Integrating AI Innovations into Existing Processes

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

Exactly a year ago, we embarked on a transformative journey in application modernization, specifically harnessing generative AI to overhaul one of our client’s legacy systems. This initiative was challenging yet crucial for staying competitive: - Migrating outdated codebases - Mitigating high manual coding costs - Integrating legacy systems with cutting-edge platforms - Aligning technological upgrades with strategic business objectives Reflecting on this journey, here are the key lessons and outcomes we achieved through Gen AI in application modernization: [1] Assess Application Portfolio. We started by analyzing which applications were both outdated and critical, identifying those with the highest ROI for modernization.  This targeted approach helped prioritize efforts effectively. [2] Prioritize Practical Use Cases for Generative AI. For instance, automating code conversion from COBOL to Java reduced the overall manual coding time by 60%, significantly decreasing costs and increasing efficiency. [3] Pilot Gen AI Projects. We piloted a well-defined module, leading to a 30% reduction in time-to-market for new features, translating into faster responses to market demands and improved customer satisfaction. [4] Communicate Success and Scale Gradually. Post-pilot, we tracked key metrics such as code review time, deployment bugs, and overall time saved, demonstrating substantial business impacts to stakeholders and securing buy-in for wider implementation. [5] Embrace Change Management. We treated AI integration as a critical change in the operational model, aligning processes and stakeholder expectations with new technological capabilities. [6] Utilize Automation to Drive Innovation. Leveraging AI for routine coding tasks not only freed up developer time for strategic projects but also improved code quality by over 40%, reducing bugs and vulnerabilities significantly. [7] Opt for Managed Services When Appropriate. Managed services for routine maintenance allowed us to reallocate resources towards innovative projects, further driving our strategic objectives. Bonus Point: Establish a Center of Excellence (CoE). We have established CoE within our organization. It spearheaded AI implementations and established governance models, setting a benchmark for best practices that accelerated our learning curve and minimized pitfalls. You could modernize your legacy app by following similar steps! #modernization #appmodernization #legacysystem #genai #simform — PS. Visit my profile, Hiren Dhaduk, & subscribe to my weekly newsletter: - Get product engineering insights. - Catch up on the latest software trends. - Discover successful development strategies.

Most AI projects fail for a very boring reason. They are designed like IT projects. MIT research found that out of 300+ enterprise AI pilots, only 5% created measurable business value. That statistic has barely improved in years. The problem is not the models or the data. It is the framing. Leaders keep asking: “Where can I apply AI in my existing processes?” The better question is: “What would this process look like if built today with AI at the center?” Jon Cooke's recent post on Agent APE highlights the same shift I have spoken about: stop trying to fit AI into workflows that were never designed to adapt. Start engineering AI-native processes that can learn, evolve, and improve in production. How do you actually make that happen? A few principles matter: 1. Start with outcomes, not outputs Most initiatives target outputs (more invoices processed, faster OCR, better NPS). The right framing is outcomes (cash flow acceleration, fraud detection, lifetime customer value). Outcomes drive re-engineering, not patchwork. 2. Engineer, do not design Traditional BPM meant mapping “as-is” and “to-be” diagrams. But design assumes you know the end state. With AI, the end state is discovered. Treat it as engineering: build, test, learn, and evolve with AI as a co-builder. 3. Capture processes as living systems Static swimlanes and PowerPoints do not work anymore. Represent processes as digital twins and data products that continuously learn. This allows AI to experiment with flows and adjust dynamically without rewriting the entire operating model. 4. Replace incrementally, not all at once The Strangler Pattern works. Do not freeze the business for two years of re-platforming. Start with one slice of the process, re-engineer it with AI-native methods, and expand from there. Over time, the old shell gives way to intelligent flows. 5. Optimize for adaptability, not just accuracy Too many projects chase 99 percent accuracy in narrow tasks. The real advantage comes from adaptability, the ability for processes to evolve as regulations, customer behaviors, and market conditions change. The lesson: AI process engineering is not a project. It is an operating discipline. Executives who understand this will stop measuring success by pilots launched and start measuring it by processes re-engineered. The question to ask the leadership teams is simple: If you had to build this business process from scratch today, would you replicate the legacy version, or would you let AI show you a better one? Those who can answer honestly will lead. The rest will stay stuck in pilot purgatory.

AI integration can be daunting, but the path becomes a lot clearer with a roadmap. Here's a sneak peek at what you'll find in my comprehensive AI Integration Checklist: 1️⃣ 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 ↳ Define your AI goals to tackle key organizational challenges. 2️⃣ 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 ↳ Assess your tech infrastructure and data readiness. 3️⃣ 𝗔𝗜 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 ↳ Decide between nurturing in-house talent or partnering externally. 4️⃣ 𝗧𝗲𝗰𝗵 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻 ↳ Choose AI tools that align with your objectives, starting with pilot projects. 5️⃣ 𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 ↳ Prioritize robust data management for AI success. 6️⃣ 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 ↳ Form a cross-functional team for holistic integration. 7️⃣ 𝗖𝘂𝗹𝘁𝘂𝗿𝗲 𝗼𝗳 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 ↳ Cultivate an environment that embraces AI and continuous learning. 8️⃣ 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 ↳ Lead with responsibility in AI application. 9️⃣ 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 ↳ Measure, iterate, and scale your AI initiatives. Check out the complete checklist and take a significant step towards transforming your organization with AI. #AI #Innovation #AIIntegration #DigitalTransformation

Insights from Technology at Arizona State University — AI Innovation Moves at the Speed of Enterprise Data At Arizona State University we didn’t start from scratch—we built on strong foundations. By integrating our #CreateAI platform with our Enterprise Data Warehouse and Data Lake from day 1, we accelerated generative AI innovation in 2023. Now, we’re taking it even further: 🔥 Seamless AI + Enterprise Integration – Within existing enterprise permissions and security controls - Canvas, ServiceNow, Google Drive, and more—users can instantly leverage enterprise data, no ETL required. AI meets real-world workflows. 🔥 AI Model Fine-Tuning, No Coding Required – Soon anyone can customize AI models using unstructured data—bringing powerful AI capabilities to more people, faster. 🔥 Agentic AI Workflows – AI agents that don’t just respond, but take action when directed. They connect enterprise data, automate workflows, and enable complex decision-making—no coding needed. 2023 was about laying the GenAI groundwork. 2024 was about rapid prototyping, proving what’s possible. 2025 is about enterprise scale—deploying AI where it matters most, at a transformational level. This isn’t just experimentation—it’s a enterprise-wide transformation. Let’s go. 🚀 #EnterpriseAI #GenAI #AIInnovation #HigherEd #DataDriven #CreateAI #AITransformation Elizabeth Reilley Stella Wenxing Liu Roger Kohler Zohair Zaidi Ayat Sweid Paul Alvarado Hailey Stevens-Macfarlane, SMC Varun Shourie Josh Johnson