Bill Schmarzo

In my discussions with our clients, I often get questions around the complex vendor landscape for AI and AI Agents and how to make sense of it as well as where to invest for building an enterprise AI and Agentic capability. One thread that often gets attention is how organizations should look at RPA and Agentic capabilities in this landscape. We wrote a detailed paper on this topic discussing how these are interrelated and how organizations should think about leveraging both. For more details, please check out our paper - https://deloi.tt/4oty9rl This landscape continues to evolve and we will continue to bring our insights based on what we are seeing and deploying. AJ M. Patricia Henderson Camille Chicklis #AI #Agentic #Automation

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Special Breaking Analysis: IBM’s Technology-Arbitrage Play—Turning Agentic AI & Durable Platforms into Enterprise Outcomes We believe IBM is executing one of the more underappreciated pivots in enterprise AI: shifting from labor arbitrage to technology arbitrage—packaging agents, durable platforms, and domain IP into repeatable workflow outcomes for regulated, complex environments. Why it matters IBM is marching toward ~50% software mix bringing margin tailwinds, while consulting is being rewired for “human + digital labor.” IBM’s “Client Zero” program mapped 490 workflows, instrumented ~70 with digital workers, and claims $3.5B in productivity gains - one proof point that outcomes, not GPUs or models, are what enterprises value. The hybrid stack (Red Hat + OpenShift AI + open-weight models) keeps latency low and inference close to data, policy, and governance - important for regulated enterprises. Partnerships (Anthropic for lifecycle governance; Groq for ultra-low-latency inference) strengthen agent reliability and cost/perf in production. Would like to see stronger ecosystem momentum. What’s working IBM claims it's seeing Agentic tractioin in customer care, back-office, and IT/SDLC and shows measurable cycle-time compression and operating leverage. Durable platforms (Z, Power, GPFS/Fusion) are being reframed as AI infrastructure to modernize, not rip-and-replace. What’s missing In our view, IBM still needs a clearer platform unifier across data, systems, and software - e.g. a WebSphere-for-AI-era - plus a stronger semantic layer to harmonize silos and feed agents with governed, actionable context. Investor angle If IBM can consistently move clients from weeks → days with packaged agent blueprints - and price on value/outcomes - our view is its momentum continues and is undervalued. We assign ~70% probability to this technology-arbitrage thesis for IBM to deliver above-market growth over the next 12–24 months. This research note based on day long analyst AMAs with Arvind Krishna Rob Thomas Ritika Gunnar Matt Hicks Mohamad Ali Ric Lewis Josephine Schweiloch Jay Gambetta & perspectives from several analysts including Matt Eastwood Crawford Del Prete Anurag Agrawal Anthony J. Bradley Rick Villars - Thanks Elizabeth Bamonte for hosting... #AI #AgenticAI #HybridCloud #EnterpriseSoftware #RedHat #Watsonx #Quantum #TechArbitrage Full post here: https://lnkd.in/ebv3VDv3

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At its Investor Day this week, IBM offered some surprising new insights into its Granite foundation models - a key element of its AI strategy - showing cost & performance advantages over larger models like GPT-4 Turbo and Llama 70B: → 98% cheaper to run than GPT-4 Turbo in a financial planning use case. → 75% cheaper than Llama 70B in an accounting use case. → Outperformed larger models in key RAG scenarios. → Performance comes from training smaller models with domain-specific knowledge. → Granite models are trained on Red Hat OpenShift AI IBM's Granite models are proving that specialized, smaller AI models can outperform large-scale LLMs in enterprise settings. Beyond Granite, IBM laid out an AI strategy that brings together watson.x, Granite & Red Hat to address enterprise concerns around cost, complexity, and expertise with a cost-efficient open-source approach to hybrid AI solutions We have a deep piece on this in the works - stay tuned! https://lnkd.in/ek4MDja7

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Small Language Models (SLMs) are gaining traction as efficient and accessible alternatives to larger counterparts. They tackle challenges posed by Large Language Models (LLMs) by prioritizing resource efficiency, environmental sustainability, and task-specific capabilities. Learn more about SLMs in this insightful article by MarkTechPost: [link to article] #LanguageModels #TechTrends

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#AI #Data & #Digital #Profession: An interview podcast series with leading industry experts for advancing the AI, Data & Digital Profession, and #knowledge #sharing for #public #good. Business Architecture & Operating Model: In an recent interview (episode #4), I sat down with one of the leading architect and innovator, Dr. Terry Roach and deep dived into the topic of Business Architecture & Operating Model, which is a strategic foundation for Enterprise Scale AI Systems architecture, solutions and beyond. -> Interview Video: https://lnkd.in/gYv62sRJ Terry has extensive experience as a practitioner and leader over 30+ years, and shared critical insights around the following points for graduates and professionals. 1. Business architecture elements 2. Operating model elements 3. Business architecture and operating model integration 4. AI powered business architecture 5. AI powered operating model 6. Modernising enterprise architecture teaching & skills for upcoming professionals Sharing an important quote from Terry Roach's interview explaining the pragmatic connection between the business architecture and operating model: "The operating model will generally exist. It's there. You already have systems, you already have processes, you already have people doing stuff. What you haven't probably got is a really good structure for explaining what they all do. What the business architecture does is it gives you the format and the structure to organise the operating model knowledge into that structure". Thank you so much Terry Roach, I am grateful to you for our long standing applied research and innovation partnership over the period of last 10 years. I look forward to continue our collaboration and knowledge sharing at the School of Computer Science (Software Engineering), UTS Faculty of Engineering and IT. #AI #EnterpriseArchitecture #BusinessArchitecture #OperatingModel

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LLMs provide the reasoning and language understanding layer, helping AI agents interpret natural language queries, analyze unstructured text, and generate relevant responses. Learn how to build AI Agents for the Enterprise. https://lnkd.in/gkACi8h5

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By 2030, AI-enthusiastic consumers will have a profound impact on the global economy – commanding over $1.2 trillion of AI-influenced spending in the UK and Germany alone.    These insights are results of a new study by Cognizant and Oxford Economics that predicts consumer who embrace AI could drive large percentages of European transactions over the next five years: 39% in the UK, and 46% in Germany.    Our research identified that AI will reimagine the consumer journey across its three pivotal phases – Learn, Buy, and Use – with each presenting distinct challenges and opportunities.    AI-infused purchasing will be shaped by three pivotal consumer groups that represent the highest and lowest levels of AI intensity: Accelerators, Early Adopters, and Anchors. Each showcase unique attitudes that companies will need to accommodate for as consumer mentalities on AI shift.    These developments will carry significant business implications over the coming five years.   Dive into our full "New Minds, New Markets" research here: https://lnkd.in/dYYCCu9J

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A recent IBM study surveyed 2,000+ CEOs soliciting feedback on their AI investments - highlighting the focus on ROI, enterprise use cases, challenges and obstacles, and balancing I&O needs - and why even w/ these initial hurdles, they are still very bullish on AI's postive impact to their organization. Some Key Insights: 1. ROI of AI Initiatives: Only 25% of AI initiatives have delivered the expected ROI over the last few years, and a mere 16% have scaled enterprise-wide. 2. Focus on ROI: To accelerate progress, 65% of organizations are leaning into AI use cases based on ROI. Additionally, 68% report having clear metrics to measure innovation ROI effectively. 3. Value Beyond Cost Reduction: 52% of organizations are realizing value from generative AI investments beyond mere cost reduction. 4. Investment Risks: 64% acknowledge that the risk of falling behind has driven investment in some technologies before fully understanding their value. However, only 37% believe it is better to be "fast and wrong" than "right and slow" regarding technology adoption. 5. Funding Challenges: 59% admit their organization struggles to balance funding for existing operations with investment in innovation during unexpected changes. Furthermore, 67% state that more budget flexibility is needed to capitalize on digital opportunities that drive long-term growth and innovation. 6. Future Expectations: By 2027, 85% expect their investments in scaled AI efficiency and cost savings to yield a positive ROI, while 77% anticipate a positive return from investments in scaled AI growth and expansion. Whether you're in early stages of your AI journey or looking for answers to accelerate to goal, working with our partners like IBM, Presidio can help. Reach Out! https://lnkd.in/eXZw4Ufr

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In the rapidly evolving AI landscape, executives must understand not only what AI is but also how to harness it for strategic advantage while mitigating associated risks. This LinkedIn LIVE event is a one-hour primer to introduce a half day course, "AI Literacy for Executives" being offered by Donald Farmer and John Santaferraro.

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Our new @EY US AI Pulse Survey highlights a strategic shift towards building custom #AI solutions in-house, with 64% of senior leaders we polled opting for this approach. This reflects a maturation of the AI market, emphasizing the need for organizations to develop unique capabilities that drive differentiation. It also emphasizes the importance of strong #data infrastructure to continue to innovate and realize the highest ROI. Learn more from our findings: https://lnkd.in/euENJ88C Dan Diasio Hugh Burgin Zakir Hussain Sinclair Schuller Jimmy H Marquis III

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How do business leaders justify an investment in technology and data? This question has plagued most strategists and business leaders for decades. The reality today is that technology cannot be an afterthought - being relevant in a digitally native world requires us to realize that technology is intrinsically woven into our business strategies and our go to markets. I had the privilege of sitting down with Bronwyn Williams, one of our leading futurists and trend analysts and Dr. Bongani Andy Mabaso in Altron's Local Logic podcast series to unpack "Breaking down the data silos" and we covered all things big data, AI and driving value from technology investments. Sign-up to receive a link to the podcast once it goes live at https://lnkd.in/dMYWxDPq #bigdata #ai #financialinclusion #business #strategy

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As enterprise AI moves deeper into core systems like ERP, the gap is widening between those scaling with intent and those layering AI on top of old processes. There's an opportunity now to redesign workflows, modernize data models, and reset decision rights so AI isn't an add-on but an operational core. That requires cross‑functional governance, an employee adoption strategy as strong as the tech roadmap, and a clear line of sight to value creation. In this next phase, having AI is no longer enough. The critical differentiator will be whether your enterprise model is built to compete in an AI‑driven economy. A worthwhile read: https://lnkd.in/dUgg8WU9 #GenAI #ERP #DigitalTransformation

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We spoke about AI agents during my recent CXOTalk conversation with Praveen Akkiraju from Insight Partners. Although enterprise leaders are increasingly pressured to integrate AI agents, three use cases stand out for their capacity to deliver measurable business value: 1. Developer productivity takes the lead. Tools like Cursor and Devin are transforming coding workflows by automating repetitive tasks and optimizing debugging processes. Development teams report remarkable time savings on routine coding activities, which allows engineers to concentrate on intricate architectural decisions and innovation. 2. Customer experience is the second significant advancement. Sophisticated AI agents convert traditional chatbot exchanges into seamless conversations that can manage complex customer inquiries across various channels, offering consistent support 24/7. Organizations experience considerable reductions in support ticket volumes while sustaining high customer satisfaction ratings. 3. Operations management represents the third essential application. AI agents excel at sifting through numerous alerts, distinguishing real threats from routine notifications. These systems significantly reduce incident response times by efficiently flagging critical issues and recommending policy-compliant solutions. Security and IT teams can resolve issues within minutes rather than hours. Strategic implementation is key to success. Begin with a single, high-volume process characterized by evident bottlenecks. Conduct a focused 30-day pilot to evaluate time savings and operational enhancements. Then, scale effective solutions across comparable workflows. As always, document results to garner internal support and develop baselines for comparison. The most successful organizations start small, validating the concept in one department before expanding. They gauge success using clear metrics: diminished response times, enhanced throughput, and decreased operational costs. This systematic method gains buy-in, reduces risk, and ensures quick returns on investment. Listen to the brief video below and share your thoughts on agentic AI use cases in the comments.  #EnterpriseAI #DigitalTransformation #CIOInsights #TechStrategy #CXOTalk #AgenticAI

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🤖 LLM vs. RPA vs. AI Agents, What’s the Difference? As organizations explore automation and AI adoption, the lines between tools can get blurry. Here’s a quick breakdown to clarify where each one fits, and why AI Agents are the next evolution: 🔹 LLM (Large Language Models) Think of ChatGPT or Claude. These models understand and generate natural language. They’re great at: * Answering questions * Summarizing documents * Drafting emails But they don’t take action or interact with systems. 🔹RPA (Robotic Process Automation) Think of bots that mimic clicks and keystrokes. RPA excels at repetitive, rule-based tasks like: * Filling forms * Moving files * Triggering workflows But it lacks reasoning, context-awareness, or conversation skills. 🔷 AI Agent (Agentic AI = LLM + RPA) This is where the magic happens. An AI Agent combines reasoning (LLM) with action-taking (RPA/APIs). It can: ✅ Understand intent (via LLM) ✅ Plan actions (based on context) ✅ Execute tasks (via RPA/API) ✅ Reflect and improve (via memory or feedback loops) 📌 Example – IT Helpdesk Use Case 🧠 LLM: Understands user's problem from free-text email ⚙️ RPA: Resets password in backend system 🤖 AI Agent: Does both — and confirms resolution via chat! 💡 The future isn’t about choosing LLM or RPA, It’s about intelligent agents that think, act, and learn. #AI #AIAgents #LLM #RPA #Automation #DigitalTransformation #ITServices #EnterpriseAI #AIOps #malomatia

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In today’s hyper-competitive marketplace, scaling AI is no longer optional—it’s mission-critical. This MIT Sloan Management Review article by CGI spotlights how organizations can shift from experimental AI pilots to enterprise-wide AI adoption that delivers tangible business outcomes. The key? Creating a robust data foundation, ensuring business and IT alignment, and fostering a culture of continuous learning and agile collaboration. Leaders must define clear success metrics, orchestrate cross-functional teams, and embrace strong governance to mitigate risks and drive long-term value. By embedding AI into core processes—ranging from customer engagement to supply chain optimization—companies can unlock new growth opportunities, reduce costs, and power innovation at scale. Perhaps most exciting is the “art of the possible” that emerges when AI initiatives are fully integrated. From refining operational efficiency to radically reimagining entire business models, AI can spark a wave of transformation that positions organizations to thrive in a data-driven future. The message is clear: those who boldly invest in AI’s full potential today will shape—and own—tomorrow’s competitive landscape. https://lnkd.in/eMyb6jRN

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