Interest in artificial intelligence (AI) has never been higher. Neither has AI spending. Gartner predicts worldwide AI investments will rise to about $2.527 trillion in 2026 and to more than $3.336 trillion in 2027. Gartner also calls out AI software, which had a market value of $283 billion in 2025, and expects it to grow dramatically to reach $636 billion by 2027.
The business opportunity for ISVs is real; however, before you move from ideation to development with your AI solution, you need to make a crucial decision: Where will AI interference live? Cloud now dominates, allowing companies to train complex models, base AI problem-solving on massive data volumes, and scale without their own datacenters. But as some businesses deploy their AI solutions, whether generative, predictive, conversational, assistive, or agentic, they’re finding cloud AI has some shortcomings. As a result, they’re looking for ways to move AI decision-making to the edge.
Why Your Market Is Searching for Local AI Solutions
Local AI, which runs AI models on a local device rather than in the cloud, overcomes four significant challenges with cloud AI:
- Predictable Costs: Companies have quickly discovered that AI costs in the cloud are hard to control. They want and need their team to be using AI, but cost-effectively. Paying for AI by the token is a convenient way to start, but you’re relying on your staff to spend within budgets, and they also can’t predict or control token costs at their level. With an up-front investment, local AI solves this dilemma. Fixed infrastructure, fixed capacity, fixed costs. The team is never cut off from tokens by a budget – as capacity is stressed, tokens slow down rather than stop. Your fixed capacity is always delivering a continuous capacity of intelligence to your business.
- Data Privacy: Cloud AI models can potentially put data privacy at risk, for example, from data leakage when the model learns from information outside the training data set. The issue can lead to noncompliance for businesses in industries that must protect data under regulations like HIPAA or GDPR. However, it can also lead to intellectual property (IP) exposure, revealing trade secrets, damaging brand reputation, and destroying a company’s competitive edge.
- Availability: Cloud-based models and systems are rolled out and pulled back frequently because of capacity or regulatory issues. At this early stage of AI evolution, models are frequently deprecated as cloud capacity is allocated to the latest models. A local model, by contrast, is fully under your corporate control. Once deployed, you can run the same workload on the same model today, and for years to come.
- Reliability: Cloud AI requires a constant network connection; it doesn’t work offline. Local AI using data generated or stored on premises doesn’t depend on a network connection, so it can work uninterrupted.
Many businesses are finding that a hybrid model of local AI for low-latency, reliable operations with minimal data privacy risks, and cloud for model training and data storage.
The Opportunity for ISVs
The race to implement AI is creating a unique opportunity for ISVs to break into new markets: Companies in a range of industries want edge capabilities but lack in-house expertise or relationships with software providers who can deliver them. Furthermore, ISVs focused on developing software for those verticals can use that expertise to rise above generic offerings and address the unique needs of their markets. ISVs can perfect models and their supporting harnesses and skills with deep domain knowledge, built-in compliance, and workflow support to create high-value solutions. In addition, ISVs taking a holistic approach can vet and recommend local AI devices and offer device monitoring and management solutions that ensure uptime.
Forward-thinking ISVs will watch the direction the industry is moving, which currently appears to be away from exclusively cloud AI to local AI or hybrid solutions. Remember that companies won’t be focused on buying a local AI solution per se, but they will invest in outcomes.
Determine the best way to meet your market’s needs for availability, reliable performance, data protection, and cost control while delivering intelligent solutions that achieve their business objectives. You’ll likely conclude the solution is to bring AI to the edge.
