Custom AI Solutions for Business Needs

Want to roll out an AI offering? Don't start too broad. Choose one differentiated use case. That means: → A specific problem → For a specific type of client → That your team can solve with AI → And that has real business value For example: “We help enterprise companies implement agentic AI solutions to solve complex, multi-step problems.” “We provide synthetic data generation for LLM training purposes.” “We help knowledge management teams eliminate bad data in documents before they become bad GenAI answers.” Here’s how we approach this at @Wiseboard. We help IT outsourcing companies narrow in by: → Analyzing their delivery strengths  (Projects they’ve already done, industries they know, data and workflows they’ve already worked with) → Exploring 100+ AI use cases (We bring validated customer needs and connect them to what our clients can actually deliver) → Scoring opportunities (We rank use cases based on market demand, competition, expected ROI, and dozens of other criteria) By the end, the company has several prioritized and validated use cases and a USP for every use case. Here is an example of such a use case: ✔️ A segment → Contact centers ✔️ A use case → Contact center knowledge automation ✔️ A pain point → Bad answers harm brand's reputation ✔️USP → For contact centers struggling with inaccurate customer responses, we deliver AI-based knowledge automation solutions that ensure fast, accurate, and brand-safe answers. In the next post, I’ll show how one focused use case turned into a productized AI solution. P.S. Hit follow (if you haven’t yet) and that 🔔 to catch the rest of this series. #aipracticerollout #wiseboard

MIT Technology Review Insights just dropped a powerful reality check: “Customizing Generative AI for Unique Value” (in partnership with Microsoft Azure) explores how enterprises are moving beyond out-of-the-box models to unlock competitive advantage through tailored AI. The mission? To understand how global tech leaders are customizing generative AI—and what it takes to do it right. The data is clear: → 67% of enterprises are using or exploring RAG → 54% are fine-tuning models → 46% are investing in prompt engineering Why? Because foundational models fall short for enterprise needs. They’re powerful—but generic. Customization is the new frontier of value. → 50% of tech leaders prioritize efficiency → 49% seek market differentiation → 47% aim for better user satisfaction → 42% cite innovation and creativity But it’s not without challenges: → 52% cite data privacy/security as their top concern → 49% struggle with data quality and prep → 45% can’t yet measure customization impact effectively What’s emerging instead? A smarter approach to AI development: → AT&T uses agentic systems to automate full software lifecycles → Dentsu achieves 95% accuracy in campaign planning with a customized RAG framework → Harvey AI builds legal-specific models that support real-world legal workflows And enterprises are moving fast: → 76% still need help identifying business use cases → 53% are enabling devs with telemetry and debugging tools → Multi-agent systems are being developed to simulate scenarios and generate synthetic data Bottom line: Generative AI is only as powerful as the context it’s given. Customization unlocks that context—transforming productivity, accuracy, and innovation. This isn’t just AI adoption. It’s the rise of AI transformation. Are you customizing yet?

I've had over 500 AI agency sales calls and here's what businesses actually want. (Spoiler: It's not simple chatbots or voice agents although they do sell) While everyone's building weekend ChatGPT wrappers, businesses are quietly paying $15,000+ for completely different AI solutions. After generating six figures in AI service revenue, I've discovered exactly what companies are willing to pay premium prices for. The reality check: A $2M ARR SaaS company told me they'd rather pay $20,000 for a solution that increases revenue by $50,000 monthly than pay $2,000 for a chatbot that saves 5 hours per week. (who would've thought.. 😂) That conversation changed everything about how I approach AI services. What businesses actually pay premium prices for: Sales Automation Systems - Intelligent prospect identification across multiple data sources - Automated research and enrichment for each lead - Multi-channel outreach orchestration (email, LinkedIn, phone) - Dynamic nurturing sequences that adapt to prospect behavior - Lead scoring that prioritizes highest-value opportunities Content Creation Engines - Automated market research and competitor analysis - Multi-format content generation across all platforms - Advanced SEO optimization and ranking strategies - Brand voice consistency across all channels - Performance tracking and optimization Operational Workflow Solutions - Complete client onboarding automation - Document processing and compliance monitoring - Intelligent customer support with escalation protocols - Quality control and audit trail systems - Project management and resource optimization Data Processing & Analytics - Multi-system data integration and business intelligence - Predictive modeling for forecasting and optimization - Real-time performance optimization - Competitive intelligence gathering - Custom executive dashboards The industries reaching out most: - Professional services (agencies, consulting, law, accounting) - E-commerce and retail ($500K-$10M annual revenue) - Manufacturing and distribution - Healthcare and compliance-heavy businesses Why these command premium pricing: They solve expensive problems that directly impact revenue, provide strategic advantages competitors can't replicate, and generate measurable ROI that far exceeds investment. Stop building tools and start solving business problems. When you can demonstrate $200K in additional revenue or $150K in cost savings, charging $25K becomes an easy decision. 👉 Want the complete breakdown of high-value AI solutions? 1. Connect with me 2. Comment "SOLUTIONS" I'll send you the detailed analysis. (Must be connected - prioritizing reposts first!)

The recent news about a pharma company stopping their use of Microsoft's Copilot AI tool due to high costs and low value has started a crucial discussion: Are generic AI tools just not good enough anymore? At SJ Innovation, we believe the future lies in custom AI solutions made for specific business needs. Generic AI tools often come with a high price and limited customization, which can slow down innovation and efficiency. >> Here's why building your own agent might be the smarter move: > Cost Efficiency: Custom-built solutions remove unnecessary features, focusing only on what your business truly needs. > Enhanced Control: Gain full control over your AI's development and integration, ensuring it grows with your business. > Better ROI: With a platform designed specifically for your operations, the return on investment can be much higher. > Scalability: As your business grows, your AI solution can grow with it. > Security: Custom AI solutions offer enhanced security features tailored to your specific needs. >> Leveraging AI Wrappers and APIs: One effective way to build a custom AI platform is by using AI wrappers to create solutions based on APIs from leading providers such as OpenAI, Gemini, and Claude. >> Investing in Custom Agents: Companies should consider creating custom agents designed to handle specific tasks and workflows, integrating seamlessly with existing systems. >> Introducing CollabAI by SJ Innovation: We've developed and released CollabAI, an open-source AI wrapper designed to help businesses create custom AI solutions easily. Download it at [collabai.software](https://collabai.software/). It's time to rethink our reliance on generic tools and invest in solutions that genuinely add value to our unique business environments. Learn more and improve your AI strategy with SJ Innovation and CollabAI. #AI #CustomAI #BusinessInnovation #FutureTech #ProductivityBoost #AIWrappers #CustomAgents #OpenSource"

LLMs and Agents alone cannot solve real-world problems that exist in enterprises.   The right mix of data understanding, operations expertise, choice of the right AI model, last-mile custom engineering, and operational change is critical to drive the proper outcomes. But let me focus on the last-mile custom engineering today.   For example, a global logistics company manually processes large volumes (20-50k daily) of unstructured and complex documents as part of the billing process. This resulted in escalating costs, delays in the process, and little insights about things billing inaccuracies and leakage.   Deploying an off-the-shelf LLM-powered intelligent document processing solution helped, but not completely.    So EXL reengineered the solution leveraging the NVIDIA stack across multiple dimensions: ✅ 70% cost reduction – Migrated from a closed-source model, which relied on OCR + 2 small LLMs (fragmented & unscalable) to a simplified fine-tuned multi-modal model. Leveraged Nemo Curator & Customizer to do this conversion within 1 week and with a nimble training effort ✅ 50% lower latency – Optimized deployment on NVIDIA NIMS to improve user experience ✅ 5% accuracy uplift – Smarter processing and more accurate output for humans in the loop   ROI from enterprise AI needs more than just models or tooling – It needs practical engineering. Gaurav Iyer, Piyush Aggarwal, Somya Rai, Wyatt Bennett, Joseph Richart, Vivek Vinod, Arturo Devesa #AI #LLM #EnterpriseAI #NVIDIA #GenAI #Data #XtraktoAI

Would You Let a New Intern Run Your Most Important Client Meetings on Day One? That might sound ridiculous. But it’s exactly what many businesses are doing with AI today. I spoke with Elena Agaragimova on her podcast about something I’m passionate about: how to really bring AI into your business the right way. AI is not just another software subscription you bolt onto your tech stack. It’s not a one-size-fits-all app that magically fixes everything. You need to treat AI as a technological entity that needs to learn you. Your values. Your processes. Your special sauce. If you want real results, here’s how you start: ✅ 1. Eliminate Redundancy First, clean up your processes. AI loves clarity. Get rid of duplication and inefficiency. Simplify. Because if you feed AI a mess, you get faster, more expensive messes. ✅ 2. Identify Opportunities Next, look for the best places to apply AI. Where can it save time? Reduce cost? Improve customer experience? Think like a strategist. Don’t just automate for the sake of it. ✅ 3. Build Your Custom, Secured Knowledge Base This is the big one. Stop giving away your data to every off-the-shelf tool. Build your own secured AI knowledge base that knows you intimately. Train it on your products, your customers, your voice. When you do this well, you don’t just automate. You differentiate. We’ve seen companies who did this 3X their valuation. Why? Because they built an AI system that is as unique as their brand. It became a real competitive advantage. You wouldn’t let a new hire run your top client meetings on day one. So why let a generic AI tool with no context speak for you? Train it. Teach it. Build it to know you. If your AI understands you deeply, you’ll surpass 80% of your competitors who are stuck using generic prompts and templates. AI done right isn’t about the tool. It’s about the strategy. It’s about knowing yourself and teaching your AI to know you. What’s the first place in your business you’d want AI to really know you?

I have a dear friend who is the CIO of a PE-backed firm. She shared that she's "drowning in AI salespeople" and needs to know how to vet their solutions. Her words echo the challenge that I hear from many executives and board directors. 🗨 One recently said to me, "I'm so sick of AI. I can't tell what's real and what's hype. The risk is high if I do nothing. And if I go too fast or make bad choices, the risk is even higher. I've got to figure this out." I hear you. Your concerns and frustration are warranted. To help you, I hammered out 3 guides - business value, risk, and technical - that include questions to help you to identify AI solutions that are best fit for YOUR organization. These guides are designed to help you create business value with AI, avoid risks, and sustainably deploy and scale your AI solutions. 📊 Business Value Questions: This guide includes 24 questions designed to ensure that the AI solutions align with your strategic objectives and deliver tangible business outcomes. 🔍 Risk-Based Questions: This guide covers 33 questions focused on identifying and assessing potential risks associated with AI solutions, helping you to make informed decisions that mitigate risks. 🔧 Technical Questions: This guide contains 48 technical-based questions to ensure the AI solutions under evaluation have the technical robustness necessary to support your business objectives. 👉 Click below, share your email address, and you'll receive an email with links to all 3 documents. #AI #AIEvaluation #BusinessValue #RiskManagement #Innovation Disclaimer: While these questions provide a solid foundation for evaluating AI solutions, it's not possible to cover every possible needed question in a concise format. As always, I encourage you to apply your own expertise and judgment. https://lnkd.in/ghG4RdP4

This is a gem of a case study about how to apply AI across a business. Singapore Airlines is partnering with OpenAI to apply AI to its business in the following ways, reports A'bidah Zaid Shirbeeni in MARKETING-INTERACTIVE: 1. Personalize the airline’s virtual assistant to intuitively plan personalized travel and offer customers self-service options. Business Benefits:  ✅ Self-service delivers higher revenue impact than the flight recommendation chatbot ✅ Intuition (read: ChatGPT’s new memory) and personalization promote customer engagement 2. Create an internal AI assistant to guide employees on operations and automate routine tasks. Business Benefits:  ✅ Faster decision-making when time is critical ✅ The assistant applies learnings from past issue resolutions and support solves to answer current questions 3. Integrate ChatGPT with operations tools to crunch out complex workflows such as scheduling flight crews while referencing applicable regulatory guidelines. Business Benefits:  ✅ Optimizes planning ✅ Streamlines operations WHY THIS MATTERS: Singapore Airlines’ idea of an “AI-first customer journey” shifts the lens from thinking about AI-first companies toward using LLMs to build better customer experiences. That’s a powerful shift. This is applied AI at its finest - to build better customer experiences. What ideas spring to mind when you think about AI-first customer experiences at your company? ✨ Conversational AI imperatives from Chatbot Europe: https://lnkd.in/edxvM8d3 #ai #cx #ux #chatbot #appliedai #marketing Image credit: MARKETING-INTERACTIVE

Microsoft's latest #AI move is redefining how we think about industry-specific transformation. Rather than relying on generalized AI, Microsoft is diving deep into adapted AI models—precision-tuned for specific sectors like agriculture, healthcare, manufacturing, and finance. (link in the comments) Here’s why this shift matters: 𝟭. 𝗖𝘂𝘀𝘁𝗼𝗺𝗶𝘇𝗲𝗱 𝗔𝗜, 𝗥𝗲𝗮𝗱𝘆 𝗳𝗼𝗿 𝗜𝗺𝗽𝗮𝗰𝘁 Microsoft’s models, like Bayer’s E.L.Y. for sustainable farming and Cerence’s in-car CaLLMTM Edge, aren’t just tools; they’re crafted solutions—purpose-built to tackle the unique challenges of each industry. 𝟮. 𝗣𝘂𝗿𝗽𝗼𝘀𝗲-𝗕𝘂𝗶𝗹𝘁 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 In Copilot Studio, AI agents such as the Factory Operations Agent aren’t just automating tasks; they’re redefining roles, transforming AI from a “nice-to-have” into a strategic co-worker that helps tackle industry pain points with precision. 𝟯. 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗮𝗻𝗱 𝗦𝗲𝗰𝘂𝗿𝗲 𝗔𝗜 With its strong emphasis on trust and privacy, Microsoft is showing that transformative AI doesn’t have to compromise on ethics. This focus is setting a new standard for responsible AI in industries where security is critical. 𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲: Microsoft isn’t just providing AI—they’re bringing sector-specific expertise into the AI landscape, helping industries adapt faster and smarter. This isn’t AI for AI’s sake—it’s a strategic reimagining of AI to meet the real-world needs of modern businesses. 𝗔𝘁 Plutoshift AI 𝘄𝗲 𝘀𝗵𝗮𝗿𝗲 𝘁𝗵𝗶𝘀 𝘃𝗶𝘀𝗶𝗼𝗻 𝗼𝗳 𝘁𝗮𝗶𝗹𝗼𝗿𝗲𝗱, 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝘆-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗔𝗜. Just as Microsoft’s adapted AI models are transforming agriculture and healthcare, we’re bringing precision AI to industrial water management to drive smarter, more sustainable operations. 𝐓𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐀𝐈 𝐥𝐢𝐞𝐬 𝐢𝐧 𝐩𝐮𝐫𝐩𝐨𝐬𝐞-𝐛𝐮𝐢𝐥𝐭 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 𝐭𝐡𝐚𝐭 𝐭𝐫𝐮𝐥𝐲 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐞𝐚𝐜𝐡 𝐢𝐧𝐝𝐮𝐬𝐭𝐫𝐲’𝐬 𝐮𝐧𝐢𝐪𝐮𝐞 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬. #AI #IndustryTransformation #SustainableOperations