Data Consulting that takes Accountability for Deliverables & Builds High-Impact Teams

Nick Deveney • June 4, 2025

The journey from a successful data pilot to a fully scaled solution is often more complex than it seems. Many organisations find early success with proof-of-concept projects, only to stumble when trying to implement those learnings at scale. This is where experienced data consultants become invaluable, not just for their technical knowledge, but for their ability to build and structure high-impact teams that can deliver sustained, enterprise-level transformation. 


Why Data Pilots Succeed but Scale Fails 

Data pilots are designed for speed, agility, and experimentation. They’re usually run by a small, cross-functional group of internal stakeholders and external experts, often in a silo. Success is typically measured by early performance indicators, faster insights, better customer segmentation, more accurate forecasts. 

But when it’s time to expand the project across departments or regions, the cracks often show. Scaling introduces new variables: data governance, infrastructure readiness, change management, security compliance, and, most critically, talent gaps. Without a team model that supports collaboration, knowledge transfer, repeatability, and resilience, even the most promising pilot can stall. 

Our consultants play a key role in navigating this phase. When we embark on a project our teams embed into your teams seamlessly, creating a collaborative workspace that upskills your teams and increases their impact. They don’t just help validate a model; they help design the organisational structures and team capabilities that will carry that model through operationalisation. This can include identifying internal champions, outlining skills matrices, and advising on org design to future-proof the solution. 


Building the Right Team for Scale 


Success in data scaling isn’t about hiring more people; it’s about hiring the right people and empowering them within the right frameworks. High-impact teams are built on complementary skill sets, clear roles, and a shared understanding of business goals. 

A good consultancy works with leadership to identify which roles are critical for scale. Our unique business model means that not only do our partners have access to niche data consulting experts but leading recruiters with specialist knowledge in the data space. Taking a skill-based approach, our staffing team can help identify hidden talent with a future-focussed approach to hiring today. 

Equally important is assessing whether to augment existing teams, hire new talent, or to bring in contractors or partner firms. Boutique consultancies and their people provide objective insight here, often leveraging industry benchmarks and workforce analytics to guide decisions. 

Beyond technical staffing, consultants also advise on cultural alignment. Are teams empowered to experiment? Are KPIs connected to data outcomes? Are stakeholders trained in data literacy? These soft elements can make or break a scale-up effort. 


From Advisory to Action: Creating Repeatable Impact 


The final step in scaling data initiatives is embedding a feedback loop between implementation and outcomes. Consultants help teams move from reactive decision-making to proactive insight generation by designing systems of continuous learning and optimisation. 

This often includes: 

  • Establishing data product roadmaps 
  • Setting up cross-functional steering committees 
  • Building internal training academies or Centres of Excellence 
  • Implementing agile delivery practices tailored for data work 

Importantly, consultants don't just "hand over" a solution, they build internal capability to own it. That means structuring teams to be self-sufficient, automating governance where possible, and instilling a mindset of long-term value creation. 

When scaling is done well, it’s not just about rolling out more dashboards or pipelines. It’s about enabling your people to ask better questions, make faster decisions, and drive measurable outcomes. 

 

 Looking to Scale Your Data Strategy? 

At Eden Smith, our boutique data consulting division specialises in building agile, high-impact data teams tailored to the unique challenges of scaling. Whether you're transitioning from pilot to enterprise rollout or rethinking your team structure, we can help you future-proof your data journey. 

Whether you're expanding a promising pilot or rethinking your team structure for enterprise impact, our expert consultants Nicholas Deveney and  Lauren Johns are here to help. 


By Christa Swain December 3, 2025
Executive Summary: AI, Ethics, and Human-Centred Design Our recent Leaders Advisory Board event - designed in partnership with Corndel - featured three engaging sessions that explored how AI impacts human cognition, customer experience, and fairness. Here's what we learnt: 1. Think or Sink – Are We Using AI to Enhance or Reduce Cognitive Ability? Speaker: Rosanne Werner , CEO at XcelerateIQ & ex Transformation Lead at Coca-Cola Roseanne opened the day with an interactive and thought-provoking session, firmly positioning AI: “AI should be your sparring partner, not your substitute for thinking.” Her research revealed a striking insight: 83% of people using LLMs couldn’t recall what they wrote, compared to just 11% using traditional search . The message? It’s not about avoiding AI, but using it in ways that strengthen thinking , not outsource it. Roseanne explained how our brains form engrams - memory footprints that enable creativity and critical thinking. Over-reliance on AI risks weakening these pathways, reducing retention and problem-solving ability. She introduced the Mind Over Machine Toolkit , six strategies to use AI as a thinking partner: Provide Context First – Frame the problem before asking AI. Use AI as a Challenger – Stress-test ideas and uncover blind spots. Iterative Co-Creation – Collaborate, refine, and evaluate. Document Your Thinking – Keep reasoning visible. Reflective Prompts – Support reflection, not replace judgment. Sparring Partner – Test assumptions and explore risks. Roseanne summed it up with a simple rule: use Sink for low-value, repetitive tasks, and Think for strategic, creative decisions. 2. Designing Chatbots with Human-Centred AI Speaker: Sarah Schlobohm , Fractional Chief AI Officer Sarah brought a practical perspective, drawing on experience implementing AI across sectors - from banking and cybersecurity to rail innovation. She began with a relatable question: “Who’s been frustrated by a chatbot recently?” Almost every hand went up. Through a real-world example (redacted out of politeness), Sarah illustrated how chatbots can fail when designed with the wrong priorities. The chatbot optimised for deflection and containment , but lacked escape routes , sentiment detection, and escalation paths - turning a simple purchase into a multi-day ordeal. “Don’t measure success by how well the chatbot performs for the bot—measure it by how well it performs for the human.” Sarah introduced principles for better chatbot design: Human-Centred Design – Focus on user needs and emotional impact. Systems Thinking – Consider the entire process, not just chatbot metrics. Escalation Triggers – Negative sentiment, repeated failures, high-value intents. Context Awareness – Detect when a task moves from routine to complex and route accordingly. The takeaway? Automation should remove friction from the whole system - not push it onto the customer. 3. Responsible AI and Bias in Large Language Models Speaker: Sarah Wyer , Professional Development Expert in AI Ethics at Corndel “When we create AI, we embed our values within it.” She shared her journey tackling gender bias in large language models , from GPT-2 through to GPT-5, and highlighted why responsible AI matters. AI systems reflect human choices - what data we use, how we define success, and who decides what is fair. Real-world examples brought this to life: facial recognition systems failing to recognise darker skin tones, credit decisions disadvantaging women, and risk assessment tools perpetuating racial bias. Even today, LinkedIn engagement patterns show gender bias! Sarah made the point that simple actions - like testing prompts such as “Women can…” or “Men can…” - can reveal hidden disparities and spark vital conversations. To address these issues, Sarah introduced the D.R.I.F.T framework , a practical guide for organisations: D – Diversity : Build diverse teams to challenge bias. R – Representative Data : Ensure datasets reflect all user groups. I – Independent/Internal Audit : Test outputs regularly. F – Freedom : Create a culture where employees can challenge AI decisions. T – Transparency : Share processes without exposing proprietary code. Wrapping up the final session - before we opened the floor to panel questions and debate - Sarah created the opportunity to discuss how we address AI bias within our organisations by stepping through the DRIFT framework. Shared Themes Across All Sessions AI is powerful, but context matters . Human oversight and ethical design are critical . Use AI to augment thinking , not replace it. Measure success by human outcomes , not just automation metrics. We've had such great feedback from this event series - especially around the quality of speakers and the opportunity to have meaningful conversation and debate outside of functions. Definitely more in the events plan for 2026! If you'd like to be part of the conversation please navigate to our LAB events page to register your interest .
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