From: aidotengineer
Many teams approach their AI strategy similarly to ordering takeout: selecting something that appears good online, paying a premium, and later discovering it falls short of expectations [00:00:01]. This often stems from overly high expectations driven by discussions on platforms like LinkedIn or Twitter, which showcase “bleeding edge models” [00:00:17]. While these might suit large-scale consumer applications, for internal company use, they can be likened to “paying for truffles to garnish your instant noodles” [00:00:32], representing an unnecessary expense.
A different approach, which delivered millions of dollars in revenue, focuses on a more practical “recipe” for AI implementation [00:00:42].
Build vs. Buy Dilemma
In Q1 2024, a team faced the classic “build or buy” decision for their AI solution [00:00:54]. They opted to build in-house, utilizing two developers and approximately 10 sprint weeks of effort [00:01:05]. This resulted in a system that generated several million dollars in Annual Recurring Revenue (ARR) [00:01:10].
Contrastingly, strategies often seen on social media, involving “giant evals, multi-agent systems, RF models,” while appearing appealing, can “cost a fortune” and significantly delay launch [00:01:30]. These advanced solutions are often “overkill for your in-house needs” [00:01:46].
The rule of thumb for cost-effective AI implementation is: “buy SaaS to explore the unknown but build in-house once the workflow is yours” [00:03:02].
Advantages of In-House Development for Cost Efficiency
Building AI solutions in-house offers significant cost advantages, particularly for internal applications:
- Data Ownership and Proximity: When a company already owns the necessary data, developing in-house leverages this asset effectively [00:02:28].
- User Involvement: Direct collaboration with internal users who understand the precise workflows allows for double-checking outputs and ensuring relevance [00:02:32].
- Rapid Iteration: Proximity to users enables same-day tweaks and adjustments, enhancing agility [00:02:42].
- Leveraging Existing Infrastructure: Running AI solutions on infrastructure already paid for significantly reduces additional expenses [00:02:53].
These factors collectively “drop the cost to pennies” [00:02:57].
Key Lessons for Cost-Effective AI Implementation
The speaker shares five key lessons for successful and cost-effective AI implementation:
1. Focus on a Single Painful Job with Clear Value
Instead of attempting a comprehensive solution that tries to “boil the ocean” [00:09:59], concentrate on one specific, painful task [00:03:33]. This allows for absurdly deep dives into a problem without chasing a total addressable market [00:03:36]. Identify a “value event”—a clear dollar-based outcome—that the AI solution will drive [00:03:42]. By going deep on a single use case, such as a simple sales alert, the process becomes much simpler, avoiding overly “agentic” designs [00:03:59].
2. Prioritize Revenue Impact Over Evaluation Metrics
While offline evaluations are important as “smoke alarms,” they should not be the primary metric for success [00:04:36]. Boards ask about revenue, not F1 scores or NDCG [00:04:40]. Therefore, instrument everything to track the actual financial impact, linking AI tasks directly to revenue generation [00:04:59]. Building a revenue funnel from beginning to end, culminating in the defined value event, is crucial [00:05:07]. When a system is linked to dollars, decisions and prioritization become straightforward [00:05:31]. This also enables the automation of team performance reports and leaderboards, fostering healthy competition and identifying champions or those needing support [00:05:52].
3. Push Insights Proactively
Don’t wait for users to ask for information; become the “chef who anticipates what the next dish should be” [00:06:14]. The most effective UI is often one that users “never need to use” [00:06:25]. Proactive systems, like daily digests of key information, can be highly effective, with traditional UIs serving as fallback for unexpected tasks [00:06:37].
4. Guide Action, Don’t Just Deliver Information
Simply saving time is insufficient if users fill that time with unproductive activities [00:07:14]. The true power of AI comes from converting saved time into “time well spent” on the highest-value activities [00:07:24]. As revenue funnels are built, it becomes clearer where to divert users’ freed-up time and attention [00:07:34]. Proactive systems that surface actions users wouldn’t have considered can lead to significantly higher engagement and Net Promoter Scores (NPS) compared to chat applications [00:07:43].
5. Invest in Good Data Over Great Models
This is a critical decision point for resource allocation. “Good data consistently beats great models” [00:08:11]. While advanced models like GPT-3 might be 60 times more expensive and an order of magnitude slower than alternatives like GPT-4 mini, their biggest impact in production is often on cost [00:08:22]. The best results often come from simply adding more triggers to alert users and understanding their needs more deeply [00:08:34]. Changes in models have primarily affected costs and evaluations, not necessarily user value [00:08:45]. Building for user needs rather than chasing model benchmarks leads to a powerful flywheel: tight feedback loops make users feel heard, leading to more ideas for improvements, which drives more adoption, generating more data for prioritization, and more ideas, accelerating the revenue flywheel [00:09:03].
In summary, for effective cost management in AI projects, teams should start small, follow the money, and let their users guide development [00:10:38].