By Alan Jacobson
Analysts are shaping how organisations unlock measurable value from artificial intelligence. Alan Jacobson highlights their leadership in adopting AI and automation to drive strategic decisions, optimise performance, and enhance cross-functional collaboration. By following their example, businesses can embed AI as a shared capability, accelerating transformation and improving key performance outcomes.
Despite the growing hype around artificial intelligence (AI), only 39% of UK businesses are actively using it, according to Moneypenny’s 2025 Trends Report. This gap between interest and implementation is not due to a lack of ambition. I believe it’s due to a lack of an actionable AI strategy. Many leaders are still grappling with a fundamental question: how can we turn AI from a concept into measurable business value?
However, there is one role that is helping to buck the curve. We’re seeing data analysts emerge as AI frontrunners – not only in how they’re using the technology but in how they’re proving its worth. They’re demonstrating how to extract tangible business value from AI and, in doing so, setting a precedent for how it can be adopted across business functions. Let’s explore why.
Driving AI and automation adoption
Analysts have long been early adopters of new technologies. Their readiness for AI is reflected in our research on UK analysts’ adoption of AI and automation tools, The 2025 State of Data Analysts in the Age of AI, which revealed that 97% of UK analysts are already using AI and 87% are using analytics automation daily. They’re leading the charge on AI adoption because they understand how it will transform their jobs. Only 13% of UK analysts are “extremely concerned” about losing their jobs, and in complete contrast, 89% see AI as a career enabler.
As Gartner highlights, data and analytics professionals are central to AI success because they know how to turn complex data into strategic decisions and ensure AI initiatives deliver business value. The analysts we surveyed are already using AI to streamline data prep, extract insights at speed and improve stakeholder communication.
At the same time, analysts are applying these insights to strategic initiatives that align with KPIs and actively inform the decisions that influence them. Our research shows they’re using AI and automation to pinpoint opportunities for revenue generation and cost reduction, as well as to support key areas such as workforce planning, operational strategy, and financial management. As a result, 93% say AI has enhanced the perception of their role, positioning them as more strategic contributors within their organisations.
With analysts already on board with AI and automation, attention now turns to how their adoption can be replicated throughout other departments and organisations.
Follow the analyst’s lead
Many individuals across an organisation understand the overarching business goals – whether focused on growth, productivity, or innovation. However, beyond data analysts, few professionals have the tools or expertise to measure their performance against these targets. By following the lead of analysts – who are embracing AI and automation – other roles can gain a clearer understanding of their own KPIs and the AI-driven insights that can help track and improve them.
Analysts are not only at the forefront of AI and automation adoption; they’re also paving the way for other roles and functions to enhance their decision-making. This makes it a strategic priority for organisations to study how analysts are using AI tools, then scale those practices across teams – refining their application over time to drive better business outcomes.
To prepare other departments for AI and automation, organisations must first foster a culture of data and AI literacy. When teams understand the value of analytics, they’re better equipped to engage with AI tools, apply insights effectively, and contribute to smarter, data-informed decisions. This boosts individual and team productivity while also helping to generate continuous feedback on the impact of analytics investments.
Equally important are low-code and no-code platforms, which are transforming how organisations democratise access to data. These tools allow users to explore insights, automate workflows, and uncover key findings without needing deep technical expertise.
This shift is critical. It means that employees across finance, HR, marketing, and operations can independently analyse data, test hypotheses, and make evidence-based decisions, without relying on overburdened data analyst teams. As a result, organisations can scale their analytics capabilities more efficiently, reduce bottlenecks, and empower more people to contribute to performance improvements. Low-code platforms also support collaboration between technical and non-technical teams, ensuring that insights are generated and acted upon quickly and confidently.
Over time, the strategic value of investing in analytics and data infrastructure becomes increasingly evident. With the right tools and training, organisations can embed a culture of continuous improvement. One where every team is equipped to measure, understand, and influence the KPIs that matter most.
Make AI everyone’s business
By empowering more teams to use AI and automation in their own roles, analysts are showing businesses how those who don’t traditionally use data analytics tools can still make AI-driven decisions. This is where the real transformation happens: when AI becomes not just a specialist tool, but a shared capability across the organisation.
The result is a more agile, data-literate workforce – one that understands how to use AI and automation effectively and can demonstrate the ROI of their efforts. It also means that AI adoption is no longer siloed or experimental, but embedded in everyday decision-making.
To truly unlock the value of AI, organisations must follow the lead of their analysts: invest in accessible tools, foster a culture of data curiosity, and scale successful use cases across the business. When AI becomes everyone’s business, the impact on KPIs – and the bottom line – can be both measurable and transformative.
About the Author
Alan Jacobson is the Chief Data and Analytics Officer (CDAO) at Alteryx, where he leads the company’s data science initiatives and drives digital transformation for its global customer base. In this role, he oversees data management and governance, product and internal data, and the utilization of the Alteryx Platform to foster growth.
