The New 'Musts' for Scalable AI: Cost Control, Governance And Business Outcomes

Organizations are pumping the brakes on the full-speed-ahead AI era. While AI adoption will continue to move forward, enterprises are taking a more measured approach as concerns grow over rising AI costs.

Though the price of individual AI tokens is falling, overall business AI costs are surging as more vendors shift to consumption-based pricing models, AI pilots increasingly move into production and day-to-day use of AI tools grows. By 2028, Gartner predicts (June 24, 2026) that costs for AI-heavy developer roles could rise above average developer salaries.

The New AI Economics: Balancing Innovation And Inferencing Costs

Every prompt, agentic AI action and AI generated output consumes an organization’s compute resources. As AI use expands to enterprise-wide deployments, AI inferencing costs can quickly expand beyond set budgets.

Organizations must now make complex decisions about where AI workloads should ideally run to power innovation and stay competitive without driving up costs. Because AI deployment strategies are evolving, testing is a valuable approach before making long-term infrastructure decisions.

“Running parallel pilots is a critical element in getting real-world data about what the cost infrastructure and return on invested capital look like. There is not one model that will fit all,” said Todd Gustafson, president HP Federal LLC & U.S. public sector for HP Inc.

Cloud environments are essential for large language model (LLM) training and compute-intensive AI applications. Costs can spiral, however, when controls and governance are missing, and everyday AI tasks happen in the cloud rather than on localized infrastructure like edge devices and AI PCs equipped with NPUs, CPUs and GPUs.

Deliver AI Guardrails, Data Readiness And ROI Clarity

This complexity is an opportunity for channel partners to help customers scale AI without driving up costs. Customers are increasingly depending on solution providers across four critical areas:

1. AI workload placement. Defaulting workloads to the cloud can be too expensive. Organizations need support with ‘right workload, right location’ assessments for different tasks and determining the most cost-effective environments based on performance, security, latency and compliance requirements.

2. AI solution and user matching. Compute and AI needs vary by employee. Solution providers can evaluate roles, workflows and AI use patterns to recommend the optimal mix of AI PCs, cloud-based AI services and AI software licenses. Knowledge workers can benefit from AI PCs that execute tasks locally, while also accessing cloud-based AI when a project has greater compute demand. Developers, engineers and data scientists often rely on cloud-hosted AI environments for compute-intensive projects, but also benefit from AI workstations with powerful processors and discrete graphics cards that support more demanding local workloads. Strategic device planning balances workload locations and can improve workforce productivity without overspending.

HP’s Workforce Experience Platform (WXP) offers insights into how employees work and what resources they need, which guides more informed AI device, tool and workload decisions.

“WXP uses telemetry data to develop the right persona for the right user,” Gustafson said. “Organizations can make AI investments that are less guess-based and more analytical, giving the power to the people who really need it.”

3. AI solution governance. Robust governance is essential for data security, compliance across global operations and the quality of AI-powered output and decision making. The recent rogue actions of ChatGPT and Claude AI models have intensified concerns about AI governance and controls for human users and autonomous agents alike. Channel partners can help customers establish AI platform governance frameworks, develop protected data policies, and manage AI subscriptions and licensing to prevent costly sprawl.

“An important consideration is the data itself,” Gustafson said. “Does it involve personally identifiable information, company confidential information or sensitive government data? When AI operates within a protected environment (as in the case of a local AI PC), organizations gain confidence that it cannot be compromised.”

4. ROI outcome measurement. Business impact metrics, rather than user enablement or number of solution licenses, are now paramount. Enterprises need support establishing and continuously measuring outcome-focused KPIs for AI solutions and programs. This empowers them to prove value with executive stakeholders around employee productivity, cloud inferencing, customer service response times, workflow automation and all AI investments.

“Now is the time to bring the PC back into the calculation and show customers the power of these end-user devices,” said Jason Mooneyham, corporate vice president, global commercial channel & North America channel chief, AMD “Bringing models down to the client level, rather than generating massive token costs, should be factored into the budget and the overall ROI plan.”

AI PCs: Powerful, Local Compute For Cost-Efficient AI

AI PCs are increasingly core building blocks of internal and customer-facing AI that can scale without skyrocketing infrastructure costs and hidden security risks. Equipped with dedicated NPUs and powerful CPUs and GPUs, AI PCs execute everyday tasks locally. Document summarization, content creation, analysis of smaller data sets and collaboration happen on the device’s compute, which can reduce overall cloud token spend.

For solution providers, HP AI PCs powered by AMD Ryzen^TM AI PRO processors are a practical pathway to strategic AI consulting and recurring service delivery around governance, AI workload frameworks and infrastructure, and AI investments that deliver meaningful business results.

“Bring the AI to the user, bring the AI to the data and bring the AI to the device where it will be most impactful and effective,” Mooneyham said. “That is how you can start to take dollars being spent on larger models and bring them down to show ROI.”

Learn how you can build smarter AI economics strategies for your customers in partnership with AMD and HP.