From: aidotengineer
Many teams initially approach AI strategy by seeking “bleeding edge models” and complex solutions, often leading to disappointment and high costs for in-house needs [00:00:01]. While such advanced systems might be suitable for large-scale external applications, they are often “overkill” for internal company requirements [00:01:46]. A more effective approach, particularly for internal tools, involves prioritizing user needs, focusing on specific problems, and establishing robust feedback loops.
Build vs. Buy: The User-Centric Decision
The decision to build an AI solution in-house versus buying a Software-as-a-Service (SaaS) product should be guided by user-centric considerations [00:01:50].
- When to Buy: SaaS is beneficial for exploring unknown territories or when requiring vendor integrations and cross-industry best practices [00:02:16].
- When to Build In-House: Building an internal solution is advantageous when:
- The organization owns the data [00:02:28].
- Colleagues possess precise knowledge of workflows needed to achieve goals [00:02:32].
- Users can be directly involved in double-checking the outputs [00:02:36].
- Proximity to users allows for rapid adjustments and same-day tweaks [00:02:42].
- The user interface (UI) can be tailored to speak the users’ specific language [00:02:50].
- The solution can run on existing infrastructure, significantly reducing costs [00:02:53].
The general rule of thumb is to “buy to explore the unknown but build in-house once the workflow is yours” [00:03:02].
Five Lessons for User-Centric AI Development
To successfully implement user-centric AI solutions that drive revenue, consider these five lessons:
1. Focus on One Painful Job to Be Done
Instead of pursuing a broad, comprehensive solution, concentrate on a single, “painful job to be done” that has a clear, quantifiable “dollar-based outcome” [00:03:33]. This deep dive into a specific use case makes the development process simpler and more manageable [00:03:59].
- User Input is Key: Engage directly with users to understand their needs and challenges fully. “Talk to them to really nail it” [00:04:19].
- Keep it Simple: Staying focused on a narrow use case allows for simplicity and helps avoid complex, agentic systems that may be unnecessary for internal tools [00:04:25].
2. Revenue Impact Trumps Evaluation Metrics
While offline evaluations serve as “smoke alarms,” the ultimate measure of success is the actual impact on revenue [00:04:40].
- Track Everything: Instrument every part of the system to clearly link AI tasks to specific dollar outcomes, building a “revenue funnel” from start to the final value event [00:04:59].
- Users as Guardrails: Users can act as “guard rails,” allowing for ambitious experiments without overthinking evaluations [00:05:21].
- Align with Business Goals: Tying the system to financial impact simplifies decision-making and prioritization, shifting conversations to potential sales and performance [00:05:31].
- Automate Reporting: Automate team performance reports and create leaderboards to foster healthy competition, gain leadership investment, identify champions, and support struggling users [00:05:52].
3. Push Insights Proactively
Don’t wait for users to ask for information or insights; anticipate their needs and deliver them proactively [00:06:11]. The “best UI is the one you never need to use” [00:06:25].
- Anticipate Needs: As the business owner, you know the next logical steps for users. Proactively provide them, such as daily digests with essential information [00:06:28].
- UI as Fallback: While proactive pushes are primary, a traditional UI can serve as a fallback for unexpected or unplanned tasks [00:06:45].
4. Guide Action, Don’t Just Deliver Information
Simply saving time for users is not enough; the AI system must guide them toward high-value activities [00:07:09]. If saved time is filled with unproductive tasks, the AI’s value diminishes [00:07:14].
- Convert Time Saved into Time Well Spent: Understand the highest value activities and divert users’ newly freed time and attention towards them [00:07:26].
- Proactive Systems Drive Engagement: A proactive system that surfaces activities users wouldn’t have considered can lead to significantly higher user satisfaction (NPS) and engagement compared to traditional chat applications [00:07:43].
5. Invest in the Basics (Good Data Beats Great Models)
When allocating development resources, prioritize good data and foundational improvements over chasing the latest, most expensive models [00:08:02].
- Data Quality is Paramount: “Good data consistently beats great models” [00:08:11]. More advanced models often come with significantly higher costs and slower performance without a proportional increase in value for internal use cases [00:08:22].
- Focus on User Needs, Not Benchmarks: The greatest impact often comes from simple improvements like adding more triggers to alert users or deepening the understanding of their specific needs [00:08:34]. Changes in models might only affect costs and evaluations, not user value [00:08:45]. Build for what users need, not what you want to try [00:08:58].
The Revenue Flywheel: A Powerful Feedback Loop
When development focuses on true user value rather than just model benchmarks, a powerful “flywheel” effect begins [00:09:09].
- Tight Feedback Loops: Users feel heard, leading them to provide more ideas for improvements [00:09:17].
- Iterative Improvement: This feedback enables rapid experiments, driving more adoption [00:09:27].
- Data Generation: Increased adoption generates more data for prioritization and further ideas [00:09:32].
- Accelerated Growth: This continuous cycle causes the “revenue flywheel” to spin faster, leading to sustained improvement and financial impact [00:09:38].
In summary, start small with a clear dollar value, track revenue impact over traditional metrics, proactively guide users, ensure time savings translate to high-value activities, and invest in fundamental data improvements rather than just complex models [00:09:52]. Let your users be the guide for effective AI development [00:10:38].