De-risking your product initiatives: Why every intelligent app must start with a PoC

As we settle into using AI, many executives are eyeing ambitious initiatives to leverage new technologies and drive innovation. Among these, intelligent apps powered by AI stand out for their transformative potential. But they come with increased risks. Budgets can balloon, timelines can extend, and success may feel elusive.

How can you ensure these high-risk, high-reward initiatives deliver measurable value?

Starting with a proof of concept (PoC) is a strategic approach that transforms uncertainty into opportunity. This method allows you to validate your intelligent app’s potential while minimizing risks and accessing funding from partners like AWS, especially when leveraging their AI services.

What is an intelligent app?

Intelligent applications harness AI to deliver personalized, adaptive, and data-driven user experiences that surpass traditional functionalities. By using real-time and historical data to make decisions, predict user needs, and offer tailored solutions, these apps unlock tremendous potential for businesses and users, driving innovation and competitive advantage.

However, realizing this upside can come with significant challenges. Gartner estimates that at least 30% of generative AI projects will fail after PoC due to poor data quality, inadequate risk controls, escalating costs, or unclear business value.

Despite these hurdles, such setbacks highlight the critical role of PoCs in enabling teams to "fail fast," refine strategies, and ultimately achieve success with intelligent, AI-powered solutions.

Why PoCs are essential for AI-driven intelligent apps

Intelligent apps that incorporate AI components, such as predictive analytics, natural language processing, or generative AI, have enormous potential to drive innovation.

One example of an organization we worked with is Drexel University. Their research team wanted to develop an app to test its hypothesis about eating disorder treatment, and our developers utilized machine learning framework, third-party API integrations, and a modern tech stack (Rails, React Native, React) to create a clinical-trial ready app in eight months. The app integrates with the patients’ consumer glucose monitor and provides guidance without being invasive, and has resulted in more accurate clinical trials.

While the benefits of intelligent apps are significant, their experimental nature introduces significant project risks.

Starting with a PoC is a strategic approach that transforms uncertainty into opportunity. This method allows you to validate your intelligent app’s potential while minimizing risks and accessing funding from partners like AWS, especially when leveraging their AI services.

Key challenges in developing intelligent apps:

• Higher complexity: AI solutions require advanced models, vast datasets, and seamless integrations, often introducing new layers of intricacy.
• Uncertain outcomes: Without real-world validation, predicting an AI system’s performance or business impact can be challenging.
• Greater resource demands: Developing, training, and scaling AI systems can stretch budgets and overwhelm teams.

A well-planned PoC helps mitigate these risks by enabling your organization to test, validate, and refine AI initiatives before full-scale deployment.

Here’s why PoCs are critical for intelligent app projects:

1. Validate AI models and assumptions

AI projects thrive on high-quality data and finely tuned models. A PoC enables you to test these components in a controlled environment, ensuring your AI delivers actionable, accurate results before committing to full-scale deployment. It helps surface gaps in data or algorithms early, saving significant time and cost later.

2. Minimize financial and operational risk

AI initiatives often come with substantial upfront investments in tools, infrastructure, and talent. A PoC allows you to validate the concept without committing your full budget, reducing the financial and operational risks associated with unproven solutions. This approach ensures you’re only scaling what works.

3. Build confidence among stakeholder

AI projects can seem daunting due to their complexity and the uncertainty of returns. A well-executed PoC provides stakeholders with tangible proof of value, alleviating concerns and paving the way for broader organizational support. It bridges the gap between conceptual promise and demonstrated impact.

4. Refine your intelligent app vision

AI-driven initiatives often uncover unexpected challenges or opportunities. A PoC provides a structured space to iterate and refine your intelligent app strategy. This ensures the final solution aligns with user expectations, technical feasibility, and business objectives, avoiding costly misalignment down the road.

Leverage partner funding for AI initiatives

For executives focused on AI-driven projects, platforms like AWS offer funding programs designed to encourage exploration of their advanced AI services, such as Amazon SageMaker, Amazon Comprehend, and Amazon Bedrock.

Securing partner funding for your AI PoC offers several benefits:

• Lower costs: Funding programs from platforms like AWS subsidize infrastructure and development costs, allowing you to execute a robust PoC without straining your budget
• Access to top-tier expertise: Gain access to technical guidance and resources directly from the platform provider. AWS, for example, offers hands-on support to optimize your AI models and integrations.
• Faster time to value: Pre-built AI services like AWS SageMaker and Comprehend accelerate development timelines, enabling your team to iterate quickly and achieve tangible PoC results in less time than building from scratch.

Best practices for intelligent app PoCs

An AI PoC is a chance to prove real-world impact while uncovering insights that shape your long-term strategy.

Here are five tips that will help you create a winning PoC:

1. Define success metrics

Establish measurable goals, such as model accuracy, user adoption rates, or projected ROI, to objectively evaluate your PoC’s effectiveness and business impact.

2. Start with a focused use case

Identify a specific, high-impact problem your intelligent app will solve. This ensures the PoC scope remains manageable while demonstrating clear value. A targeted PoC demonstrates immediate value while laying the groundwork for scaling AI solutions.

Identify a specific, high-impact problem your intelligent app will solve. This ensures the PoC scope remains manageable while demonstrating clear value.

3. Assemble a multidisciplinary team

Collaborate with experts across product management, data science, engineering, and business strategy to address technical challenges, align goals, and ensure stakeholder buy-in.

4. Prioritize agile iteration

Use iterative development cycles to prototype, test, and refine quickly. Early adjustments based on real-world feedback increase the likelihood of delivering a viable solution.

5. Leverage pre-built AI services

AWS provides advanced tools such as SageMaker and Comprehend to jumpstart development, reduce complexity, and accelerate time-to-value.

The path to de-risking your AI initiative

Intelligent apps with AI components offer unparalleled potential to transform your business, but their complexity demands a thoughtful approach. Starting with a PoC enables you to test your vision, mitigate risks, and unlock valuable partner funding to support your journey.