I recently watched the Netflix documentary BlackBerry, and it got me thinking. As I witnessed it unfold, I couldn’t help but draw parallels to our world of software Quality Assurance (QA) and testing. So, let me share a few insights on what we can learn from BlackBerry’s rise and fall, especially when adapting to new technologies and testing approaches.
Innovation and market domination
BlackBerry, originally known as Research In Motion (RIM), started off as a two-way pager before evolving into the first smartphone with integrated email functionality in 1999. This was a game-changer. BlackBerry became the go-to device for businesses, governments, and professionals who valued secure messaging. By the mid-2000s, BlackBerry had captured over 40% of the U.S. smartphone market.
At its peak, BlackBerry was known for its commitment to security, its secure messaging platform BlackBerry Messenger (BBM), and its distinctive but highly functional physical keyboard. In the early 2000s, it was the essential tool for corporate communication and enjoyed success with a dedicated, loyal user base.
Failure to adapt
However, as the tech world evolved, BlackBerry struggled to keep up with the rapid advancements in mobile technology. The launch of the iPhone in 2007 and the rapid growth of Android devices signalled a major shift in the mobile industry. While BlackBerry excelled in security, it stuck to its physical keyboard and enterprise-focused model. Meanwhile, Apple and Android quickly captured the broader smartphone market by appealing to both consumers and businesses through multimedia capabilities, social media integration, and continuous innovation in hardware and software with touchscreen devices and rich app ecosystems. The documentary BlackBerry (2023) highlights how RIM was slow to adapt to the growing demand for touchscreens, apps, and consumer-focused features, ultimately leading to its eventual downfall.
Despite attempts to regain relevance with its BlackBerry 10 operating system and Android-based phones, the effort came too late. By the time BlackBerry shifted its focus to enterprise software, cyber security, and IoT, the mobile market had moved on. BlackBerry eventually shut down its mobile services by 2016.
Pivot when the wave comes
BlackBerry’s rise and fall offer valuable lessons, particularly in the fast-paced world of software QA. Just as BlackBerry struggled to keep up with evolving technologies, QA professionals must also avoid falling behind by adapting to the growing demands of non-functional testing, such as security and performance, and embracing the rise of Generative AI.
Continuous innovation is essential
BlackBerry failed to innovate quickly enough, and that’s a mistake we can’t afford to make in QA. We need to be proactive in adopting new testing methodologies. The rise of Generative AI in software development and testing isn’t just about automating processes – it’s about testing AI systems themselves, particularly with a focus on accuracy, robustness, explainability, and performance.
- Non-functional cyber security testing is no longer optional: As the tech landscape becomes more complex, security and performance testing are critical to the success of any software product. In the past, BlackBerry’s emphasis on secure communication was a major selling point. Today, security testing is paramount, especially with the rise of Gen-AI. Generative AI introduces new vulnerabilities and requires testing for biases, fairness, and data privacy. Similarly, performance testing needs to evolve as AI-driven systems often have vastly different performance metrics and resource requirements.
- Adapting to new technologies like Gen-AI: BlackBerry’s failure to pivot quickly was a direct consequence of its failure to adapt to new technology. In QA, it’s no different. While AI-augmented testing tools have been a focus, we now need to start focusing on testing Gen-AI itself. This involves developing unique test strategies, such as adversarial testing, back-to-back testing, and even techniques like metamorphic testing to ensure the reliability and accuracy of AI outputs. As AI becomes a larger part of software development, thorough testing at every stage will be crucial.
- Building a robust ecosystem for non-functional testing: BlackBerry’s limited app ecosystem ultimately hindered its growth. In software QA, we can avoid this pitfall by creating an integrated testing environment that covers the full stack of testing offerings, ranging from functional testing and non-functional aspects like security and performance. Testing Gen-AI requires new tools that can measure AI-related performance (such as latency, throughput, and scalability) and security (such as vulnerability to adversarial attacks and data leaks). An all-encompassing approach to testing can help ensure we’re covering all bases.
- Speed and agility matter more than ever: BlackBerry’s slow response to market changes ultimately harmed the company. In QA, speed and agility are just as important. The faster we can implement new security measures, performance checks, and AI testing, the more we keep our practices ahead of the curve. With the rise of DevSecOps and Continuous Integration/Continuous Deployment (CI/CD), continuous security and performance testing must be integrated into every phase of development. Failing to be agile in this environment will leave us trailing behind.
- User experience is crucial, even in testing: While BlackBerry was known for its security, it failed to provide a user-friendly experience compared to its competitors. In QA, we need to ensure the tools we use are intuitive and efficient, not only for testers but also for the users of the software. As we test systems powered by AI, ensuring seamless user experiences while maintaining security and performance will be key. AI-driven systems need to be tested for accuracy and reliability in real-world use cases to ensure they perform as expected under varied conditions.
- Know when to pivot: BlackBerry’s pivot to software and cybersecurity came too late. Similarly, in QA, when a specific testing approach or tool no longer meets the needs of the business or technology, we need to adapt quickly. As Gen-AI evolves, new testing strategies will emerge. By embracing these shifts early, whether through enhanced security testing, performance testing of AI models, or adapting new AI testing frameworks, we stay ahead of the game.
Evolve or get left behind
At iOCO, we’ve invested heavily in research and development to help clients transition to AI-augmented practices. We go beyond functional testing by tackling AI-specific challenges like security and performance testing. We’ve built reusable frameworks that accelerate AI test automation, supporting faster and more effective delivery. We’re also focused on enablement –through masterclasses and short courses, we’re upskilling our team to stay ahead of the curve and deliver confidently in this evolving space.
The future of quality engineering is already shifting. Those who evolve soon enough will lead the way with smarter, more secure AI solutions. Let’s shape the future of AI-driven quality engineering together.
References:
- BlackBerry (2023). Netflix Documentary.
- BBC News. (2016). The rise and fall of the BlackBerry handset
- BBC News. (2016). BlackBerry stops designing its own phones.
