Tag: enterprise AI

  • Ford Pro’s Kevin Dunbar Shares How AI Transforms Fleet Management

    Episode Summary

    Kevin Dunbar joins Maribel Lopez to discuss how AI is revolutionizing commercial fleet management through Ford Pro Intelligence. With nearly two decades of experience at companies like Cisco and Palo Alto Networks, Kevin shares insights on how Ford's commercial division is processing over a billion data points daily to help fleet operators optimize operations, reduce costs, and improve safety.AI with Maribel Lopez: Transforming Fleet Management with Kevin Dunbar

    Guest: Kevin Dunbar, General Manager of Ford Pro Intelligence
    Host: Maribel Lopez, Founder of the Data for Betterment Foundation and Lopez Research

    Key Topics Covered

    Ford Pro Intelligence Platform

    • Commercial division serving business and government customers
    • Comprehensive ecosystem from vehicle upfitting to fleet management
    • Data services, telematics software, and fleet controls
    • Updated from last earnings to 757,000 and 24% yoy growth. (vs. 675,000+ subscribers with 20% growth rate.)

    Data at Scale

    • Processing over 1 billion connected vehicle data points daily
    • Sensor data ranging from tire pressure and GPS to seatbelt activity and driver behavior
    • Clean, structured data transformation into actionable insights

    AI Applications in Action

    • Digital vehicle walkarounds replacing 20-minute manual processes
    • Predictive maintenance moving customers from reactive to proactive service
    • E-switch assist tool using machine learning for electrification decisions
    • Connected uptime system achieving 98% vehicle availability

    Tangible Business Impact

    • 10% reduction in insurance costs through safer driving coaching
    • 20% improvement in driver safety metrics
    • 25% reduction in speeding incidents
    • 80% reduction in cost downtime
    • 10-20% total cost of ownership reduction


    Notable Quotes

    “We want to make sure that their Ford vehicle works as hard for their business digitally as it does mechanically.” – Kevin Dunbar

    “It's not just about having data. It's about having clean, structured data.” – Kevin Dunbar

    For more episodes of “AI with Maribel Lopez,” visit Lopez Research and follow our latest insights on AI transformation across industries.

    About Ford  Pro and Ford Pro Intelligence

    Ford Pro is helping commercial customers transform and expand their businesses with vehicles and services tailored to their needs. Ford Pro Intelligence is Ford’s comprehensive solution for fleet digitalization and operational efficiency, combining connected vehicle data, telematics tools, and smart management software under one platform

    Follow Kevin at https://www.linkedin.com/in/kevin-dunbar-78343558/

    Follow Maribel at https://www.linkedin.com/in/maribellopez/

    #FordProIntelligence #FordPro #FleetManagement #Fleets  #DataSecurity 

  • Verint Executive Reveals: The 3 Best Starting Points for Enterprise Agentic AI Adoption

    Episode Overview

    In this episode, Maribel Lopez sits down with David Singer, Global Vice President and Go-To-Market Strategy at Verint, to explore the rapid evolution from generative AI to agentic AI and how organizations can successfully implement AI solutions that deliver real business outcomes.


    Key Topics Discussed


    The Evolution from Generative to Agentic AI

    • Generative AI: Excellent at answering questions and synthesizing information from knowledge sources
    • Agentic AI: Takes the next step by actually executing actions autonomously, not just providing recommendations
    • The critical difference: autonomous decision-making versus rules-based automation


    Building Trust in Autonomous AI Systems

    • Start with human-in-the-loop monitoring for training and validation
    • Gradually reduce oversight from constant monitoring to spot checks
    • Apply quality monitoring practices to AI agents similar to human agents
    • Consider AI agents as “silicon-based employees” requiring training, access controls, and performance management


    Successful AI Implementation Strategies

    Start with Clear Outcomes: Define specific business goals before selecting technology

    • Focus on solutions that deliver outcomes, not just impressive technology
    • Begin with well-understood processes that can be enhanced rather than completely reimagined

    Three Proven Starting Points:

    1. Call Wrap-up Automation: AI-powered summarization reduces agent workload
    2. IVR Modernization: Convert top call flows to agentic conversational AI
    3. Quality Management: Scale from monitoring 1-3% of calls to near 100% coverage


    Vendor Selection Criteria

    • Proven outcomes at scale: Look for vendors with demonstrated success stories and customer references
    • Technology adaptability: Choose providers who can evolve with the rapidly changing AI landscape
    • Production readiness: “POCs are easy, production is hard” – prioritize vendors with production deployment experience


    Change Management for AI Adoption 

    • Deploy solutions that genuinely help employees first
    • Build internal champions through positive early experiences
    • Scale gradually to maintain trust and adoption


    Key Insights

    • Employee Experience Drives Customer Experience: AI solutions that improve employee satisfaction often lead to better customer outcomes
    • Observability is Critical: Comprehensive monitoring and quality management become essential as AI systems gain autonomy
    • Outcomes Over Technology: Success comes from focusing on business results rather than being enamored with the latest AI capabilities


    About the Guest

    David Singer is the Global Vice President and Go-To-Market Strategy at Verint, where he focuses on delivering AI-powered outcomes for customer experience automation. Verint has been incorporating AI into their platform for over a decade, evolving from call recording and workforce management to comprehensive CX automation solutions. 

    You can follow David here: https://www.linkedin.com/in/dwsinger/

    You can follow Maribel here: 

    Closing Thoughts

    Singer emphasizes two crucial points for organizations embarking on AI initiatives:

    1. Avoid spending significant resources on new technology only to use it exactly as you did before
    2. Always start with outcomes first – let business goals drive vendor selection, implementation strategy, and change management approaches

  • Cisco Live 2025: Jokel and Pandey on Enterprise AI Infrastructure and the Internet of Agents

    In this episode from Cisco Live, Maribel Lopez sits down with two Cisco executives, Vijoy Pandey, SVP of Outshift at Cisco and Nathan Jokel, SVP of Corporate Strategy and Alliances at Cisco, to discuss how AI is fundamentally changing enterprise infrastructure over the next year. The conversation explores the evolution from deterministic to probabilistic computing, the emergence of agentic workflows, and practical advice for business leaders navigating the AI transformation.

    Host: Maribel Lopez
    Guests:

    • Vijoy Pandey, SVP of Outshift at Cisco
    • Nathan Jokel, SVP of Corporate Strategy and Alliances at Cisco

    Recorded at: Cisco Live

    Episode Overview

    In this episode from Cisco Live, Maribel Lopez sits down with two Cisco executives to discuss how AI is fundamentally changing enterprise infrastructure over the next year. The conversation explores the evolution from deterministic to probabilistic computing, the emergence of agentic workflows, and practical advice for business leaders navigating the AI transformation.

    Key Topics Discussed

    The Three Waves of AI Infrastructure Evolution

    • Wave 1: AI training in public cloud (mostly behind us)
    • Wave 2: AI inference moving to enterprise data centers for control, security, and economic reasons
    • Wave 3: AI moving to the edge with physical and embodied AI requiring new infrastructure for robots and devices

    From Deterministic to Probabilistic Computing

    Vijoy explains the fundamental shift happening in computing:

    • Traditional computing: deterministic, machine-speed but limited
    • Human intelligence: agentic but slow
    • New paradigm: AI agents with human-like behavior operating at machine speed and scale

    The Internet of Agents

    A collaboration platform where AI agents from different vendors can:

    • Get discovered and authenticated
    • Compose workflows together
    • Execute tasks collaboratively
    • Be evaluated for performance

    Real-world example: Building a sales funnel portal using agentic interfaces from Salesforce, ServiceNow, Microsoft, and Cisco security – all working together without manual UI clicking.

    AI and Energy Challenges

    • The Problem: By 2028, projected 63 gigawatt shortfall for new data center capacity
    • Solutions:
      • Invest in diverse energy sources (nuclear, renewables, battery storage)
      • Build data centers near power sources (e.g., Cisco's Middle East partnerships)
      • Develop more energy-efficient infrastructure
      • Focus on smaller, specialized models instead of racing for maximum parameters

    Cisco's Specialized AI Models

    • Foundation SAC 8B: 8 billion parameter model specialized for security policy
    • Deep Network Model: Expert model trained on network configurations


    Outshift: Cisco's Innovation Engine

    Cisco's internal incubator tackling problems adjacent to core business in:

    • Space: Areas adjacent to networking, security, observability, collaboration
    • Time/Risk: Higher-risk ventures that can't enter at Cisco scale initiallyCurrent Big Hairy Audacious Goals (BHAGs):
    1. Internet of Agents
    2. Quantum Internet – building quantum networks for distributed quantum computing

  • SAP Modernization Gains Momentum: Customer Success Stories From Sapphire

    SAP Modernization Gains Momentum: Customer Success Stories From Sapphire

    For nearly two decades, Lopez Research has tracked SAP customers’ struggles when transitioning to standardized, cloud-based solutions. The reality of enterprise IT deployments is undeniable: SAP powers critical business operations—from finance and human resources to sales, marketing, and supply chain management—making any transition extraordinarily complex.

    Lopez Research has consistently heard these concerns from organizations across industries. However, we’re witnessing a significant shift as customers finally embrace modernization. Two key factors drive the modernization momentum: substantially improved SAP offerings and an urgent desire to capitalize on artificial intelligence capabilities that require modern infrastructure.

    Migrating business software from on-premises to cloud environments is like changing a car’s tires while driving at full speed. This challenge gets amplified by years of customizations that make simple drop-in replacements to modern cloud offerings impossible. The path to a new business suite is inevitably costly, complex, and time-consuming.

    Simplifying Business with SAP

    At Sapphire 2025, SAP’s message was unmistakable: they’re committed to simplifying how customers engage with their products—whether through faster, more cost-effective modernization of existing solutions, embracing AI innovations, or building solutions with partners. This comprehensive transformation has been years in the making.

    On day one, Christian Klein introduced a new transition guidance tool to accelerate migrations with fewer resources. SAP is streamlining AI deployment by embedding intelligence directly into its products. The company expects to deliver more than 400 embedded AI scenarios by year-end, with Joule functioning as a CoPilot interface across the entire suite. Multi-agent frameworks are this year’s dominant theme, and SAP has embraced agentic AI capabilities. A key objective is providing tools that reduce custom application and AI development costs throughout the entire lifecycle. SAP is enhancing customer experience through several key initiatives:

    • Line-of-business specific solution packages tailored to different buying centers (finance, HR, supply chain, sales, procurement, services)
    • Simplified commercial terms and contracts
    • Out-of-the-box AI packages with managed integration
    • Pre-packaged, SAP-managed industry-specific content
    • Inclusion of SAP Build to extend applications while maintaining a clean core

    With these changes, SAP aims to create solutions that deliver a “best of suite as a service” approach, significantly reducing complexity and implementation costs for companies adopting SAP’s business suite.

    At Sapphire 2025, SAP also announced new partnerships with AWS and Perplexity.ai. The Perplexity answer engine is being embedded directly into SAP Joule. It allows companies to integrate accurate, trusted, real-time answers on public web data with their SAP data for enhanced decision-making. The AWS collaboration establishes a dedicated AI co-innovation program and fund to support the development and deployment of generative AI applications on the SAP Business Technology Platform for cloud ERP workloads.

    SAP Responds to Customer Challenges

    On day two of SAP’s Sapphire conference, Thomas Saueressig acknowledged these customer challenges and highlighted the company’s progress over the past year. At last year’s Sapphire, SAP introduced its simplified engagement model, the RISE with SAP offering, and an integrated tool chain to streamline transformation processes. This year, Saueressig revealed that “customers using the RISE with SAP methodology and the integrated tool chain saved up to 30% of the cost for their transformations.”

    The RISE with SAP offering helps businesses transition their SAP ERP systems from on-premises to the cloud. Rather than a new standalone product, it’s a bundled package of software, platform, infrastructure, and services, marketed as “Business Transformation as a Service.” The RISE with SAP methodology encompasses SAP S/4HANA Cloud and the SAP Business Suite, enabling customers to progress from fit-to-standard to fit-to-suite implementations.

    SAP also offers GROW with SAP, a program tailored for small to medium-sized businesses seeking rapid cloud ERP adoption. It delivers a comprehensive package of solutions, best practices, adoption services, community access, and learning resources for smooth cloud implementation. The company now supports 8,500 RISE with SAP customers and manages over 146,000 systems with an impressive five-nines (99.999%) availability service level agreement.

    While companies recognize data as their strategic asset, unlocking its value has remained challenging. In February 2025, SAP announced the SAP Business Data Cloud (BDC)—a next-generation, fully managed Software-as-a-Service platform that unifies, governs, and analyzes business data across SAP and third-party sources. This solution represents the evolution of SAP’s data and analytics offerings, integrating capabilities from SAP BW, SAP Datasphere, SAP Analytics Cloud, and new AI features enabled through a strategic partnership with Databricks.

    Real-World Customer Success Stories

    The critical question remains: Will SAP’s efforts yield meaningful results? This year’s Sapphire showcased customers sharing their modernization journeys and the value they’re creating through SAP solutions and artificial intelligence.

    Mercedes-Benz: Navigating Complex Transformation

    Mercedes-Benz decided to adopt RISE with SAP in 2024. Katrin Lehmann, CIO of Mercedes-Benz Group, explained, “I took on that role just a year ago, and the first thing you do is assess what you have. I have over 10,000 applications in the system landscape, and 1,200 of those are SAP applications. Most of them (SAP apps) are still on ECC. How did we tackle that topic? We bought LeanIX.”

    SAP’s LeanIX software provides comprehensive visibility into an organization’s entire software estate—whether purchased, planned, or custom-built. By offering detailed insights into the current IT landscape and all interdependencies, SAP LeanIX helps companies plan and navigate both technical and business aspects of ERP transformation.

    Lehmann continued, “We implemented LeanIX and sorted the systems by importance. We used the time methodology, transform, invest, migrate, eliminate, to plan where we need to go. We took the most important systems, and we’re migrating those to RISE right now.”

    She emphasized that “Moving to RISE also means that we can leverage innovation, and AI is very important to us. But, AI is not new to us at all.” Mercedes-Benz engineers have been using GitHub Copilot for several years throughout their supply chain and value chain. The company has developed its own internal ChatGPT called MRS GPT, which is used across finance, HR, and manufacturing plants. When quality issues arise, workers can use natural language to ask MRS GPT for resolution assistance. The company is now looking to incorporate Joule and other SAP AI capabilities.

    Metal Services: Transformation During Crisis

    Jeff Suellentrop, CITO of Metal Services LLC, shared how his company grew too quickly and needed to reinvent its business during bankruptcy, which followed rapid growth. “We wanted to reimagine how we do business. We selected GROW with SAP, and an all-best practice implementation to replace every system in the entire company. Now there are other subsystems, but we got rid of the paper and pencil. And not only do we use the best practices for simplification, but also to level up. The (SAP) best practices were better than many existing processes. It was a few huge lifts, and we went live within eight months of initiating that process.”

    He added, “We’ve built out our full data model, which is one of the big unlocks (of value). We have the ability with a standardized, out-of-the-box core product to deploy to multiple sites, and then we can continuously innovate on top of that. We use SAP maintenance to maintain all our equipment, and we’ve returned millions and millions of dollars back into the business. Our profitability and utilization are up. We have some other AI models as well, and we’ve just scratched the surface.”

    Mars: Multi-Business Unit Strategy

    Large multinational companies face unique challenges when designing systems that can’t follow a one-size-fits-all approach. Will Beery, Global CIO of Mars Snacking, outlined the company’s digital strategy to create an architecture that is “global, standardized, scalable, and has the ability to make it easier to plug in acquisitions.”

    “We’re using the public cloud to house True Fruit, Nature’s Bakery, and now KIND. For my retail business, we’ve deployed a retail-specific RISE instance that will run M&M stores but also be fit-for-purpose (solution) for Hotel Chocolat. For Mars Wrigley, we’re developing a more customized, complex template for the rest of Mars Incorporated. The three-tier model allows us the agility to support smaller businesses like KIND. It allows us interoperability across all businesses, to drive scale and efficiency where it makes sense. It’s also going to enable a massive integration if we were to acquire a sizable snacking business in the future. SAP and RISE are a massive partner, helping us do that fast and at a lower total cost of ownership.”

    Praveen Moturu, Global Vice President – Digital Platforms at Mars Information Services, elaborated, “We took a model-driven approach from our business capabilities, business processes, data, applications, and technology, and put those models in the SAP Business Suite. It has helped us to build a cleaner platform for the future, which is resilient, and that’s one of the key cornerstones of our transformation.”

    Starting with what SAP calls a “clean core” is challenging but potentially rewarding. Moturu explained, “The productivity from the business suite is enormous for us. We started with leveraging the automation that’s already available in the (SAP) Business Suite to reduce manual interactions, human errors, and give our associates time back. Real-time data and embedded analytics helped us to drive productivity and make accurate and timely decisions that help us to drive quality. And last, but not least, the AI components that the business we introduced with Joule have increased productivity across the company. We have deployed it in our public and private cloud, and it helped us onboard new users, change management, and learning.”

    NBCUniversal: From Project to Platform Mindset

    How you start the transformation journey will shape how resilient and flexible your company becomes as market dynamics change. Abhinav Gupta, SVP of Enterprise Product at NBCUniversal, shared how his company approached transformation and offered advice for business leaders.

    “The fundamental premise of our strategy is to consolidate disparate technology platforms supporting similar processes onto a common technology platform with a standardized set of processes. It sounds easy, right? This is hard to do because this is not about the technology. It requires driving a significant mindset shift within the company. The way I like to verbalize this mindset shift is this concept of moving from a project mindset to a product or platform mindset.”

    Gupta explained how NBCUniversal structured its governance to drive this mindset shift: “One of the ways we are driving this mindset shift within NBC Universal is in the way that we have structured our governance model. The steering committee for our finance transformation effort has representation across the entire breadth of the company. We have the four CFOs who represent each of the four divisions. We have our chief accounting officer, our corporate Financial Planning and Analysis leader, our global CIO, our CTO, and our head of AI on the steering committee as well. We also have representation from EY, our primary Systems Integrator in this effort, and SAP.”

    He praised SAP’s collaborative approach, noting that SAP “brought its top product and engineering folks that have collaborated with our teams, and collectively, we have overcome some significant problem statements.”

    Despite the successes, NBCUniversal acknowledged the ongoing challenges of transformation. Gupta emphasized, “In spite of the incredible wins that I’ve shared with all of you today, we still have a long way ahead of us. The one key takeaway for the audience is this. It’s important to bake in incremental wins across your long transformation journeys. This is helpful to keep your organization and people energized, which is crucial to your success.”

    Expanding Success Stories

    Saueressig highlighted additional customer achievements, including PHP’s creation of $500 million in value using Signavio for business process transformation, enabling the company to reinvent its shared service center model. SAP Signavio is a comprehensive suite for business process transformation that helps organizations design, analyze, improve, manage, and monitor process changes.

    Nestlé saved over 5 million hours using solutions like SAP’s WalkMe—a digital adoption platform providing in-app guidance, automation, and analytics—to drive enablement and digital adoption for 270,000 people across more than 200 applications. First Abu Dhabi Bank achieved a 25% reduction in application count.

    Jan Gilg, Chief Revenue Officer for SAP Americas and SAP Business Suite, and Member of the SAP Extended Board, emphasized, “An integrated, harmonized enterprise foundation is the key, and that’s the reimagined SAP Business Suite. It is data-driven, AI-powered, and can democratize access to business AI. I’m convinced that this will then truly unlock the next wave of productivity, and the time is now.”

    Lopez Research Perspective

    Lopez Research believes we’re witnessing a critical inflection point in enterprise software modernization. After years of hesitation, SAP customers are accelerating their transformation journeys, driven by more flexible migration options and the compelling business case for AI-powered operations. While the path remains challenging, the potential benefits in operational efficiency, data-driven decision making, and competitive differentiation are becoming too significant to ignore.

    SAP customers have long viewed their relationship with the software provider as a partnership rather than a typical vendor arrangement. They expect SAP to innovate on their behalf but adopt these innovations cautiously, recognizing the potential business disruption of hasty implementation. This balanced approach to modernization—combining thoughtful planning with strategic ambition—appears to be gaining traction as organizations seek the agility needed to thrive in increasingly digital markets.

  • Why Small AI Models Matter: The Business Case for Enterprise AI Efficiency

    In the rapidly evolving landscape of artificial intelligence, business leaders face a critical decision: which AI models will deliver the most value to their organization? While much attention has focused on massive models with billions of parameters, there’s a compelling case for considering smaller, more specialized AI models. Lopez Research interviewed Kate Soule, Director of Technical Product Management for IBM’s Granite products, to learn more about these smaller models.

    IBM Granite represents a family of open AI models specifically designed for transparency, data governance, and practical business applications. IBM’s third-generation of Granite models (3.2) offer reasoning capabilities and multimodal models that offers vision and has been optimized for document understanding.

    Big News: Small Models Will Make Enterprise AI More Efficient and Effective. Image Source: Adobe Stock

    Why Size Matters (and Why Smaller Models Can Be Better)

    Soule says, “Everything gets more difficult as the model gets larger.” This challenge manifests in several business-critical ways:

    • Higher operational costs. Larger models require more computing resources and energy, directly increasing operational expenses.
    • Increased latency. Bigger models take longer to generate responses, potentially degrading customer experience.
    • Need for powerful hardware. If using larger models in the cloud or on-premises, these models demand more powerful and expensive GPU infrastructure.
    • Limited customization. Adapting massive models to your business needs requires substantial computing resources and expertise.

    The Small Model Efficiency Advantage

    IBM’s approach with their Granite models focuses on efficiency through purpose-built smaller models. At just 2 to 8 billion parameters (compared to the largest industry models, such as Llama 3.1 from Meta, exceeding 400 billion parameters), these models deliver several key advantages, including::

    1. Cost-effectiveness. Lower computational requirements translate directly to reduced operational costs.
    2. Customization potential. Smaller models are more manageable and less expensive to fine-tune for specific business tasks.
    3. Deployment flexibility. Some models are small enough to run locally on standard hardware, such as an AI PC, eliminating cloud dependency.
    4. Faster response times. Reduced model size means quicker inference and better user experiences.

    Whether it’s Small Models or Large Models, Companies Need a “Fit-for-Purpose” Approach

    Rather than seeking a single “model to rule them all,” business and IT leaders are working together to assemble a portfolio of AI solutions that incorporate foundational LLMs with specialized AI models to support different business needs. As Soule notes, “To get value and to be able to deploy AI cost-effectively… you need to consider having fit-for-purpose models.”

    Fit-for-purpose AI model selection allows businesses to optimize performance and cost based on the specific requirements of each use case. A larger model may be the best solution for high-value, complex tasks. For routine operations, a smaller specialized model often delivers comparable results at a fraction of the cost.

    Soule shared that IBM’s Granite models embody this philosophy with their modular design. Instead of trying to create a single massive model for all tasks, IBM has developed distinct models for different enterprise functions. For example, IBM’s Granite solutions offer the models for use cases such as coding, time-series forecasting, security and language models for designed for agentic workflows, RAG etc.

    Despite their efficient design, IBM shared that Granite models don’t sacrifice performance for cost. According to IBM’s benchmarks, Granite outperforms comparable models across various enterprise tasks, achieving high scores on Hugging Face’s RAGBench Leaderboard. This targeted approach ensures organizations can select the right tools for specific business challenges without unnecessary computational overhead.

    Multimodal Models: Expanding AI’s Capabilities

    Multimodal AI models can simultaneously process and interpret multiple types of data inputs—such as text, images, audio, and video. Unlike unimodal models that work with only one data type (typically text), multimodal models can understand the relationships between different forms of information, similar to how humans process the world through multiple senses:

    IIBM’s Granite 3.2 vision models offer a practical application of multimodal capabilities in an enterprise context. Rather than focusing on image generation (creating pictures from text prompts), these models specialize in image understanding—extracting valuable information from visual content. At just 2 billion parameters, these specialized vision models can:

    • Extract data from documents, even poorly scanned PDFs
    • Analyze charts and graphs to answer specific business questions
    • Process dashboard screenshots to provide insights on performance metrics
    • Interpret receipts and other visual business documents

    Making Strategic AI Decisions: The Performance-Cost Matrix

    As AI technology evolves, organizations must balance cost, accuracy, and safety for model use. Soule shared at least four guidelines for evaluating what models to use within the enterprise, including:

    1. Right-sizing your models: Match model capabilities to business requirements rather than defaulting to the largest available.
    2. Design model selection criteria with transparency and governance in mind: Understand how models were trained and whether they align with your governance requirements.
    3. Systems-based security: Implement guardrails and safety protocols beyond relying solely on the model’s built-in safeguards.
    4. Customization needs: Assess how much adaptation a model will require for your specific use cases.

    For example, Soule discussed how different models may offer various levels of transparency and governance. One distinguishing feature of IBM Granite models is that IBM publishes detailed information about their training datasets and methodologies, allowing enterprises to understand what’s “under the hood” of these models. The Granite ecosystem includes risk and harm detection capabilities, transparency tools, and IP protection. Model transparency allows IBM to provide indemnification for Granite models, offering businesses additional protection when deploying these AI solutions.

    The Shift is Underway

    The “bigger is better” paradigm is giving way to a more nuanced approach to enterprise AI. Business leaders can achieve comparable performance by strategically implementing smaller, specialized models for appropriate use cases while significantly reducing costs and complexity. IBM’s Granite models are one example of this approach. We’ve also seen the large foundation model providers, such as Open.AI and Meta, support smaller models. 

    Looking ahead, we’re moving toward more flexible AI deployment models where businesses can dynamically allocate resources based on task importance. This could mean using models of various sizes for different tasks or enabling features like “reasoning” when demands such as accuracy justify the additional cost and latency. As AI becomes further integrated into business operations, this efficiency-focused approach will be increasingly critical for sustainable AI adoption and competitive advantage.

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