Innovations span across multiple aspects of business. They can be product improvements, operational changes, marketing methods or sales strategies. These changes are well thought out, taking into account the best understanding of customer expectations that the company has. However, the success of such an innovation is not always guaranteed, due to one or more of the following factors:

Small or unbalanced sample sizes in surveys

Surveys with limited responses can lead to inaccurate conclusions that don’t reflect the broader customer base. For example, drawing insights from just 50–100 participants may not be enough to represent diverse customer preferences.

Poor segmentation in customer feedback

Without proper segmentation, even large samples may misrepresent key customer groups. Behavior-based segmentation often provides deeper insights than basic demographics, ensuring more meaningful feedback.

External factors affecting A/B testing outcomes

Real-world variables like weather or major events can distort test results, masking the actual impact of innovations. Identifying control groups that share similar external conditions with test groups is critical for accurate comparisons.

Insufficient time to collect enough data before an opportunity passes

In fast-moving domains like online advertising, waiting to collect enough data may result in missed market windows. Quick, data-driven decisions are essential when timing is critical and opportunities are fleeting.

Long development cycles due to labor-intensive testing processes

Thorough testing, especially in software or operations, is time-consuming and can delay product rollouts. Streamlining validation processes without compromising reliability is key to maintaining innovation momentum.

This is where Artificial Intelligence (AI) plays a transformative role— improving various aspects of testing, preventing bias and yielding rigorous measurements. In an increasingly dynamic marketplace, testing without bias is critical. AI equips organizations to test smarter, faster, and more accurately by optimizing everything from sampling to test case selection.

We will now take a look at how AI enables a far more accurate and smarter testing process.

AI in Consumer Behavior Testing

Customer research forms the foundation of many business decisions. However, traditional testing methods often fall short due to sampling bias or operational inefficiencies. AI can address these challenges across various survey and testing formats.

Representative Test Groups with AI

A major challenge in consumer behavior testing is selecting a group that reflects the broader customer base. In-person surveys, often conducted at specific times and locations, can introduce selection bias—for instance, surveying customers at 10 a.m. on weekdays may exclude working professionals. This skews results and limits the value of insights.

To overcome this, businesses can leverage AI to enhance the selection process. By analyzing purchase history, customer profiles, and behavioral patterns, AI models can predict when and where the most representative cross-section of customers is likely to shop. This allows for more strategic deployment of survey efforts—ensuring the data collected is balanced, diverse, and better aligned with the broader market. In doing so, businesses can significantly improve the reliability and relevance of insights derived from customer feedback.

Enhancing Survey Efficiency with AI

Online and mail-in surveys are often preferred over in-person methods for their broader reach. Online surveys are especially cost-effective and easy to scale. Mail-in surveys, however, are more expensive and typically suffer from low response rates—even with incentives like discount coupons. To meet response targets, companies often send mailers to three to five times the desired sample size, driving up costs and reducing efficiency.

AI can play a critical role in optimizing this process. By analyzing historical purchase data and customer behavior patterns, AI models can predict the likelihood of an individual responding to a survey. This allows companies to intelligently prioritize and target those customers who are most likely to engage, thereby reducing the number of mailers needed. Not only does this improve cost efficiency, but it also enhances the quality and representativeness of the feedback collected.

Improved In-Store Testing Accuracy

Evaluating the effectiveness of in-store promotions and price discounts requires careful analysis of customer response at the store level—a task that comes with several complexities. Often, detailed customer profiles and demographic data at individual store locations are not readily available, making it difficult to attribute results to specific customer behaviors. In addition, external variables such as local events, weather conditions, or holidays can significantly influence store traffic and sales, potentially distorting the true impact of the promotion being tested.

To address this, businesses often rely on A/B testing methodologies, where a test store is compared against a carefully selected control store. The control store is chosen to match the test store as closely as possible in terms of customer demographics, purchasing patterns, and external conditions during the test period. This approach helps isolate the effect of the promotion itself by minimizing the influence of outside factors, thereby enabling more accurate measurement and more confident decision-making.

Optimizing Digital Advertising with AI-Driven Testing

Digital advertising operates in a fast-paced environment where timing is critical and Click Through Rates (CTR) are typically low. Advertisers often test multiple messaging variations to identify the most effective one, especially when promoting time-sensitive events. However, in digital channels like search and shopping ads, the audience profile is often unknown in advance, making it difficult to target specific customer segments.

Traditional A/B testing methods, which require waiting for statistically significant sample sizes for each variant, can be too slow for these dynamic campaigns. This is where AI-driven approaches like Multi-Armed Bandits (MAB) offer a powerful advantage. MAB algorithms continuously test and evaluate multiple ad variants in real time, dynamically allocating more impressions to higher-performing options as new data comes in. This adaptive approach not only accelerates decision-making but also boosts overall campaign performance by maximizing exposure to the best-performing ads.

By learning from each customer interaction, MAB methods refine their estimates and quickly converge on the most effective message, ensuring that time-sensitive opportunities are not lost to long testing cycles. The result is a more efficient use of ad spend and higher engagement across the campaign period.

The Evolving Landscape of Product Testing and the Role of AI

Product testing varies significantly across industries, shaped by the nature of the product and the complexity of its development process. For commercial goods such as consumer packaged goods (CPG) and white goods, testing is typically carried out in controlled lab environments before mass production. These tests are well-defined, limited in scope, and usually completed in a relatively short time frame. The parameters for assessing product quality are clear, and the environment is stable, making the testing process straightforward.

However, in domains like electronic chip design and software development, testing becomes far more complex and continuous. These products are created using digital tools and object-oriented programming methods that enable distributed development across multiple teams. As these teams build different components, the overall product becomes too intricate for simple validation methods. Specialized testing teams are brought in to verify that each iteration of the design behaves as expected. To ensure objectivity, these teams often treat the design as a “black box,” testing outputs without access to the underlying code.

In these high-tech environments, testing revolves around two critical metrics:

• Line coverage, which verifies that every line of executable code is tested. Any line not covered may harbor undetected bugs.
• Functional coverage, which checks if the system performs the required tasks correctly. For instance, a calculator must handle operations like addition or division—including edge cases like division by zero—with consistent accuracy.

Testing is especially rigorous in chip design because any bug post-production is extremely costly to fix—requiring hardware recalls or redesigns. In contrast, software bugs, while still serious, can typically be resolved through patches and updates.

Due to this complexity, test cycles are time-consuming. Developers often build on legacy code, leaving unused or redundant segments intact. Testing teams, using high-level design descriptions and prior experience, create large test suites to ensure comprehensive coverage. This makes each test cycle lengthy, and iterative development methods—where design and testing happen in multiple rounds—further add to the total time investment.

AI models can significantly reduce the testing load by identifying redundant or low-value test cases early in the process. Techniques such as Generative Adversarial Networks (GANs) are especially effective at generating test cases that are more likely to improve coverage, as they can mimic and prioritize under-tested scenarios more intelligently than random methods.

Graph Neural Networks (GNNs) are another powerful AI tool transforming product testing. GNNs can generate rich, structured representations of code and design logic. For mature or near-complete designs, GNNs can predict the potential code coverage achievable with a given set of test cases. This predictive capability is much faster than executing full test runs. With this information, testing teams can pre-select an optimized set of test cases that are likely to deliver the desired coverage, significantly accelerating the validation phase.

Additionally, Reinforcement Learning and Transformer-based models operating on tabular or structured test data have also shown promise in optimizing test strategies and prioritizing impactful test paths.

By integrating these AI techniques into the testing process, organizations can reduce time-to-market, lower testing costs, and maintain or even improve product reliability. AI doesn’t just speed up testing—it makes it smarter, more targeted, and better aligned with the complexity of modern product development.

Future Directions with GenAI & LLMs

As Large Language Models (LLMs) and Generative AI (GenAI) increasingly contribute to code generation, the nature of software development is rapidly evolving. These tools can produce functional code quickly by drawing on vast amounts of training data, often offering popular or commonly accepted solutions. However, subtle bugs or logic mismatches—arising from differences between actual requirements and AI-generated code—can be difficult to detect through traditional testing methods.

In this new landscape, robust and intelligent testing is more critical than ever. AI-driven testing frameworks not only accelerate the validation process but also help uncover hidden biases and logic errors that may go unnoticed in standard test cycles. As AI becomes a more integral part of both code creation and testing, its dual role ensures a more resilient, efficient, and reliable development pipeline.

This article was originally published on Times Tech Buzz in the May 2025 edition.

The post Bias in Business and Product Testing: How AI is Changing the Game appeared first on Tiger Analytics.

Technology adoption in business: A three-part evolution

In 2025, technology adoption in business will evolve across three key areas: enhancing customer experiences, improving internal business processes, and empowering the workforce with AI.

1. Personalized customer experience at scale

AI is already transforming customer service, shifting from reactive support to proactive, personalized interactions. Early success stories in human-like or even better-than-human quality interactions regularly emerge. In 2025, businesses will feel confident to “scale up” their AI-driven customer service initiatives, delivering hyper-personalized experiences for diverse customer segments. AI will not only assist with simple customer service tasks like bank balance enquiry but will also offer personalized style recommendations, financial advice and possibly health & wellness advice. Machine Learning (ML) models for predictions based on structured data, computer vision, text, and speech processing, combined with Language Models (LM) to bring rich context and human-like empathy, would elevate the quality of conversations customers could have with such personal AI assistants or “agents”.

2. AI productivity boosters for the workforce

Behind every great customer experience is a workforce empowered by technology. In 2025, AI tools will enable both technical and business ‘personas’ in an organization, providing a significant productivity boost to their daily tasks. Many technology developers already benefit from the automated code generation capabilities of AI. For business personas, AI offers an interesting promise. In response to voiced-out instructions, a team of self-organising AI agents could perform complex analysis, serve up the analysis for critical go/no-go decisions from the business user, and then go ahead with the automated execution of processes. For example, redistribution of thousands of orders across fulfilment centres to ensure timely fulfilment of holiday season customer orders, while keeping incremental cost to the company within a threshold. In 2025, multiple business personas like supply chain associates, finance managers, marketing managers and creative developers would have access to AI agents to create a win-win experience for their end customers and for themselves.

3. Systems & process integration

The full potential of AI can only be realized with the seamless integration of systems and processes. By 2025, businesses will increasingly adopt AI-driven frameworks to automate and integrate business operations, ensuring a smoother flow of data and more efficient execution. This integration will set the stage for end-to-end automation, combining people, processes, data, and technology to optimize business outcomes.

Technology Adoption Across Generations: A Broad Consumer Shift

Technology adoption is no longer just the domain of the tech-savvy youth. Thanks to the AI revolution, all generations are becoming more comfortable integrating technology into their daily lives, driving demand for smarter, more personalized experiences.

Youth: The digital learners

Students are already using technology to enhance their learning experiences, moving away from traditional methods like handwritten or computer-generated flashcards in favour of AI-generated lesson summaries. This trend will continue to accelerate in 2025, as AI further reshapes education by providing tailored learning experiences for each student.

Adults: The digital consumers

The digitally literate adult population is already accustomed to curating their own news, shopping, and entertainment experiences. In 2025, we will see the emergence of intelligent personal assistants helping consumers negotiate the best deals, complete transactions, and manage everyday tasks more efficiently. Additionally, the adoption of electric vehicles and smart technologies will continue to rise, though self-driving cars will depend on infrastructure readiness.

Seniors: The companion revolution

For seniors, technology adoption will be driven by the need for companionship and support. A small segment of affluent seniors may experiment with social robots—AI-powered physical robots capable of holding empathetic conversations. While this may seem very futuristic, there have been innovations like Jibo, almost a decade back. With recent developments in the human language capabilities of AI, we could see the reemergence of this concept of emotional support and companionship via social robots. However, it will be well beyond 2025 when such capabilities will be used at scale.

Advanced R&D: Paving the way for tomorrow’s technologies

The frontier of technology is constantly pushed forward through research and development, where the focus of adoption will be on technology solutions that will shape the next 5 to 10 years.

Quantum computing & cryptography

By 2025, quantum computing will be a growing topic in academic and business circles. While not yet ready for mainstream use, quantum computing infrastructure will continue to expand via the cloud, providing new capabilities for researchers. Quantum cryptography will also progress, providing a foundation for secure, AI-driven solutions in the future.

[Note: just as I finished writing this article, I saw this announcement from Google about the breakthrough performance of their quantum computing chip – Willow].

Clean energy: Powering AI

Clean energy solutions will play a critical role in fueling the growing power demands of AI technologies. Research into renewable energy sources and even fission technologies will continue at a steady pace, ensuring that the future of AI remains sustainable and eco-friendly.

Conclusion

In 2025, technology will continue to be the driving force behind productivity and innovation across industries. AI will reshape everything from customer service to education and beyond, with transformative impacts on the workforce and consumer behaviours. Governments, corporations, and individuals alike will need to invest in technology and STEM education to ensure they are equipped for this rapidly evolving landscape. The future is bright, and those who embrace these technological advancements will shape tomorrow’s world.

Key takeaways:

• AI-Powered customer service: By 2025, AI will scale to deliver personalized, proactive customer experiences at a level not seen before.
• Empowered workforce: AI productivity tools will help businesses optimize processes, with assistants available for employees in all sectors.
• Generational technology adoption: From students to seniors, all generations will increasingly rely on AI, from learning tools to companion robots.
• Advanced R&D: Quantum computing and clean energy will continue to develop, laying the foundation for a sustainable AI-driven future.

This article was originally published in ET Edge Insights on December 15, 2024.

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