By Mahesh Thiagarajan, Executive Vice President, Oracle Cloud Infrastructure
The hype around AI continues to grow, and the technology is moving faster than even the most optimistic forecasts, reshaping what’s possible for startups almost overnight. While it’s always fun to discuss pie-in-the-sky ideas, the real question is not just what AI can do, but what it should do for your business.
Startups need measurable results: better products, faster time to market, smarter customer insights, and new revenue streams. What matters most is aligning AI with these business objectives and using infrastructure that can empower you to keep up with your competition, support growth, and please customers.
From my conversations with business leaders, the most important considerations fall into three categories:
- Can I do what I do today more efficiently— faster, cheaper, or with fewer resources?
- Can I grow my customer base faster and/or deliver more value to existing users?
- Can I deliver new revenue from products or services I hadn’t previously considered?
The answers to these questions should help you decide where to spend time, where to invest, and how to build AI into your business.
Below are six key areas that you and other startup founders should keep in mind as you put AI to work.
1: Focus is critical
For startups, every decision matters, especially when evaluating AI choices. It’s important to make sure these investments are directly connected to business goals. Start by asking the three questions above and use the answers to help you choose the infrastructure tools needed to support your direction. In a world of distractions, focus gives you an edge. Pick tools that free up your time to solve the most important problems for your business.
One of the biggest advantages startups have today is the ability to fail fast and iterate rapidly. We’re now seeing a shift where rapid experimentation isn’t just for early-stage companies anymore. We recently worked with several customers in the core AI space that went from proof of concept to full-scale AI deployment in under three months. A year ago, that might’ve taken 12 months. Pick a cloud infrastructure partner that works the same way, enabling you to test quickly, iterate faster, and scale when you need. Focus isn’t about doing less – it’s about meeting customer needs faster.
2: Change is constant
The AI landscape isn’t just evolving—it may be accelerating faster than any other technology wave in the history of mankind. And now a new phase of agentic applications is upon us. This next wave of AI workloads will be defined by thousands of agents designed to talk to each other in short bursts to perform tasks. Imagine AI agents handling your DMV registration: one handles authentication, another takes payment, and another schedules delivery – all without you clicking through 10 screens.
This shift demands a new foundation. For this to work, agents need a standard way to talk to each other and access their data sources quickly. The Model Context Protocol is important in paving the way for a wave of AI-driven agentic applications which are designed to fetch and return responses to questions with minimal human help. Many of these applications are already emerging, with broader adoption likely in the next 12 to 18 months.
Startups seeking to capitalize on this shift must prioritize infrastructure that delivers real-time data access, low-latency networking, and the elasticity required to power agent-based ecosystems.
3: Innovation at every layer
The rapid growth of AI is driven by innovation across the entire stack, not only in the latest silicon, but in computer science techniques governing how LLMs work. Understanding this full range of innovation is crucial for staying ahead.
Take KV caching, which is the ability to reuse previous calculations for inferencing instead of recalculating everything at every step. This has the ability to save time, compute power, and money—essential factors for startups. Then there’s the Mixture of Experts technique that allows LLMs to break down complex problems and delegate each piece of the puzzle to the model—and associated infrastructure—best suited to handle it. For example, some tasks might be best served at lower cost if they run through CPUs rather than pricier GPUs. That is true for jobs relying on structured data originating in relational databases and applications, like Oracle Fusion Cloud Applications, that run on them.
When a business’s AI stack is optimized across layers, from data to model to compute, it can gain both efficiency and flexibility. This kind of innovation builds over time, and it starts with choosing infrastructure designed to support it.
4: Data remains core
Applications change all the time, but one thing remains constant: their reliance on large volumes of data, often in varying formats depending on the use case. As AI evolves toward more agentic and autonomous systems, the amount of data generated and consumed is growing exponentially. The more these systems interact, the more important it is to keep data close to where it’s being processed.
In many cases that means placing data as close as possible to required LLMs to minimize round trips that sap network performance. With Oracle Cloud Infrastructure (OCI), storage, compute, and models live side-by-side, helping to remove performance bottlenecks and maximize throughput.
Supporting AI workloads effectively while staying cost-efficient requires using a range of storage types tailored to different stages of the data lifecycle. OCI offers high-performance file systems like File Storage with Lustre for training large models, Object Storage for managing unstructured data, and Archive Storage for long-term retention, all designed to meet the price performance demands of AI-driven architectures.
Oracle brings decades of experience managing mission-critical data into our autonomous data platform. With OCI, startups can:
- Seamlessly integrate advanced LLMs from a variety of providers or create your own
- Parlay insights from Oracle Cloud Fusion Applications to feed their models
- Tap directly into data from Oracle Database without additional integration overhead
Faster, direct access to the data not only enables improved system performance but also lays the foundation for quicker, more informed decision-making.
5: Commoditization continues
The path of technological evolution is clear: hardware horsepower rises as the price for that power is stabilizing. That trend will continue and, as noted before, innovation around the improved technology will carry on as well.
As a startup, take advantage of that commoditization and the flexibility it enables, without losing track of the problem you’re solving. What’s the value proposition, and how can cloud-powered AI help you build on it?
The right infrastructure foundation should support the pace without forcing trade-offs between cost, performance, and simplicity.
6: Be on the lookout for net-new business
Keeping your eye on the prize is critical but it doesn’t mean wearing blinders. AI is not just a tool enabling optimization—it can be a catalyst to discover completely new ideas.
While your business goals and product roadmap are being advanced, it’s important to stay alert to new opportunities that AI might surface. These may be in adjacent areas or perhaps crop up out of the blue in some orthogonal way.
Take a startup helping restaurants optimize staffing schedules. The original product is simple: use past data and forecasts to reduce overstaffing and minimize staff burnout. But over time, its AI models started surfacing that demand surges don’t match historical foot traffic. Digging deeper, the startup realized the spikes correlate with local events, weather shifts, and even social media trends about specific menu items. That insight turns into a new product altogether: a hyperlocal demand prediction API, not just for restaurants but for brands, delivery platforms, or anyone trying to forecast foot traffic and purchasing behavior. That’s not an iteration – that’s a new category creation. And it happened by noticing a signal no one was looking for.
These are the kinds of opportunities AI can unlock, helping to move from cost savings to net-new revenue and long-term business value. That’s the real potential of deploying AI with purpose: not just automation, but meaningful growth.
Speed Alone Isn’t Enough
With so much innovation across infrastructure, models, and applications, it’s easy to get caught up in chasing the newest thing. Startups are inclined to move fast and test boundaries, but sustainable impact comes from knowing where to focus.
The question is not whether to adopt AI, but where to apply it. Every initiative needs to tie back to a clear objective, whether it’s improving efficiency, accelerating customer growth, or creating new revenue. Startups that stay grounded in practical outcomes and align with the right partners are most likely to lead the next wave of innovation.
Success in AI won’t be about who moves first, but who moves with purpose, concentrates on the right problem, and makes sure the infrastructure, data, and models are aligned to solve it.
Build, train, and deploy AI at scale with OCI’s powerful, cost-effective infrastructure. Get the performance, flexibility, and control your startup wants, backed by the same infrastructure trusted by leading enterprises. Learn more here.
