure isn't just technology - it's understanding the business heartbeat that drives it. @genpact is here to help

Can AI implementation go from promise to performance? This excellent article by Virginie Glaenzer made me reflect on my own firm's journey with clients: 𝟭. 𝗘𝗺𝗯𝗲𝗱 𝗔𝗜 𝗶𝗻 𝗢𝘂𝘁𝗰𝗼𝗺𝗲𝘀, 𝗡𝗼𝘁 𝗝𝘂𝘀𝘁 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀 𝗦𝘁𝗼𝗽 𝗮𝘀𝗸𝗶𝗻𝗴 "𝗖𝗮𝗻 𝘄𝗲 𝗯𝘂𝗶𝗹𝗱 𝗶𝘁?" 𝗦𝘁𝗮𝗿𝘁 𝗮𝘀𝗸𝗶𝗻𝗴 "𝗛𝗼𝘄 𝘄𝗶𝗹𝗹 𝗶𝘁 𝗺𝗼𝘃𝗲 𝘁𝗵𝗲 𝗻𝗲𝗲𝗱𝗹𝗲?" The most successful AI deployments begin with ruthless clarity about success metrics. Before any model sees a training data plan, align your team on specific business KPIs: revenue uplift percentages, process cycle-time reductions, or customer satisfaction score improvements. Map each phase of your AI rollout directly to these metrics. 👉 The Pilot Principle: Purpose Over Proof Rather than broad "proofs of concept" that try to solve everything, launch tightly scoped pilots targeting high-impact, well-defined problems. A customer service team reducing response time by 40% is infinitely more valuable than a sophisticated model that impresses data scientists but confuses end users. 𝟮. 𝗦𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝗲𝗻 𝗖𝗵𝗮𝗻𝗴𝗲 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗬𝗼𝘂𝗿 𝗦𝘁𝗲𝗲𝗿𝗶𝗻𝗴 𝗖𝗼𝗺𝗺𝗶𝘁𝘁𝗲𝗲 𝗜𝘀 𝗠𝗶𝘀𝘀𝗶𝗻𝗴 𝗞𝗲𝘆 𝗩𝗼𝗶𝗰𝗲𝘀 Most AI governance includes executives and maybe some IT leaders. That's not enough. The organizations that scale AI successfully include data engineers, business-line owners, HR representatives, and—crucially—actual end users in their steering committees. 👉 Create Your AI Adoption Playbook Develop a living document that codifies your patterns and learnings. Include data validation checklists, model monitoring dashboards, rollback procedures, and success criteria templates. Think of it as your "AI operating system"—the foundation that makes everything else possible. 𝟯. 𝗕𝘂𝗶𝗹𝗱 𝗮𝗻 𝗔𝗜-𝗙𝗶𝗿𝘀𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗧𝗵𝗮𝘁 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗧𝗿𝗮𝗻𝘀𝗳𝗲𝗿𝘀 Foundation level: AI literacy for everyone (what it is, what it isn't, how it affects their role) Power-user level: Advanced workshops for teams who will work directly with AI tools Champion level: Certification tracks for "AI ambassadors" embedded in each department These AI champions become your implementation force multipliers—they understand both the technology and their department's specific needs, making them perfect bridges between technical and business teams. Make Success Contagious! 🤝 Source: https://lnkd.in/g3DRp4Kj

Why do so many legal technology implementations fail to deliver their promised value? Too often, legal teams rush to adopt the latest tools without first understanding their actual pain points. Here are the critical steps that separate successful implementations from costly failures: 📊 Start with Discovery, Not Solutions Map your current workflows meticulously. Track how long tasks take, where errors occur, and what frustrates your team most. 🎯 Set Measurable Goals Replace vague aspirations like "improve efficiency" with concrete targets: -Reduce contract turnaround by 30% -Eliminate 50% of manual compliance errors -Increase client intake capacity by 25% These specific metrics give you clear success criteria and help demonstrate ROI to stakeholders. 👥 Embrace Change Management Technology fails when people resist it. Appoint enthusiastic "technology champions" who can provide peer support and bridge the gap between IT and daily users. Their grassroots advocacy often proves more effective than top-down mandates. 🔄 Pilot, Learn, Iterate Test solutions with a small group for 6-8 weeks before full rollout. That same legal department reduced their NDA processing time to 1.5 hours and cut errors by 80% during their pilot. These wins built momentum for broader adoption. Remember: legal technology adoption is about solving real problems, not chasing innovation for its own sake. #legaltech #innovation #law #business #learning

Many onboarding leaders make this costly mistake when scaling their operations: They think linearly. 6 team members handling 30 customers? Let's hire 12 people to serve 60 customers. Sounds logical, right? Wrong. Here's what's really happening Your implementation specialists are drowning in manual work. They're setting up integrations, configuring your product, and conducting very repetitive long training sessions, instead of focusing on being a consultant to customers on the solutioning, best practices, and delivering value. They are turning into bottlenecks instead of functioning as accelerants for customer go-lives. What’s worse? Every new customer adds complexity, not just volume. Your team gets less efficient as you grow, not more. What leaders need to implement is strategic specialization. With over 30 monthly onboarding sessions involving 6+ team members, it's time to think differently. Instead of hiring more generalists, create these specialist roles: CS Operations Specialist - Sets up metrics and KPIs that actually matter - Analyzes bottlenecks from your tooling data - Optimizes configurations in your platforms - Becomes your efficiency engine Training & Enablement Lead - Creates onboarding playbooks for new hires - Builds customer LMS content that scales - Develops project templates that work - Innovates with "Reverse Demos" and hybrid training Integrations/Migrations Specialist - Handles complex technical implementations - Builds automation for repetitive processes - Creates playbooks for requirement gathering - Turns your most time-intensive work into streamlined operations When you do this, your implementation managers can focus purely on what they do best - putting their product and domain knowledge to use to implement customers consultatively, while dedicated specialists optimize the system around them. Plus, with the new agentic AI coming, these specialists can use AI to automate more and more of their activities with a goal of scaling outcomes from their team non-linearly To help you scale effectively, identify your biggest bottleneck first and then assign your first specialist to address it. The ROI is immediate and will compound.

Software alone doesn't get results. Services and implementation does—especially with AI. Two recent articles (linked in comments) made this clear: The a16z article explains how Salesforce, ServiceNow, and Workday succeeded because they invested heavily in implementation and services: “Each of these companies sells an enterprise platform requiring significant implementation, services, and support.” The Semafor piece describes a new role—"forward-deployed engineer"—someone who integrates AI directly into real-world operations. This person helps AI actually solve business problems instead of just looking impressive on a demo. To me, this sounds like a good old-fashioned services "Solution Architect" with some West Coast pixie dust sprinkled on top. Or "business analyst"... AI isn't plug-and-play. You need a skilled team to set it up, adapt it to your business, and keep it running smoothly. Without strong implementation, even great technology fails. Salesforce’s early margins were lower because of high upfront costs related to implementation and services. But this strategy built long-term success by creating loyal customers who saw real value. Yet many startups are scared of services and implementation revenue, as their short-sighted boards demand a focus on recurring revenue. In today's market, especially with new AI technologies, it's not enough to offer a shiny new product. You need the team behind it that can deliver real business outcomes. Good implementation teams: - Figure out how AI fits your specific business. - Ensure AI integrates smoothly with your current systems. - Provide ongoing support to handle real-world issues quickly. I've seen too many projects stall because people underestimated implementation. I've seen too many companies fail because execs and boards don't want services. With AI, skipping implementation isn't just risky—it's a guaranteed path to disappointment.