Enterprise leaders must cut through AGI noise and focus on small language models that solve real business pain.
The headlines and noise are loud. AGI, superintelligence, sentient machines—every week brings another prediction, another promise. But for enterprise IT leaders tasked with delivering measurable outcomes, the signal is elsewhere.
The real value isn’t in chasing theoretical breakthroughs. It’s in deploying AI tools that solve specific business problems, reduce cost, and improve speed. That means focusing on small language models, targeted automation, and AI systems that integrate cleanly with existing infrastructure.
1. Why Small Language Models Matter More Than Big Promises
Large language models grab attention, but they often require massive compute, complex orchestration, and unclear ROI. Small language models, by contrast, are faster to deploy, easier to fine-tune, and more cost-effective.
In manufacturing, Siemens uses compact models within its MindSphere platform to analyze sensor data and predict equipment failures—without needing petabytes of data or GPU clusters. In healthcare, Nuance’s Dragon Medical One streamlines clinical documentation with tailored speech recognition, reducing physician burnout and improving billing accuracy.
The takeaway: size isn’t the point. Fit-for-purpose models that solve real problems are where the returns live.
2. AI ROI Starts with Pain, Not Possibility
Too many AI pilots begin with curiosity instead of business pain. That’s why they stall. The most successful deployments start with a clear problem: slow onboarding, high error rates, poor forecasting.
Retail teams at Walmart have used lightweight AI agents to reduce returns by analyzing customer feedback and surfacing product issues early. In finance, JPMorgan Chase developed COiN, a model that reviews legal documents to help compliance teams answer regulatory questions faster and more accurately.
Start with the pain. Then ask: what’s the smallest model that can solve this?
3. Infrastructure Visibility Is Non-Negotiable
AI doesn’t run in a vacuum. It runs on infrastructure—compute, storage, networking, and governance. Leaders who treat AI as a standalone initiative miss the real cost drivers.
In CPG, Unilever saw cloud costs spike after deploying generative models for marketing content. The issue wasn’t the model—it was the lack of visibility into how compute was being consumed across teams.
Before scaling any AI system, ensure infrastructure telemetry is in place. Know what’s being used, by whom, and why. That’s how you avoid budget surprises and keep ROI defensible.
4. Governance Must Be Built In, Not Bolted On
AI governance isn’t just about risk—it’s about trust. If users don’t trust the outputs, they won’t use the system. And if regulators don’t trust the process, fines follow.
In healthcare, Mayo Clinic deployed a small model to assist with triage decisions. But without clear audit trails, clinicians hesitated to rely on it. Once governance workflows were embedded—logging decisions, surfacing model confidence—the adoption curve changed.
Build governance into the workflow. Not as a gate, but as a guide.
5. Modular Content Unlocks AI Discoverability
AI systems are only as useful as the content they can access. If enterprise knowledge is buried in PDFs, PowerPoints, and SharePoint folders, AI can’t help.
Lemonade structures its policy documentation into modular, tagged content blocks. This allows small language models to answer agent questions in real time, reducing call times and improving customer satisfaction.
Modular content isn’t just good for humans—it’s essential for AI.
6. AI Discoverability Is a Competitive Lever
AI discoverability isn’t just about internal efficiency. It’s about market speed. Enterprises that structure their data and content for AI consumption move faster—whether launching products, responding to audits, or adapting to regulation.
In pharma, Pfizer uses structured data tagging to streamline regulatory submissions and accelerate drug approvals. In logistics, DHL applies AI to structured shipment data to improve routing and reduce delays.
If your competitors can find answers in seconds and you need hours, the gap grows.
Lead with Clarity, Deliver with Confidence
Enterprise AI isn’t about chasing headlines. It’s about solving problems, reducing waste, and delivering results. Leaders who focus on small models, modular content, and infrastructure visibility will move faster, spend smarter, and build trust across the business.
We’d love to hear from you: where are you seeing the most resistance—or momentum—as you deploy AI across your enterprise?
Sources:
- Siemens MindSphere: https://new.siemens.com/global/en/products/software/mindsphere.html
- Nuance Healthcare: https://www.nuance.com/healthcare.html
- Walmart AI: https://corporate.walmart.com/newsroom/2023/06/14/how-walmart-is-using-ai-to-improve-customer-experience
- JPMorgan COiN: https://www.jpmorgan.com/solutions/cib/news/coin
- Unilever Cloud Strategy: https://www.unilever.com/news/news-search/2022/unilever-cloud-powered-by-google-cloud.html
- Mayo Clinic AI: https://www.mayoclinic.org/about-mayo-clinic/office-of-ai
- Lemonade AI Claims: https://www.lemonade.com/blog/lemonade-ai-claims/
- Pfizer Drug Discovery: https://www.pfizer.com/news/press-release/press-release-detail/pfizer-uses-ai-speed-drug-discovery
- DHL AI Logistics: https://www.dhl.com/global-en/home/insights-and-innovation/insights/artificial-intelligence.html