10 Tips for Designing AI Applications

1) Discover the customer problem space before considering AI solutions

Start by understanding what customers are actually trying to accomplish and in what situations before deciding how AI might help. These customer problems exist independently of how they are currently solved. Understanding what constitutes a true customer problem versus a solution is fundamental to success, whether you are building AI applications or more traditional software.

Many AI applications fail because they solve the wrong problem or only address symptoms rather than root causes. Research confirms this: a study of 65 experienced AI practitioners found that leadership-driven failures where organisations solve the wrong problem were cited by 84% of interviewees as the primary reason AI projects fail.

For instance, don’t start with “we will build an AI chatbot for customer service.” Instead, research what customer service teams are actually trying to do, and where the existing solutions fall short. Are they spending too much time finding information? Do they find it hard to escalate complex issues appropriately? Understanding these real situations and what’s going wrong helps you determine if AI is even the right solution.

2) Validate customer problems with concrete examples before training models

Use specific, real customer situations to test your understanding of the customer problem you want to solve. Generic problem descriptions lead to generic AI solutions that create little value.

Instead of saying “sales teams need better lead qualification,” document specific scenarios with real examples. “Sarah receives 50 inbound leads weekly. She spends 10 hours manually researching each company’s size, recent funding, and technology stack to determine fit. This research follows the same pattern but varies by industry.” Include 10 representative examples of leads, the source data, and the analysis results. This concrete detail reveals what AI could actually automate versus what might require human insight. Such level of detail in problem definition follows the same principles that drive breakthrough B2B products in any domain.

As RAND Corporation noted in their report, “AI projects often fail when they focus on the technology being employed instead of focusing on solving real problems for their intended end users.”⁵ Don’t skip validating your understanding of the problem – otherwise you sow the seeds of failure already at the very beginning.

3) Identify what humans must decide versus what machines can automate

Once you have discovered and validated problems, identify which decisions require human judgement versus which can be automated and delegated to AI. Separate the aspects that follow repeatable patterns from those requiring value judgements. Do not try to automate decision-making where human judgement is essential. Value judgements cannot and should not be delegated to machines.

In those cases use AI instead to gather and process the data that humans need to make good decisions. The value comes from handling large amounts of information from multiple sources that would be impossible for humans to process manually.

Can be automated: Extracting company information from websites, categorising support tickets by topic, scheduling meetings based on availability.

Probably require human judgement: Deciding whether to offer a discount to save a deal, determining if a customer complaint warrants escalation to management, choosing which product features to prioritise based on strategic goals.

The key difference is that AI excels at pattern recognition and data processing, but humans must make decisions that involve company values, customer relationships, and strategic trade-offs.

4) Evaluate existing solutions to identify improvement opportunities

Assess how well existing (non-AI) solutions solve the customer problems you discovered, which requires analysing two aspects of solutions:

1. The value of outcomes the solutions enable customers to reach in principle.
2. The efficiency of the process required to reach those outcomes.

Look for both to find opportunities to create more value:

A) Improve the outcomes customers are able to reach

The value of any application lies not in its features, but in the outcomes it enables. AI systems can often deliver better results because they can consider far larger data sets than humans can handle.

Example: A procurement team currently evaluates 3-4 vendor proposals manually. AI could analyse 50+ alternatives against your criteria, potentially finding better options they are currently missing.

B) Make the process to reach the outcome more efficient

Automate manual steps and reduce repetitive work needed to reach the outcomes. Ask: Can the software determine the correct next step toward an optimal outcome, or does the human possess knowledge the machine cannot access?

Pay special attention to context amnesia in current solutions. In many applications today, humans must repeatedly provide the same information because the system forgets previous interactions. Often customers are forced to act as their own memory systems. These represent prime opportunities for AI to maintain state and continuity.

Example: Instead of manually entering meeting notes into a CRM, copying action items to task lists, and updating project status, AI could extract and distribute this information automatically, eliminating 15 minutes of administrative work per meeting.

When AI applications excel at both improving outcomes and streamlining the processes, they create strong Problem-Solution Fit that drives sustainable competitive advantage and Product-Market Fit.

5) Design for the complete workflow, not isolated tasks

After identifying where AI can improve outcomes or efficiency, step back to examine how these improvements fit into the customer’s complete workflow. “Too often, trained AI models [...] do not fit into the overall business workflow and context,” RAND noted.⁶

Look beyond individual tasks to understand the broader context and complete sequence of work to achieve a larger outcome, like completing a project that takes weeks or months. Every problem is part of a larger workflow with dependencies and interconnections.

Map what happens before and after your AI solution is used: where does input data come from, and how do AI outputs become inputs to following workflows? Poor integration between automated and manual steps easily destroys the value AI creates.

Your AI should remember previous interactions and build context over time, not start from zero with each session. Focus on problems where accumulated understanding creates significant value.⁷

Example: Email management tools typically treat each email as if they represent isolated tasks, offering at most threaded views of conversations. But people manage ongoing projects, relationships, and commitments across weeks or months. An AI email assistant should understand that when you respond to John’s proposal, the relevant context includes prior meeting notes and emails. If you defer responding to Sally’s long email, you want a reminder before your next meeting with her, not just a generic “follow up” alert.

The best AI products solve the entire customer problem for their target market, not just the parts that AI handles well. Rather than adding another point solution that increases the complexity of the customer’s tool stack, design your AI to replace existing tools and simplify the overall end-to-end solution.

6) Start with the simplest approach that solves the problem

Resist the temptation to use advanced AI techniques when simpler approaches deliver better customer outcomes with less complexity. “In some cases, leaders understand AI only as a buzz-word and do not realize that simpler and cheaper solutions are available”, RAND noted.⁸ Don’t let the availability of sophisticated tools drive your solution design. Remember, for a man with a hammer every problem looks like a nail.

For example, don’t build a complex machine learning model to categorise support tickets if a simple keyword matching system achieves 90% accuracy. Start with rule-based logic, and add AI complexity only where it demonstrably improves results. Patterns in a dataset with a handful of dominant characteristics can be quickly captured by a few simple if-then rules.

Ask whether increasing accuracy from 90% to 95% justifies the additional complexity and development effort. It might, if the rule-based logic is complex and difficult to maintain, and you are developing a scalable product so that a large customer base reaps benefits from the increased accuracy and easier maintenance.

Often, the biggest wins come from automating data collection and formatting, not from sophisticated algorithms.

7) Invest in high-quality, domain-specific training data

When training your own models or creating prompts, invest heavily in data preparation and curation. Poor data quality is the second most common reason machine learning applications fail to deliver expected results.⁹ LLM-based applications also need high-quality examples in their prompts to produce good results.

If you are building a sales AI, for instance, don’t train on all deals equally. Focus on high-quality examples: successful deals where the sales process was well-documented, outcomes were clearly positive, and deal characteristics represent your target market. 100 excellent examples outperform 1,000 mediocre ones.

8) Build user trust through transparency and control

Strive for transparency in AI outputs, even though complete explainability can be technically challenging. Focus on providing users with actionable context about AI suggestions and control over important outcomes. Transparency is essential for building trust.^{8 9}

Show confidence levels: “85% confident this lead qualifies based on company size and industry match, similar to 47 previously successful conversions.”

Provide context for reasoning: “Categorised as urgent because it mentions ‘deadline’ and ‘CEO approval required’, similar to 23 previous escalations.”

Enable easy corrections: Build correction mechanisms directly into your interface. If AI miscategorises a document, let users fix it with one click, not through a separate feedback form.

Give users ways to adjust AI autonomy. Some users prefer confirmation for every action especially in the beginning, while others are quick to let AI act independently on routine tasks. Design controls that let users set their comfort level.

9) Build feedback loops that strengthen trust over time

Capture user corrections and interaction patterns to continuously improve AI performance. The goal is learning from every interaction, not just periodic retraining cycles.

Track correction patterns to improve future decisions. If users consistently override AI classifications about “urgent” emails from certain domains, automatically adjust those classifications. When users approve AI suggestions without changes, increase confidence in similar future scenarios.

As your AI proves reliable through transparent operation and continuous improvement, users will naturally delegate more responsibility. Monitor this progression. Successful AI applications become more autonomous over time as they earn user trust through consistent performance.

10) Measure and validate business impact before and after implementation

Define and quantify the expected business value of your AI solution before development begins, then track actual results after deployment. This validation approach applies whether you are building AI features or validating entirely new product opportunities. Many teams focus on technical metrics like accuracy and speed while ignoring the business outcomes that matter to stakeholders.

Before implementation: Estimate the specific value your AI will create. If you are automating lead qualification, don’t just estimate “faster processing.” Quantify: “Sarah currently spends about 10 hours weekly researching 50 leads. Since about 80-90% of her actions are repetitive, AI automation should be able to reduce this to 2 hours, freeing 8 hours for actual sales activities. At her €150k sales quota, this represents €40k additional revenue potential annually.”

Define business success metrics beyond technical performance:

• Technical metrics: Classification accuracy (target 95%), response time (target 5 seconds)
• Business metrics: Reduction in time-to-qualification (target 80%), increase in qualified leads per rep (target 50%), improvement in conversion rates (target 15%)

After deployment: Track both technical performance and business outcomes. AI that achieves 98% accuracy but doesn’t improve business results has failed despite technical success. Conversely, AI with 85% accuracy that doubles productivity has succeeded despite imperfect technical performance.

Example: A customer service AI might have only 85% accuracy in ticket routing, but if it reduces average resolution time from 4 hours to 1 hour and increases customer satisfaction scores by 20%, the business impact far outweighs the technical limitations.

This measurement discipline ensures you are building AI that creates real value, not just impressive demonstrations. It also provides concrete data to justify continued investment and guide future improvements.