Embedded Files
Enterprise AI Manifesto: The Right-Sized Transformation
We Stand at a Crossroads
The Generative AI revolution began with giants — massive models that promised to transform everything.
But promises alone don't build sustainable businesses. Today's enterprises face a harsh reality: runaway AI costs, ungovernable black boxes, and one-size-fits-all solutions that fit no one perfectly.
We reject this path.
The future belongs not to those who build the biggest models but to those who build the right ones. We champion a new paradigm: Right-sized AI powered by small language models (SLMs) that deliver precision, efficiency, and control.
Our Declaration
We believe that enterprise AI must serve enterprise needs — not the other way around. This means:
Intelligence Over Size: A model's value lies not in its parameter count, but in its ability to solve real problems efficiently. Small language models — with millions to billions of parameters instead of hundreds of billions — deliver targeted intelligence where it matters most.
Ownership Over Dependence: Every enterprise deserves to own its AI destiny. By training and deploying models on proprietary data within corporate firewalls, we maintain control over our most valuable asset: our data.
Efficiency Over Excess: We refuse to waste compute, energy, and capital on oversized solutions. Right-sized models cost 3-23× less than massive foundation models, matching their accuracy on specialized tasks.
Speed Over Scale: In business, milliseconds matter. Small models respond in sub-second timeframes, enabling real-time decision-making and seamless user experiences.
Transparency Over Opacity: We demand to see inside our models. Open, auditable SLMs replace proprietary black boxes, enabling trust, compliance, and continuous improvement.
The Three Pillars of Right-Sized AI
1. Controlled Economics
We reject the unpredictable per-token pricing that makes large language models a financial liability. Small models operate with predictable costs, requiring a fraction of the compute resources while delivering equivalent results for domain-specific tasks. This isn't just cost reduction—it's strategic liberation from vendor lock-in and usage anxiety.
2. Sovereign Data
Your data belongs to you. Period. Small language models trained on internal datasets and hosted within corporate infrastructure ensure that sensitive information never leaves your control. This isn't just about privacy—it's about competitive advantage and regulatory compliance in an increasingly regulated world.
3. Adaptive Architecture
The enterprise of tomorrow runs on hybrid intelligence: multiple specialized models working in concert, each optimized for specific tasks. Like microservices for AI, this architecture enables rapid iteration, targeted updates, and resilient performance. When regulations change or business needs evolve, you update the relevant model—not your entire AI infrastructure.
The New Rules of Enterprise AI
Rule 1: Match the Model to the Mission
Use SLMs for routine, high-volume tasks requiring speed and consistency.
Reserve LLMs for genuinely open-ended, creative work.
Deploy multiple models in specialized roles, rather than one for everything.
Rule 2: Domain Expertise Trumps General Knowledge
Fine-tune models on contextual industry-specific data.
Embed regulatory knowledge and business logic directly into the model.
Create AI agents that understand your business, not just human language.
Rule 3: Control Your Stack
Train and host models within your IT infrastructure.
Maintain transparency into model behavior and decision-making.
Build a capability that can't be controlled by external vendors.
Rule 4: Scale Intelligently, Not Just Bigger
Optimize for efficiency, not just capability.
Measure success by business outcomes, not model size.
Invest in sustainable Applied-AI that grows with your business.
The Vertical Revolution
Industry-specific software is already transforming markets by delivering tailored workflows rather than generic features. Small language models accelerate this trend by embedding domain expertise directly into applications:
Healthcare SaaS processes medical records while maintaining HIPAA compliance.
Financial platforms flag suspicious transactions using fine-tuned regulatory knowledge.
Manufacturing systems predict maintenance needs through real-time sensor analysis.
Retail solutions optimize inventory based on regional preferences and market dynamics.
This isn't theoretical — 76 percent of organizations using AI-driven vertical SaaS report significant ROI from improved customer engagement and operational efficiency. That's a compelling business outcome.
Your Path Forward
The transformation begins now. Here's how to build your right-sized AI strategy:
Phase 1: Audit and Align
Identify high-volume, routine tasks currently handled by expensive LLMs or human labor.
Map your organization's data assets and governance requirements.
Define success metrics based on business outcomes, not technical benchmarks.
Phase 2: Pilot and Prove
Start with one focused use case aligned with your business.
Fine-tune an open SLM on your specific data and requirements.
Measure performance against both accuracy and operational efficiency.
Phase 3: Scale and Systematize
Deploy multiple specialized models across different business functions.
Build hybrid architectures that leverage both SLMs and LLMs.
Create feedback loops for continuous model improvement.
Phase 4: Own and Optimize
Bring Applied-AI model training and inference fully in-house.
Develop internal expertise in model fine-tuning and deployment.
Build competitive moats through proprietary GenAI capabilities.
The Enterprise AI Future is Right-Sized
We are not anti-LLM progress. We are pro-intelligent progress. The future of enterprise AI isn't about building ever-larger models — it's about building ever-smarter business transformation solutions.
In this future, AI serves business goals rather than driving them; Intelligence scales with efficiency, not just compute; Every organization controls its own AI destiny; Innovation accelerates through focused, domain-specific models.
The giants had their moment. Now it's time for precision, control, and customized intelligence.
Right-sized AI transformation has begun. The question isn't whether you'll join it — but will you lead it.
This represents a fundamental shift from the "bigger is better" LLM mentality to strategic, purpose-built SLM AI solutions. The market leaders that embrace Right-sized Applied-AI models today will define the competitive landscape tomorrow.
Next, act now to discover How to Lead an Applied-AI Initiative.
Next > Microsite Projects
Google Sites
Report abuse