As artificial intelligence (AI) continues to reshape the insights function, the key question for many insights practitioners has become not whether to adopt AI, but who controls it and how.
According to Jack Austin, commercial insights lead at Virgin Media O2, customer research has evolved from a mechanism from which to seek reassurance to a guide that can direct strategy and improvements. The reason for this is clear.
As Austin explains: "Whether it is economic pressure, changing consumer expectations, technological disruption or shifts in trust and loyalty, businesses increasingly need a deeper understanding of their customers."
Ahead of his opening panel at All Access: Future of Customer Insights and Data Analytics, Austin speaks to CX Network about translating user research into the operational and revenue metrics leaders will act on, and the top considerations organizations should make when deciding whether to build or buy their insights tools, and why insight teams should be at the center of the AI agenda.
CX Network: The panel you feature in is called Owning AI, not being owned by it and it will cover the evolving role of the insights team. What are some of the most notable ways you have seen insights evolve over the course of your career?
Jack Austin: When I first started my career in insights, research was often viewed as something that happened towards the end of the decision-making process. It was a validation step: a way of checking that a proposition, pricing change or campaign had been tested with customers and was unlikely to create significant issues.
In many cases, research was there to provide reassurance rather than to actively shape direction.
The biggest evolution I have seen is the move from research as a reactive service to insight as a strategic capability. The role of insight teams has become much more about bringing the voice of the customer into decisions from the outset, helping organizations understand changing behaviors, identify opportunities and navigate uncertainty.
That shift has been accelerated by the world becoming more complex. Whether it is economic pressure, changing consumer expectations, technological disruption or shifts in trust and loyalty, businesses increasingly need a deeper understanding of their customers. The challenge is that senior leaders are often naturally removed from the day-to-day realities of customers' lives. As the saying goes, "you are not your customer".
Insight teams have a unique role in bridging that gap. By combining research, behavioral understanding and commercial context, we can help organizations balance short-term operational pressures with longer-term customer relationships.
At the same time, the expectations of insight teams have changed. We are no longer judged purely on the quality of individual studies; we are judged on our ability to influence decisions, build relationships and make insight accessible across organizations.
The rise of AI is the next major stage of that evolution. As insight tools and capabilities become more widely available, the value of expertise, judgement and context becomes even more important. The future of insight is not about owning information; it is about helping organizations understand what information matters, what it means and what action should follow.
CX Network: One of the points this panel will explore is that insights and CX teams should sit at the center of the AI agenda. How can insights and CX leaders position themselves at this intersection?
Jack Austin: Insight and CX teams cannot afford to sit on the sidelines of the AI conversation. The question is not whether AI will impact our industry – it already has. The question is how we ensure it is applied in a way that genuinely improves decision-making and customer understanding.
Our role has always been about helping organizations make better decisions based on evidence. AI creates huge opportunities to accelerate how we analyze information, synthesize learning and identify patterns, but those capabilities still require expertise, judgement and context.
The availability of technology does not automatically create the ability to use it effectively.
This is why insight teams should be at the center of the AI agenda. We understand the strengths and limitations of different approaches, we understand research quality, we understand customer behavior and, importantly, we understand where the technology can and cannot be trusted.
There is a risk that AI becomes viewed simply as a cheaper or faster replacement for existing insight activities. That is where insight teams need to be visible and proactive. We need to demonstrate that we are not resisting change; we are leading it. We should be the team helping organizations understand where AI can add value, where human judgement remains essential and how we maintain quality and trust.
However, ownership does not mean keeping AI behind closed doors. Successful adoption requires collaboration and transparency. Other teams will want to understand what is possible and how these tools can help them. Our role is to guide that adoption, establish appropriate guardrails and ensure AI is used to enhance insight rather than dilute it.
The phrase "owning AI, not being owned by it" captures that balance. We need to embrace the technology, experiment with it and make it part of our everyday work, while retaining the uniquely human elements that make insight valuable: understanding context, recognizing nuance and knowing when the data does not tell the whole story.
The future belongs to insight teams that combine technological curiosity with human understanding.
CX Network: Communicating CX to the C-suite remains a challenge for many practitioners. How do you translate user research into the operational and revenue metrics leaders will act on?
Jack Austin: The challenge for insight teams has never simply been generating understanding of customers; it is ensuring that understanding translates into decisions the business can act on.
At a senior level, customer insight needs to connect with the realities leaders are responsible for: growth, revenue, retention, operational performance and long-term sustainability. That does not mean reducing every customer decision to a financial equation, but it does mean understanding the commercial context and helping leaders navigate the trade-offs involved.
A key role for insight teams is often acting as the bridge between customer needs and business priorities. For example, there may be decisions that create short-term commercial benefit but damage customer trust over time, or there may be customer improvements that are highly valued but simply are not commercially viable. Insight helps bring evidence and perspective into those decisions.
The first step is understanding what different stakeholders need from insight. Not every audience requires the same output. Some decisions need a clear recommendation, some need a compelling customer story, and others require a direct connection to operational or financial metrics.
This is where strong relationships with stakeholders and collaboration across the business become critical. Insight teams should work closely with colleagues in areas such as analytics, modeling and commercial strategy to connect customer understanding with measurable outcomes. For example, understanding how changes in customer experience influence loyalty, churn or revenue requires bringing different perspectives and expertise together.
As insight professionals, we are often very comfortable explaining whether something is a good idea from the customer perspective. The challenge is ensuring we can also demonstrate why it matters commercially. That is where collaboration, historical learning and stronger links between insight outcomes and business performance become increasingly important.
Ultimately, the goal is not to make customer insight more commercial by losing the customer perspective. It is to make business decisions better by ensuring commercial choices are informed by a genuine understanding of customers.
CX Network: If an organization is deciding whether to build or buy the tools they need for insights work, what are some of the factors they should consider?
Jack Austin: The decision to build or buy insight tools should start with the problem an organization is trying to solve, rather than the technology itself.
There is understandably a lot of excitement around AI capabilities, but the most important question is not simply whether a tool can process information quickly. It is whether it can provide meaningful, reliable and contextually relevant insight that helps people make better decisions.
One of the biggest considerations is the quality and breadth of the data and knowledge behind the tool. Insight is not created from information alone; it comes from understanding the context behind that information. A generic AI model may be able to identify patterns, but it will not necessarily understand the nuances of a particular market, customer base, brand or organization.
This is where a more tailored approach can provide value. Whether an organization builds internally or works with an external partner, the solution needs appropriate guardrails, relevant data sources and a clear understanding of how it will be used.
There are also practical considerations around ownership, governance and ongoing investment.
AI tools are not a one-off purchase. They require maintenance, updating and continuous improvement to ensure they remain accurate, relevant and aligned with business needs.
Organizations should be clear about who owns the tool, who is responsible for quality and how it fits into existing ways of working.
For many organizations, the right answer may be somewhere between building and buying: partnering with trusted specialists who understand both the technology and the organization's specific needs. This can provide access to expertise while avoiding the limitations of completely off-the-shelf solutions.
Finally, there needs to be a clear focus on return on investment. The strongest AI use cases will be those where the technology allows insight teams to spend less time on manual processing and more time applying judgement, generating understanding and influencing decisions.
The aim should not be to automate insight out of existence. It should be to remove friction, increase capability and allow insight professionals to focus on the parts of the role where human expertise creates the greatest value.
Register now to watch Jack Austin speak alongside Bill Staikos at All Access: Future of Customer Insights and Data Analytics
Quick links
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