How to Align Generative AI with Business Objectives

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.

Over the past few months, I've had the privilege of speaking at various conferences and engaging in deep conversations with customers, colleagues and partners about leveraging generative AI in business. It's clear that we're on the cusp of a transformative era, but amidst this rapid evolution, a critical focus emerges: the need for strategic prioritization and road mapping. 🔍 Key Insights: Augment Employee Productivity: There is so much noise in the market on what Gen AI can do. Some of it is real and some of it is aspirational. The foremost step for businesses is to identify and prioritize use cases where AI can realistically and significantly boost productivity. It’s not just about adopting technology; it's about integrating it in a way that replaces manual overhead in existing workflows and augments workforce productivity. Business-Specific Embedded AI: When it comes to generative AI in business, success lies in tailoring it to address unique business challenges. Embedding AI into enterprise products and services (at the business process level) can unlock unprecedented value, driving not just efficiency but also innovation. Advanced Industry specific use cases: Beyond the basics, there is a growing need for providers of technology to create domain/industry specific models with built in enterprise intelligence and benchmarking. 💡 The Bottom Line: As we navigate this exciting landscape, the key for leaders is not just to invest in AI but to invest wisely. It's about identifying where AI aligns with your business goals and how to build a roadmap to addresses short term and long terms goals. It is also important to understand your vendors AI ethics policy along with their data privacy and protection policy to understand if your vendor’s AI is responsible, reliable and relevant. 🤝 I would love to hear from you on how you are integrating generative AI into your business strategy? Reach out to discuss more about the future we're building together! #GenerativeAI #BusinessTransformation #AIstrategy #Innovation

In a compelling conversation with Ben Wodecki on the AI Business podcast, I shared insights on AI strategy, the transformative potential of open-source models, and what lies ahead for AI. This discussion extends beyond the realm of technology, emphasizing the strategic alignment of AI with core business objectives to foster growth and innovation. 🌐 Reflecting on my journey from IBM to shaping Fortune 500 strategies with data and AI, we explore the critical question not of "How do I use AI?" but "What are my strategic business objectives, and how can AI help achieve them?" Through examples, we illustrate how leveraging unique data assets can drive business objectives, emphasizing the strategic use of technology to bolster competitive advantages. 🔍 Additionally, we delve into the significance of benchmarks for generative AI models, highlighting the Hugging Face benchmark for model performance and the 🔭 Galileo benchmark's 'hallucination index.' These tools are pivotal for businesses in selecting AI models that align with their technical needs and adhere to governance and ethical standards, ensuring a balanced and effective AI strategy. 🛠️ The podcast covers the nuances of aligning AI initiatives with business goals, the evolving role of C-suite engagement in leveraging AI, and the importance of AI governance and education across all organizational levels. 🌍 Amid this technological discourse, we touch upon the urgent need for inclusivity in AI development to combat technological colonialism, ensuring equitable advancements reflect diverse global perspectives. 📈 Looking ahead, we anticipate a shift towards more specialized, efficient, and multimodal generative AI models in 2024. This evolution promises not only to enhance performance but also to reduce the environmental impact of AI technologies. This episode is not merely a conversation; it's a comprehensive guide for leaders eager to navigate the AI landscape with strategic foresight and ethical consideration. Tune in to gain invaluable insights that could redefine your organization's trajectory in the AI-driven era. 👉 Dive into the full episode for a deep dive into AI strategy, benchmarks, and the future of AI #AI #Strategy #OpenSource #GenerativeAI #Leadership #Innovation #Benchmarks #EthicalAI #FutureOfTech