Many companies are facing budget and timeline pressures, and the need to do more with less, faster. But how can you manage this while still getting the critical insight that you need, and without compromising too much? In this article, we will talk about the areas to focus on to help you get the most from your time and budget, all whilst still getting the insight you need to succeed.
What does agile research mean?
Agility has become quite the buzzword in Research and Development (R&D), but what does it truly mean? Agile research is more than a methodology; it represents a cultural shift, a mindset that prioritises efficiency, adaptability, and a seamless integration of insights throughout the product development process. It’s about doing more with less, moving faster without sacrificing quality, and, crucially, embedding consumer needs at every step. Yet, despite its promise, agility is a complex, multifaceted concept that requires continuous refinement and alignment.
Agility defined: beyond speed
The misconception that agility is synonymous with speed oversimplifies its true nature. Agility, as defined by practitioners, is about optimising resources to achieve better outcomes, not just faster results. It’s about working smarter: front-loading foundational research when necessary, iterating quickly when appropriate, and tailoring approaches to each project’s unique needs.
For some, agility means embedding research at every phase of the development process, shifting away from the traditional siloed approach. For others, it’s about fostering collaboration across diverse stakeholders, ensuring that everyone – be it marketers, procurement teams, or product designers – is aligned on objectives. Ultimately, agility is not just a process; it’s a philosophy that organisations must embrace and adapt to their specific contexts.
Challenges to achieving agility
Despite its benefits, implementing agility is fraught with challenges. Three recurring barriers often arise:
1. Stakeholder alignment
Agility requires collective buy-in from all stakeholders, but achieving alignment is easier said than done. Different departments often have conflicting priorities, and global organisations face additional hurdles in bridging cultural and logistical gaps across regions. Stakeholders may resist change, clinging to legacy processes or misunderstanding the objectives of agile methods. This can lead to inefficiencies and, in some cases, derail projects altogether.
2. Discipline and focus
The fast-paced nature of agile projects often tempts teams to overextend themselves. For instance, adding extra questions to consumer surveys or testing multiple prototypes simultaneously might seem efficient, but it can dilute the clarity of insights and slow down decision-making. Discipline is crucial – not just in maintaining a clear scope but in resisting the urge to overcomplicate.
3. Legacy processes and systems
Many organisations, especially large, established ones, grapple with entrenched processes that are ill-suited to agile methods. Transitioning from traditional, linear workflows to an agile framework often involves rethinking everything from procurement cycles to consumer testing timelines. Breaking free from these constraints requires significant effort, innovation, and patience.
Practical examples of agility in action
Despite these challenges, organisations that embrace agility often unlock remarkable efficiencies and insights.
1. Rapid usability testing at scale
One company implemented a rapid testing protocol to evaluate prototypes with 100 participants in a single day, delivering actionable data within 48 hours. By combining qualitative and quantitative approaches, the process struck a balance between depth and speed, enabling iterative improvements that aligned with both stakeholder needs and consumer feedback.
2. Engaging hard-to-reach consumers
In the maternal health sector, a company faced the challenge of engaging new mothers during the critical postpartum period. Instead of relying on standard methodologies, they developed long-term partnerships with key recruitment agencies and built consumer panels. This approach not only addressed immediate testing needs but also established a scalable framework for future research.
AI: a catalyst for agility
Artificial intelligence (AI) has emerged as a powerful tool in the pursuit of agility. By automating repetitive tasks, analysing large datasets, and generating new insights, AI enhances researchers’ capacity to act quickly and effectively.
AI tools in practice
Kaplina: automates the coding of open-ended survey responses, performing sentiment analysis at scale while maintaining quality through human oversight
Coloop: summarises qualitative data from focus groups and interviews, enabling researchers to focus on deeper analysis and interpretation
Affectiva: uses facial coding to capture product-related emotions, offering insights beyond traditional “liking” metrics
Voice-to-text platforms: enable quick, on-the-go testing by allowing respondents to articulate their thoughts verbally rather than in writing
Applications in research
AI’s versatility makes it invaluable across multiple stages of the R&D process:
Idea generation: generating consumer-centric mood boards based on real data rather than relying on internal assumptions
Data processing: reducing administrative workloads, such as questionnaire formatting or demographic coding
Prototyping and co-creation: facilitating live iterations of designs during co-creation sessions
Balancing the benefits and risks of AI
While AI offers undeniable advantages, its integration into R&D must be approached with caution. Ethical concerns, such as biases in AI models and the potential for over-reliance, demand scrutiny. Many AI platforms are built on datasets that lack diversity, raising questions about their applicability to non-English-speaking or underrepresented demographics.
Moreover, AI should complement – not replace – human expertise. Researchers must maintain their fundamental skills in data analysis, moderation, and interpretation to challenge and validate AI outputs effectively. Without this foundation, organisations risk losing the nuance and empathy that make research truly impactful.
The future of agility and AI in R&D
As organisations refine their agile methodologies, the integration of AI is poised to play an even greater role. Looking ahead, several priorities stand out:
Ethical AI use: developing models that are inclusive and representative of global consumer bases
Enhanced collaboration: blending in-house capabilities with agency expertise to create flexible, tailored solutions
Focus on fundamentals: ensuring that researchers maintain core skills alongside AI adoption, safeguarding the human element of research
Innovative applications: expanding the use of AI in areas like video analysis, synthetic data generation, and live co-creation
Conclusion: agility as a journey
Agility is not a destination but an ongoing journey. It requires a mindset of continuous improvement, where teams strive to balance speed, efficiency, and quality. By embracing agile methodologies and leveraging tools like AI thoughtfully, organisations can deliver products that resonate deeply with consumers while staying ahead of the competition.
Yet, amidst all the innovation and transformation, the fundamentals of research remain timeless: asking the right questions, keeping consumer needs at the heart of the process, and maintaining human empathy. These principles, combined with the power of agility and AI, form the foundation for a future where R&D is not only faster but smarter and more inclusive.
As we move into the next phase of agility in R&D, the focus must remain on collaboration, clarity, and continuous learning – ensuring that every step forward is meaningful and impactful.
We’d love to hear from you! What do you think of these strategies? Are there any key points we missed? What’s worked for you in managing tight budgets and timelines? Share your thoughts, and let’s start a conversation. If you’d like to discuss your specific challenges and explore how we can help, don’t hesitate to reach out to [email protected].
This article is a comprehensive written summary of our Campaign for Real Innovation Webinar, with client-side guest speakers Elliot Hollamby-Jones from Bayer, Stephanie Jones from Additional Lengths, and hosted by Richard Heath from Blue Yonder in November 2024. The full webinar is available to watch on-demand via the YouTube link below. Thank you for your interest.
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