Enterprise-Ready Generative AI Solutions

Generative AI's multi billion $ problem I've spent the last 3 months meeting CTOs, CIOs and technology leaders at mega enterprises as well as leading tech cos. All of them share one common concern with generative AI and copilot - how do we measure business level outcomes? Many organizations are piloting copilots that promise to make their employees more productive. These copilots help write better emails, create better slides, write blog articles, summarize meeting transcripts. In terms of usage, reality seems divorced from hype. It seems that after an initial burst of engagement, ongoing usage is low at this stage. That is reasonable to expect. However, even when there is ongoing usage in pockets, business leaders are struggling to understand the impact at a business outcomes level. If an employee is writing better emails, how does that tangibly improve business results? This question is fundamental to justifying hefty price tags associated with copilots. So what are forward-thinking CIOs doing? Here are the five steps. 1. Focus on business value - They are focusing their teams on identifying end to end value streams in their business. These range from the lead to cash process, software development life cycles, employee service delivery, customer support delivery, etc. 2. Research value streams - They are organizing their teams to identify work process within these value streams that lends itself to better / efficient output through generative AI solutions 3. Experiment - they are running hundreds of experiments targeting these work processes within these value streams eg test case development, sales email generation, calls summarized to opportunity updates in CRM, gen aI for data analysis. 4. Value and feasibility analysis - These experiments help teams understand the value and difficulty of applying GenAI to end to end value streams. 5. Roadmap development - based on experiments and understanding of end to end value streams, CIOs are developing a roadmap for the future of GenAI in their organizations which will help them deploy these solutions with conviction. In our Moveworks world, we are increasingly hearing CIOs set audacious goals for building a Generative AI service desk. This goal often takes the shape of "zero service desk", or "touch less service delivery". I'm proud that many customers are well on their way to this goal - and I predict that by 2025, most of our customers would have eliminated L1 service desks entirely, and reduced L2 /L3 by 50%. Generative AI has real value at the enterprise level, and individual productivity copilots are merely the obvious (but not so useful) starting point.

CEOs are asking for business outcomes to be achieved with enterprise GenAI. Their teams rush to implement tools that can do more harm than good. Or compliance teams are blocking GenAI roll out. How can we strike the right balance between innovation and compliance? AIMultiple looked into early implementations for clues. The below principles can be used to evaluate GenAI tools before roll-out: ✅ Consistent Enterprise customers need predictability and enterprises deliver that. This sets them apart from immature businesses. ✅ Controlled Building with evolving 3rd party APIs is building on sand. Enterprises need to own at least parts of the tech stack. ✅ Explainable Enterprise users need to know the data that drive decisions. RAG can support this. ✅ Reliable Through human-in-the-loop or guardrails, expensive mistakes need to be avoided. ✅ Secure Depending on the attack surface, securing a model can be a trivial or complex but it needs to be considered. ✅ Ethically trained An LLM built on unethical data is a bomb waiting to explode. Enterprises need to understand the training data. ✅ Fair Bias in training data can impact model effectiveness. ✅ Licensed LLM licensing is complex but important. You don't want to rely on Llama-2 in a product that will have 700M active users next year. ✅ Sustainable Business leaders should be aware of the full cost of generative AI and identify ways to minimize its ecological and financial costs. Sources: More background on the principles: https://lnkd.in/eamiSzj9 LLM API changes: https://lnkd.in/dm7Kg_ig Llama 2 license: https://lnkd.in/dXnzMasv *** Follow me for latest in B2B tech Ring the 🔔 on my profile for notifications #enterpriseai #generativeai #ethicalai

McKinsey & Company: "𝗧𝗵𝗮𝘁'𝘀 𝗛𝗼𝘄 𝗖𝗜𝗢𝘀 𝗮𝗻𝗱 𝗖𝗧𝗢𝘀 𝗖𝗮𝗻 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗳𝗼𝗿 𝗠𝗮𝘅𝗶𝗺𝘂𝗺 𝗜𝗺𝗽𝗮𝗰𝘁" This McKinsey & Co report highlights how #GenAI, when deeply integrated, can revolutionize business operations. I took a stab at CPG eCommerce use case below, and thriving with generative #AI isn’t about just deploying a model; it demands a deep integration into your enterprise stack. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: 𝗠𝘂𝗹𝘁𝗶-𝗹𝗮𝘆𝗲𝗿𝗲𝗱 𝗚𝗲𝗻𝗔𝗜 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗖𝗣𝗚⬇️ 𝟭. 𝗖𝘂𝘁𝗼𝗺𝗲𝗿 𝗟𝗮𝘆𝗲𝗿: → The user logs in, browses personalized product recommendations, and either finalizes a purchase or escalates to a support agent—all seamlessly without grasping the backend processes. This layer prioritizes trust, rapid responses, and tailored suggestions like skincare routines based on user preferences. 📍Business Impact: Boosts customer satisfaction and loyalty, increasing conversion rates by up to 40% through hyper-personalized interactions that drive repeat purchases. 𝟮. 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿 → Oversees user engagement: - Chatbot launches and steers the dialogue, suggesting complementary products - Escalation to a human agent activates if AI can't fully address complex queries, like ingredient allergies 📍Business Impact: Enhances efficiency in consumer support, reducing resolution times and operational costs while minimizing cart abandonment in #eCommerce flows. 𝟯. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗟𝗮𝘆𝗲𝗿: → Performs smart actions using context: - Retrieves user profile data - Validates promotions and inventory - Creates customized options, such as virtual try-ons - Advances the process, like adding to the cart 📍Business Impact: Accelerates innovation in product discovery, lifting marketing productivity by 10-40% and enabling dynamic pricing that optimizes revenue in competitive #FMCG markets. 𝟰. 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗔𝗽𝗽 𝗟𝗮𝘆𝗲𝗿 → Links AI to essential enterprise platforms: - User verification and access management - Promotion rules and order processing - Support agent routing algorithms 📍Business Impact: Streamlines supply chain and sales workflows, cutting technical debt by 20-40% and improving inventory accuracy to reduce stockouts and overstock costs. 𝟱. 𝗗𝗮𝘁𝗮 𝗟𝗮𝘆𝗲𝗿 → Delivers instant contextual details: - Consumer profiles - Purchase records - Promotion guidelines - Support team directories 📍Business Impact: Powers precise AI insights, enhancing demand forecasting and personalization to minimize waste in perishable goods while boosting overall data-driven decision-making. 𝟲. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗟𝗮𝘆𝗲𝗿 → Supports scalability, efficiency, and oversight: - Cloud or hybrid setups - AI model coordination - High-speed response handling - Privacy and compliance controls 📍Business Impact: Ensures robust, secure operations at scale, unlocking value by optimizing resource use, slashing IT ops costs.

With GPT-4o, we've entered the age of interactive intelligence in the consumer space. I think, in the enterprise AI world, we're still dealing with archaic age. Let me set the context : Every enterprise is unique because of its specific processes and data, which define its value. The challenge lies in managing this data and internal processes manually amidst increasing data volumes and evolving business objectives. However, the real challenge always is finding the needle in the haystack. I believe process-oriented AI is the long-term solution—it doesn't need to be AGI. A configurable AI that performs one task perfectly can revolutionize enterprises. Here’s how I think we can make this happen: Connect Enterprise Data: Integrate structured and unstructured data into a massive data lake. Retrieval-Augmented Generation (RAG) is key here for real-time access. Multi-Modal LLMs: Use multi-modal large language models based on specific processes. Purpose-built, fine-tuned models are essential for embedding the right data structures. Multi-Agent Framework: Set up a multi-agent framework to define and deploy business processes within internal systems. Human-in-the-Loop: Ensure human oversight to approve every AI decision. In my view, the key to 100% accuracy in Enterprise AI is not removing humans from the loop but keeping them integral to the decision-making process. This approach will transform enterprise operations, ensuring efficiency and alignment with business objectives. #genai #wexa #agenticautomation #agents

Generative AI on SAP BTP Architecture Most people think SAP BTP is complex. But with Generative AI - it becomes powerful. What you’re looking at is more than just an architecture. It’s a blueprint for building intelligent, scalable, real-world apps on a secure and trusted SAP ecosystem. ✅ At the top: the User Interface layer ➔ Built with SAP UI5 and Web Components ➔ Powered through the SAP Cloud Application Programming Model (CAP) ✅ In the middle: the Generative AI Hub ➔ Uses SAP AI Core for prompt registry, trust, and control ➔ Orchestrates everything from data masking to I/O filtering ✅ At the heart: SAP HANA Cloud ➔ Vector Engine + Knowledge Graph Engine ➔ Harmonized with AI models for contextual insights ✅ The network: SAP and Partner Foundation Models ➔ Built-in, Partner-hosted, SAP-hosted ➔ All secured via HTTPS and SAP Destination Service This is SAP’s vision for enterprise AI Secure. Composable. Explainable. You can move fast without compromising on governance You can build AI into business processes not as an add-on but as a core capability And the best part It’s not just future ready It’s enterprise ready today P.S. Save this if you're building AI on SAP and want a roadmap that actually works Save 💾 ➞ React 👍 ➞ Share ♻️ Follow Alok Kumar for everything related to SAP

To build on my previous short post on OpenAI's ChatGPT Enterprise announcement... A common concern that organizations have when using public Generative AI services and Large Language Models relates to security and privacy. There is a real risk of accidentally sharing confidential information through prompts or allowing services to train their models using proprietary data. I have spoken with over ten Silicon Valley startups that have identified this risk as an opportunity to sell their proprietary solutions for derisking the use of Gen AI and LLMs. These solutions encrypt communication, API use, and storage, and keep private data on private instances of cloud services or on-premises. OpenAI's ChatGPT Enterprise is their response to this risk, offering enterprise-grade security and privacy. It also provides "unlimited higher-speed GPT-4 access, longer context windows for processing longer inputs, advanced data analysis capabilities, customization options, and much more." OpenAI promises not to train its models on business data or conversations, and it is SOC 2 compliant. All conversations are encrypted in transit and at rest. Additionally, a new admin console allows for easy team member management, domain verification, SSO, and usage insights, making it suitable for large-scale deployment in enterprises. In summary, I think this is the first real attempt by a company to convince large organizations to trust a model that is both open and public and secure and private. I anticipate that Microsoft, Google, AWS, and other big tech providers will introduce more secure and scalable options in the near future. Two things will happen as a consequence: 🚀 More organizations will adopt Gen AI and LLM as part of their processes and technology stack ⏰ The window of opportunity for startups that address security and privacy challenges is closing quickly Or, as Abhishek so eloquently put it: WeProtectYourDataStartups.shutDown(); Read more here: https://lnkd.in/gjXcJEMi #generativeai #openai #chatgpt Mark Oost Ron Tolido Pascal Brier Bryan Brochu

Andreessen Horowitz shared their enterprise adoption report for GenAI last week, and here's some key trends they've shared+I've observed as a GenAI consultant. Growth in Generative AI (GenAI) Adoption: Generative AI consumer spend exceeded $1 billion quickly in 2023. Anticipated enterprise revenue from GenAI expected to surpass consumer market in 2024. Initial Enterprise Engagement with GenAI: Mostly limited to a few obvious use cases and "GPT-wrapper" products. Skepticism existed regarding GenAI scaling in enterprises and its profitability. Increasing Enterprise Resource Allocation to GenAI: Significant increase in budgets for GenAI within six months; nearly tripling in some cases. Expansion into a variety of use cases and transitioning workloads into production. GenAI considered a strategic initiative; foundational models being built and deployed. Budget Allocation and Return on Investment (ROI): Average spend on GenAI in 2023 was $7M among surveyed companies. Future spending projected to increase 2x to 5x in 2024. Budget reallocation from one-time innovation funds to recurring software lines. ROI measurement focuses on productivity, customer satisfaction, revenue generation, savings, efficiency, and accuracy. Talent and Implementation Needs: Demand for highly specialized technical talent to scale GenAI solutions. Professional services offered by model providers for custom development are in demand. Trends Towards Multi-Model and Open Source: Enterprises are adopting multiple models to avoid vendor lock-in and stay ahead. A shift from dominance of closed-source models towards open-source adoption is notable. Preference for open-source due to control, customization, and security concerns. Customization and Cloud Influence: Enterprises prefer fine-tuning over building models from scratch. Cloud service providers influence purchasing decisions, with preferences divided by CSP loyalty. Early Features and Model Performance: Early-to-market features and model performance are key factors in adoption. Perception that model performances are converging, especially after fine-tuning. Designing for Flexibility: Applications are being designed for easy model interchangeability to avoid dependency. Building In-House Versus Buying: Enterprises focus on building in-house applications, incorporating APIs from foundational models. Potential shift expected when enterprise-focused AI apps enter the market. Internal Versus External Use Cases: Greater enthusiasm for internal use cases due to concerns about public perception and safety. Cautious approach to deploying genAI in consumer-facing sectors due to risks. Market Opportunity and Future Growth: Model API and fine-tuning market projected to reach $5B run-rate by end of 2024. Increase in genAI deal size and faster closure times indicating rapid market growth. Wider opportunities beyond foundational models, including tooling, model serving, and application building.

How are companies deploying #generativeAI? Most companies are in pilot mode investing in “no regrets” use cases in areas that are accessible without fundamental enhancements to their data and digital core in areas such as IT, marketing, customer service, sales and finance. But Reinventors, representing only 9% of companies, are going further. They’re scaling the technology to power enterprise-wide Reinvention transforming capabilities end to end with a clear 360 value business case. They are deploying #GenAI in no-regret areas while also investing more aggressively in strategic bets across broader segments of the enterprise including supply chain, R&D, engineering, asset management and capital projects where the benefits are significant. These investments offer competitive advantage and will reshape how industries operate. Read our in-depth research report to see how Reinventors are pulling ahead, and how you can leapfrog today's leaders by applying #generative AI across the enterprise. https://lnkd.in/g_YQ3T5m   Oliver Wright Muqsit Ashraf Michael Moore Karen Fang Grant