Blog
Many enterprise AI projects start with the wrong question: “How do we use AI?” That’s backward. The right question is: “What business problem can we now solve that we couldn’t 18 months ago?”
AI is still software. Most companies don’t build their own infrastructure—and they shouldn’t start now. The goal isn’t to adopt AI for its own sake. It’s to apply it where it creates real leverage, delivers measurable value, and aligns with your product mission.
Successful AI doesn't just solve important problems—it solves problems that are possible to solve with available data, tooling, and time. Teams that get this wrong often overestimate what their AI can deliver and underestimate what it takes to deliver it.
Developing in-house AI solutions may seem appealing but often proves prohibitively costly and distracting, and takes longer than needed (and estimated) to deliver value. Most companies should focus on what they do best—and adopt AI tools that are built and maintained by specialists. Buy the system that’s already solved your problem, not the kit to build it from scratch.
Initial excitement around AI often fades quickly as projects fail to deliver meaningful results. According to McKinsey, successful AI initiatives align closely with specific business objectives such as optimizing supply chains, enhancing customer experiences, or automating data analysis. Without clear goals, companies frequently abandon AI prematurely. S&P Global found that 42% of enterprises discontinue most AI projects before reaching production, resulting in substantial missed opportunities and financial losses.
Consider PepsiCo, which successfully adopted a third-party AI application to optimize inventory management. This approach reduced waste and saved millions. In contrast, Klarna developed proprietary AI tools for credit decisioning but underestimated risk. That misstep led to tightened lending practices and financial losses. The difference wasn’t simply a question of building versus buying. What mattered was whether the AI was aligned to a problem that was both possible to solve and important to solve—and whether it was executed reliably.
Companies often initiate AI projects due to external pressures rather than genuine business needs. Vikram Sreekanti, CEO of RunLLM, notes, "If your AI project starts with ‘our board asked us to look at this,’ it’s already off track." Effective AI implementation starts by clearly defining urgent, specific business problems.
Having engaged with numerous organizations exploring AI solutions like RunLLM’s AI Support Engineer, a clear pattern emerges: those who capture real value from AI differ distinctly from those embarking on superficial explorations. In an era where FOMO is strong due to AI's pervasive disruption, a disciplined focus on your company's specific objectives and problem-solving priorities becomes more critical than ever.
Without this clarity, teams chase ambiguous goals, apply AI to the wrong parts of the business, and often end up with tools that confuse more than they help. As Vikram explains, "If your sales team struggles with lead qualification, operations faces supply chain disruptions, or finance is overwhelmed by manual reconciliation, you don’t wonder whether something might help—you know you need help." These companies don’t dabble in AI—they deploy it with intent, focused on specific problems that are high-value and feasible to solve. That clarity creates momentum and measurable ROI.
Moving AI from prototype to production involves more than making an API call to OpenAI. It demands careful consideration of workflows, data quality, integration, and user trust. According to Accenture, successful AI adoption requires a holistic approach encompassing technology, people, and processes.
Chenggang Wu, CTO of RunLLM, puts it simply: "If your AI is making decisions in real workflows, it needs to be observable, interruptible, and able to escalate when it’s unsure. That’s what productionization really means."
Deploying AI at scale means consistently ensuring quality and reliability. Many organizations encounter AI solutions that promise much but deliver little, often due to inaccurate or unreliable outputs. Shirshanka Das, co-founder and CTO of DataHub, succinctly highlights this challenge: "AI products are easy to build but hard to productionize. Chief problems include verifying data accuracy and continuously monitoring AI quality in production."
High-stakes, interactive applications in enterprise environments require visibility and measurement to maintain trust. This includes robust logging, clear escalation paths, reliable fallback behavior, and continuous feedback mechanisms—especially when data is fragmented across systems like CRMs, ERPs, internal wikis, and communication logs. For example, a production-grade AI assistant designed to support enterprise customers must not only deliver technically correct answers, but also cite trusted sources, flag low-confidence responses, and escalate when uncertain. These safeguards help teams catch issues before they escalate and give users confidence that the system is reliable even when edge cases arise.
Enterprise AI doesn’t fail because the models are bad. It fails because companies chase trends, misjudge the problem, or can’t get systems into production.
The teams that win treat AI like software. They solve valuable problems, ship quickly, and improve fast.
This isn’t about being first to launch. It’s about being first to get it right—and first to scale what works.
© RunLLM, Inc. 2025
.avif)