An unexpected bill appears
Conversations usually start the same way. Executives get excited about AI, choose a cutting-edge model, and begin the project. A few months later, they’re over budget, under deadline, and wondering what went wrong.
The model rarely fails. It’s everything around it.
The numbers are sobering. MIT NANDA research – The GenAI divide: The state of AI in business in 2025 We found that 95% of AI pilots have zero measurable returns, despite corporate investments of $30-40 billion. A 2025 survey of more than 1,000 companies by S&P Global found that 42% of companies have abandoned most of their AI efforts, a dramatic increase from the previous year, when just 17% abandoned nearly half of all proofs of concept before going live.
These failure patterns were top of mind when we built SumerBrain. We knew that the real cost of implementing AI was not the API bill, but the infrastructure, data work, customization, human resources, security reviews, iteration cycles, and the opportunity cost of failing the first time. That’s why we designed our platform to address each of these directly before they become problems.
Before your team writes a single line of code or signs a vendor agreement, you should ask the following questions:
Questions Most Teams Ignore
1. What kind of data do we actually have and can we use it?
Frontier models are powerful, but their usefulness is determined by the data you feed them. Most organizations realize midway through a project that their data is siled, inconsistently formatted, or simply not connected in the way AI requires. Informatica’s CDO Insights 2025 study identifies data quality and readiness (43%), lack of technical maturity (43%), and skills shortage (35%) as the top barriers to AI success. Data preparation is a hidden burden on any AI project, and we built SumerBrain to solve it from the ground up.
2. Who owns the infrastructure?
Running AI at production scale is not a plug-and-play exercise. Real-time inference, low-latency pipelines, model versioning, and monitoring require significant infrastructure investments. Are you building it internally? Who maintains it? What happens if it breaks down during a game, during a campaign, or at a critical moment for the customer?
3. Do we have the talent to sustain this?
Building a building is one thing. Maintaining and improving custom AI systems requires specialized teams such as ML engineers, data engineers, and AI product managers, but most organizations don’t have them sitting on the bench. Research shows that 34-53% of organizations with mature AI implementations cite a lack of AI infrastructure skills and talent as a key barrier. Hiring and retaining that talent is its own cost center.
4. How long does it take to see ROI?
General-purpose AI solutions promise rapid time to value. In reality, custom deployments take 6 to 18 months to work reliably at scale. Deloitte’s Q4 2024 Enterprise Survey found that more than two-thirds of organizations expect less than 30% of AI experiments to be scaled within the next three to six months, and less than one-third of generative AI experiments have gone into production. Can your organization absorb that schedule and the recurring costs along the way?
5. What are the security and compliance implications?
Particularly in sports, where proprietary data is a competitive asset, understanding how an AI vendor processes data is non-negotiable. Who has access? Where does it live? What are our contractual protections if something goes wrong? These weren’t an afterthought for us, they were fundamental requirements on which we built SumerBrain from day one.
Build vs. Buy: The Real Calculation
Here’s a quick look at what organizations commonly underestimate when choosing to build in-house.
|
cost category |
Is it often overlooked? |
|
Developing a data pipeline |
✅ |
|
Infrastructure setup and maintenance |
✅ |
|
Fine-tuning and evaluating the model |
✅ |
|
Security and compliance review |
✅ |
|
Continuing ML engineering talent |
✅ |
|
Pre-production iteration cycle |
✅ |
|
Opportunity cost of delayed implementation |
✅ |
An MIT study found that in-house AI builds succeed only 33% of the time, while purchasing solutions that integrate with existing systems have a 67% success rate. The initial cost of a dedicated platform seems expensive until you factor in 18 months of internal build time with no guaranteed output. With a contract platform, you’re doing more than just purchasing software. You’re buying certainty.
Why general purpose AI isn’t enough for the last mile
This is something most vendors won’t tell you about. Frontier models are built for a wide range of applications. They are designed to do many things well in many industries. That is their strength and their limitation.
According to NTT Data, off-the-shelf AI programs have lower adoption rates and efficiency gains than custom-built enterprise tools, primarily due to poor data hygiene, lack of proper AI operations, and infrastructure mismatch. The RAND Corporation estimates that the overall failure rate for AI projects is more than 80%, twice the failure rate for non-AI technology projects.
The last mile of AI deployment is where versatility breaks down. That’s the difference between:
・A model that can answer questions about fan behavior generally Comparison to companies that understand your specific fan, stadium layout, business goals, and conversion definitions
・System that can process game data In theory Comparison to one built to ingest live tracking data and process it in seconds
· AI working in a demo vs. AI working under the pressure of 70,000 people and a 3-hour time frame to drive revenue.
The last mile is not a technical footnote. This is where value is actually created or lost. And that’s exactly what SumerBrain was designed to own.
What is the right platform?
Solving the last mile requires a platform built from the ground up with depth, not breadth. For sports organizations, that means:
Domain-specific data models. It’s built around the rhythm of seasons, game weeks, and live events, rather than typical business workflows.
Real-time infrastructure. Decisions are made in seconds, not in batches. The moment must be captured before it passes.
Configurable business logic. Your goals will change from week to week. AI should also adjust to inventory levels, opponent mixes, fan sentiment, and promotional priorities without requiring an engineering sprint.
Security by design. In an industry where data is the moat of competition, security cannot be improved after the fact. We built it into the SumerBrain architecture from the beginning.
Responsibility for results. We need to be able to accurately measure what the AI did, why it did it, and what it produced. Not just impressions, but actual revenue, retention, and engagement.
What we built for: Pro football from day one.
When we set out to build SumerBrain, we didn’t start with a general-purpose AI platform and adapt it to sports. We started with professional football and designed every architectural decision based on the specific demands that come with it.
Professional sports organizations are leveraging the richest fan datasets in existence, including ticket purchase history, in-stadium behavior, merchandise patterns, and streaming engagement. We knew from the beginning that the hardest part would be the infrastructure to activate that data in real time at the speeds required by live games. So we purposefully built that.
Latency, data integration, security, and configurable business logic were not problems discovered along the way, but requirements that we designed before writing the first line of code. We’ve seen how enterprise AI projects fail. We knew that the gap between a promising prototype and a production-level system was not a technical footnote. Most projects die there. That’s why we planned for the last mile from day one, rather than as an afterthought.
The result is a platform where hard problems have already been solved. We measure it in weeks rather than 18 months to production. Instead of uncertain output, there is a contractual guarantee. We have an architecture built around protecting your most valuable competitive assets, not bolt-on security.
That’s the difference in the platform it’s built on for Pro soccer and everything else.
conclusion
AI brings tremendous value to sports organizations. But the winning team won’t necessarily be the one that invests the most, but it will be the one that invests in the right places.
The true cost of AI is not the model. That’s all you need to make the model work you. Organizations that understand this early on will see their benefits compound by the time everyone else does.
Sumer Sports is building an AI infrastructure specifically for professional football. sumer brain is designed to solve last-mile problems that cannot be solved by common AI solutions.
