As AI rises in popularity and gains additional use cases, business leaders are faced with a critical question: 

How can we adopt and utilize AI effectively within our business? 

Across industries, while the specific solutions may vary, the foundation of any AI implementation should center on creating the greatest impact for humans. At The Bridge (now North Highland), our experience design team focuses on ensuring that design is centered on the human experience and generates tangible value. Similarly, AI tools must augment and enrich user experiences. In The Experience Economy, written by B. Joseph Pine II and James H. Gilmore, the authors highlight a fundamental shift in economic value—businesses are moving from simply offering products and services to staging experiences that are personal, engaging, and memorable. 

AI, as a powerful tool for enhancing user experiences, should align with this paradigm. By creating meaningful and impactful interactions, businesses can ensure AI generates tangible value for users, fostering repeat engagement and long-term loyalty. 

When developing a strategy for integrating AI into existing goods or services, it is essential to address three key pillars: AI, Experience Design, and the Human Aspect. By emphasizing these elements, companies can craft unique and impactful experiences where AI meaningfully enriches user interactions. 

1. AI (Technical Foundation)

• Focuses on the technical components required to develop, implement, and support AI models. 
• Ensures models provide accurate, reliable, and contextually appropriate responses that meet user expectations. 
• Prioritizes continuous learning and improvement to adapt to evolving user needs. 

2. Experience Design (Human-Centered Approach) 

• Centers on crafting an experience that is accessible, intuitive, and usable for diverse user personas. 
• Leverages user research and persona development to design tailored interfaces and workflows. 
• Incorporates user feedback to iterate and refine the experience over time, ensuring alignment with user preferences and behaviors. 

3. Human Aspect (Trust and Approachability) 

• Emphasizes creating outputs and user experiences that are approachable, relatable, and culturally sensitive. 
• Builds trust by ensuring transparency in how AI models operate and generate outputs. 
• Focuses on driving user engagement and retention by aligning AI-driven interactions with human values, emotions, and expectations.

Why This Approach Matters

In an era where users are bombarded with choices, the quality of the experience is often the differentiator between a product that succeeds and one that fails. AI-driven experiences must strike a balance between technical excellence, thoughtful design, and human-centered outputs. Companies that prioritize all three pillars will be better positioned to deliver not only functional solutions but also transformative experiences that resonate deeply with users. 

By adopting this approach, businesses can harness the full potential of AI—not as a standalone technology, but as an integrated element that elevates the value and impact of their offerings. 

What Are the Challenges of Building an AI Tool? 

When embarking on an AI initiative, companies must navigate a range of technical and non-technical challenges. Understanding these barriers is critical to ensuring the success of the project and maximizing its potential impact. Below are some key challenges to consider: 

User Adoption

One of the most overlooked yet critical challenges in developing AI tools is ensuring user adoption. According to Forbes, more than 70% of projects fail due to insufficient user adoption, highlighting the importance of prioritizing this aspect from the outset. 

Key factors influencing user adoption: 

• Awareness: Users need to be informed about the tool’s purpose, capabilities, and benefits. Without clear communication, potential users may ignore or underutilize the tool. 
• Ease of Use: Tools must be intuitive and accessible, with user interfaces designed around the specific needs and skills of the target audience. 
• Training and Support: Providing comprehensive training, documentation, and ongoing support can help users unlock the full potential of the tool, driving engagement and retention. 

A focus on user adoption not only ensures project success but also enhances the return on investment by maximizing the tool’s utilization. 

Data Availability and Access

AI tools rely heavily on data to train and test models, enabling them to deliver accurate and expected outcomes. Data also serves as the foundation for continuous learning, allowing AI systems to refine their accuracy and improve efficiency over time. 

Key considerations include: 

• Data Quality and Accuracy: Poor-quality data can introduce bias, inaccuracies, and errors that propagate through the model. These issues may lead to suboptimal outcomes or ethical concerns. Also, formatting, acquisition, and translation of the data especially should be highlighted given that the data format heavily impacts how performant the AI model will be – more so than other data driven intersections. 
• Historical Data Influence: Like humans, AI models use past experiences (in this case, historical data) to inform future decision-making. When the input data is flawed, the model may develop skewed patterns or “hallucinations,” producing unreliable or inappropriate results. 

To overcome these challenges, organizations must prioritize robust data collection, cleaning, and validation processes. Leveraging diverse and representative datasets can also reduce the risk of bias and improve model performance. 

 Data Privacy and Security 

The importance of data privacy and security cannot be overstated, especially in highly regulated industries such as finance, healthcare, and insurance as an example. AI tools process sensitive user information, making them a target for breaches and misuse if not properly secured. User data also must be protected from other users as well to ensure, for example, that specific financial position data is not utilized by the model if that is non-public data. 

Key strategies for addressing privacy and security challenges include: 

• Data Anonymization: Removing personally identifiable information (PII) to protect user identities while allowing models to learn from the data. 
• Multitenancy and Encryption: Implementing secure architecture designs to ensure that user data is isolated, encrypted, and only accessible to authorized systems. 
• Compliance: Adhering to industry-specific regulations like GDPR, HIPAA, or CCPA to safeguard user trust and avoid legal repercussions. 

Balancing data utility with privacy protection is essential for building trustworthy AI tools that users feel confident engaging with. 

Model Performance

AI models are not static; they require continuous monitoring and refinement to maintain performance over time. Several performance-related challenges can arise, including: 

• Hallucinations: When models generate outputs that are factually incorrect or nonsensical due to flawed training data or poor reasoning. 
• Bias: AI systems may inherit biases present in the training data, leading to unfair or discriminatory outcomes. 
• Model Drift: Over time, the model’s predictions may become less accurate as real-world data and conditions evolve away from the training data. 

Mitigating these issues requires: 

• Rigorous testing during development to identify potential problems. 
• Continuous monitoring and retraining using fresh, high-quality data. 
• Implementing feedback loops to detect and correct errors or biases in real-time. 

Getting Started with AI

When beginning the process of building an AI tool, we start by aligning leadership on key goals and outcomes for the brand. It is vital to outline this prior to creating the user experience as it aids in focusing on crafting an experience that delivers value to consumers.  

As the user experience is created, a valuable technique for refining this experience is through  prototyping, which ensures an experience undergoes continuous refinement while gaining real-world exposure to customer interactions. By deploying early versions of the tool in controlled settings, businesses can gather user feedback, address issues, and enhance the tool’s performance to better meet user needs. 

To learn more about crafting value centered AI experiences, get in touch with The Bridge.