Key Challenges in Implementing Emerging Tech

Thought provoking and great conversation between Aravind Srinivas (Founder, Perplexity) and Ali Ghodsi (CEO, Databricks) today Perplexity Business Fellowship session sometime back offering deep insights into the practical realities and challenges of AI adoption in enterprises. TL;DR: 1. Reliability is crucial but challenging: Enterprises demand consistent, predictable results. Despite impressive model advancements, ensuring reliable outcomes at scale remains a significant hurdle. 2. Semantic ambiguity in enterprise Data: Ali pointed out that understanding enterprise data—often riddled with ambiguous terms (C meaning calcutta or california etc.)—is a substantial ongoing challenge, necessitating extensive human oversight to resolve. 3. Synthetic data & customized benchmarks: Given limited proprietary data, using synthetic data generation and custom benchmarks to enhance AI reliability is key. Yet, creating these benchmarks accurately remains complex and resource-intensive. 4. Strategic AI limitations: Ali expressed skepticism about AI’s current capability to automate high-level strategic tasks like CEO decision-making due to their complexity and nuanced human judgment required. 5. Incremental productivity, not fundamental transformation: AI significantly enhances productivity in straightforward tasks (HR, sales, finance) but struggles to transform complex, collaborative activities such as aligning product strategies and managing roadmap priorities. 6. Model fatigue and inference-time compute: Despite rapid model improvements, Ali highlighted the phenomenon of "model fatigue," where incremental model updates are becoming less impactful in perception, despite real underlying progress. 7. Human-centric coordination still essential: Even at Databricks, AI hasn’t yet addressed core challenges around human collaboration, politics, and organizational alignment. Human intuition, consensus-building, and negotiation remain central. Overall the key challenges for enterprises as highlighted by Ali are: - Quality and reliability of data - Evals- yardsticks where we can determine the system is working well. We still need best evals. - Extreme high quality data is a challenge (in that domain for that specific use case)- Synthetic data + evals are key. The path forward with AI is filled with potential—but clearly, it's still a journey with many practical challenges to navigate.

The Long Road to IoT Success The promise of the Internet of Things (IoT) is clear – connecting devices and assets to collect data, monitor conditions, and enable efficiencies, cost savings, and new revenue streams. Yet many enterprises struggle to move from IoT proof-of-concept to full deployment. Research shows the average IoT implementation takes 18-24 months to go live. Why does it take so long and how can organizations accelerate IoT initiatives? Based on my experience, I’ve identified some core challenges that lead to lengthy deployment timelines: Data Complexity: Most IoT deployments involve multiple sensors collecting vast amounts of data from disparate systems. Cleansing, normalizing and integrating this data is difficult and time-consuming. Lack of in-house skills and incomplete data management strategies often stall projects. Security: IoT introduces many new cybersecurity risks with so many connected devices and data flows. Building comprehensive protections across hardware, software, network layers takes time. Most organizations underestimate this critical component. Securing both IT and OT environments makes this more complex. Technology Immaturity: The IoT technology stack is complex with components like devices, connectivity, platforms, applications and analytics. There is still fragmentation and lack of standardization. Integrating pieces is challenging without proven blueprints. Organizational Silos: IoT requires collaboration across IT, OT, engineering, operations and business teams. Lack of alignment leads to false starts. Managing expectations and building partnerships across departments is essential. Beyond these technical and operational hurdles, organizational culture misalignment is a major, yet overlooked barrier to IoT success. If engineering, IT and business teams do not share a common vision and work collaboratively, IoT initiatives stall. A culture focused on siloed metrics and legacy processes rather than cross-functional problem solving will severely hamper any digital transformation effort. Most importantly, organizations need to ensure they are pursuing IoT not just for the sake of adopting a shiny new technology, but because they have defined a clear business model and path to value. Without a well-considered ROI and business justification, IoT deployments meander without real impact. A strong business sponsor must guide the initiative and keep it focused on measurable outcomes that enhance the organization’s objectives. To accelerate IoT deployments, leading organizations focus first on high-value use cases, centralized data platforms and developing talent. But they also cultivate partnerships between teams to align on goals. With the right strategies, business alignment and culture, IoT can start delivering ROI more quickly. But it takes realistic planning, collaboration and skill building to smooth the long road - especially in complex legacy environments.

Today's update on the challenges facing #GenAI adoption and enterprise #scaling revisits the brewing crisis and further raises the issue of skills shortage, vendor lock-in, and the elusive #ROI of adopting AI. In my research, I identified a lack of business value as a key impediment and the new skills around #CognitiveCollaboration and "Probabilistic Problem Solving" as the critical skills in the AI age. But the main stories first: --- AI Enterprise Scaling: The ROI Crisis Deepens This week's updates show enterprise AI struggling with systemic issues beyond technology, creating a crisis of confidence and hindering measurable ROI. Update 1: The Visibility Gap Crisis Challenge: Massive underestimation of actual AI usage and costs. Details: According to new research, IT leaders underestimate application usage by 1,600%. This creates a massive blind spot for AI tool engagement, costing companies millions in inefficiencies and making true ROI measurement nearly impossible. Source: https://lnkd.in/gaPqywbP Update 2: The Skills Crisis Reaches a Breaking Point Challenge: Critical talent shortages are derailing transformation timelines. Details: A staggering 87% of organizations now report skills gaps, a problem projected to cost the global economy $5.5 trillion. Despite high demand for AI/ML roles, only 28% of companies have achieved adequate data literacy, stalling deployments. Source: https://lnkd.in/g2GYGngG Update 3: The Vendor Lock-in Epidemic Challenge: Vendor lock-in is actively destroying AI value. Details: AI project abandonment rates have soared to 42%. This is linked to vendor-led pilots that create dependency without proving value, trapping companies in solutions that fail to integrate with core business workflows or deliver on ROI promises. Source: https://lnkd.in/ge85hWT6 Key Takeaway The AI scaling crisis is now more organizational than technical. Success hinges on clear ROI visibility, proactive workforce development, and vendor-independent strategies that prioritize proven business value over technology.  --- Those interested in exploring the Theory of #CognitiveChasms are welcome to check out my work at https://lnkd.in/gjsb4H-7. Cognitive Chasm

The rush to implement AI solutions can lead to significant pitfalls. Here's a provocative thought: the greatest risk in AI isn't just inaction. It's implementing without understanding. Let’s unravel why AI implementation demands careful thought and expertise. The promise of AI is undeniable. But when businesses leap without looking, the consequences can be dire. → Mismanaged data leads to flawed predictions. ↳ Garbage in, garbage out—AI doesn't magically fix bad data. → Overreliance can breed complacency. ↳ AI is a tool, not a crutch. → Lack of understanding can result in ethical oversights. ↳ Algorithms must be checked for bias and fairness. → Insufficient expertise can stall projects. ↳ Proper training and a clear strategy are essential. AI implementation isn't just about tech. It's about aligning with business goals and ethics. So, how do we get it right? Prioritize data quality → Clean, accurate data is nonnegotiable. Invest in education → Equip your team with the knowledge to leverage AI effectively. Engage multidisciplinary teams → Combine tech expertise with business acumen. Embed ethical considerations → Regularly audit models for bias and fairness. Iterate and refine → Continuous learning and adaptation are key. Remember, AI isn't a onesizefitsall solution. It's a journey that requires thoughtful planning and execution. Done right, AI can transform businesses, enabling them to act with foresight and agility. Yet, it's the careful, calculated steps that ensure this transformation is both successful and sustainable. What steps have you taken to ensure AI success in your organization? Share your thoughts below.

Generative AI’s Dirty Secret... 🤫 ....the Challenges That Hold Enterprises Back What’s really holding them back from achieving the transformative results they’ve been promised? The answer lies not in the technology itself, but in the hidden challenges that companies face when trying to implement it at scale. The Challenges of Generative AI While the potential is huge, there are quite a few obstacles standing in the way of widespread adoption. 📊 What are businesses struggling with? 1️⃣ Messy Data (46%): AI needs clean, reliable data to perform well. If the data isn’t right, the results won’t be either. 2️⃣ Finding the Right Use Cases (46%): Businesses often don’t know where AI can make the biggest impact. 3️⃣ Trust and Responsibility (43%): Companies need strong guidelines to make sure AI is used ethically and doesn’t cause harm. 4️⃣ Data Privacy Concerns (42%): Keeping sensitive information secure while using AI is a constant worry. 5️⃣ Lack of Skills (30%+): Many teams don’t have the expertise needed to develop and manage AI systems effectively. 6️⃣ Data Literacy (25%+): Employees often don’t know how to interpret or work with the data AI relies on. 7️⃣ Resistance to Change (25%): Adopting AI means rethinking workflows, and not everyone is on board with that. 8️⃣ Outdated Systems (20%): Legacy technology can’t keep up with the demands of advanced AI tools. How to Overcome These Challenges Generative AI works best when companies have the right foundation: clean data, modern systems, and a team ready to embrace the change. Here’s how businesses can tackle the challenges: 1️⃣ Improve Data Quality: Make sure your data is accurate, clean, and well-organized. AI thrives on good data. 2️⃣ Find Real Use Cases: Talk to teams across your company to figure out where AI can save time or create value. 3️⃣ Build Trust with Responsible AI: Set up rules and guidelines to ensure AI is used fairly and transparently. 4️⃣ Upskill Your Team: Invest in training programs so your team can learn how to build and manage AI systems. 5️⃣ Upgrade Technology: Move to modern, scalable systems that can handle the demands of generative AI. Why This Matters Generative AI isn’t just a fancy new tool—it’s a way for businesses to work smarter, solve problems faster, and drive innovation. 🔑 What you can gain: Better Accuracy: Clean data leads to better AI results. Scalability: Modern systems make it easier to grow and take on bigger AI projects. Faster Results: Streamlined processes mean you can see the value of AI sooner. 💡 What’s next? AI will become a part of everyday workflows, helping teams make decisions faster. Cloud-based AI tools will give businesses more flexibility to innovate. Companies will put a bigger focus on ethical AI practices to build trust with customers and stakeholders. The real question isn’t whether businesses will adopt generative AI—it’s how quickly they’ll embrace it to stay ahead of the curve. ♻️ Share 👍 React 💭 Comment