How Adaptability in Delivery Reduces Risk and Builds Confidence

Eden Smith • January 14, 2026

Why flexible, human-centred approaches lead to stronger outcomes in data and transformation projects



The Risk of Rigid Delivery Models


Many transformation programmes fail not because the technology is wrong, but because the delivery approach is too rigid.


Traditional delivery models often assume certainty: fixed requirements, clearly defined outcomes, stable teams, and predictable timelines. In reality, data and AI projects rarely operate in this way. Business priorities shift, data quality issues emerge, stakeholders’ understanding evolves, and external pressures, from regulation to market change, can alter direction overnight.


When delivery models don’t allow for adaptation, risk increases. Teams feel pressure to “stick to the plan” even when evidence suggests the plan needs adjusting. Issues get hidden rather than addressed. Confidence erodes as people worry about being blamed for deviations instead of supported in solving problems.


This rigidity also impacts people. Stakeholders who feel unheard disengage. Delivery teams lose autonomy. Leaders become cautious, delaying decisions because change feels expensive and risky.

Ironically, the attempt to reduce risk through tight control often creates the very conditions that cause projects to stall or fail.


Adaptability flips this dynamic. Instead of seeing change as failure, it treats it as insight. Instead of protecting plans, it protects outcomes.


Adaptability as a Risk-Reduction Strategy


Adaptable delivery is not about being unstructured or vague. It is about building in feedback loops, decision points, and the freedom to respond to what the work reveals.


In practice, this means:

  • Testing assumptions early and often
  • Delivering in small, meaningful increments
  • Creating space to pause, reflect, and course-correct
  • Involving stakeholders throughout, not just at milestones


This approach reduces risk by surfacing problems sooner, when they are easier and cheaper to fix. Data gaps, unclear ownership, unrealistic expectations, or capability constraints become visible early rather than late.


Adaptability also spreads ownership. When teams and stakeholders are involved in shaping the direction, they are more invested in the outcome. Decisions feel shared rather than imposed, which increases commitment and momentum.


Crucially, adaptable delivery acknowledges that uncertainty is normal in complex work. Rather than pretending everything is known upfront, it designs for learning.


Over time, this creates a culture where raising concerns is encouraged, not penalised. Risk becomes something to manage collectively, not something individuals feel they must carry alone.


How Adaptability Builds Confidence at Every Level


Confidence is often overlooked in transformation, yet it is one of the strongest predictors of success.


When delivery is adaptable, people gain confidence because:

  • They see progress quickly and tangibly
  • Their input influences direction
  • They are trusted to make decisions within clear boundaries
  • Mistakes are treated as learning, not failure

For delivery teams, this builds professional confidence. They move from executing tasks to solving problems, strengthening judgement and ownership.


For stakeholders, adaptability builds trust. They don’t feel locked into decisions made too early or too far removed from reality. Instead, they gain confidence that the programme can respond to their evolving needs.


For leaders, adaptable delivery reduces the fear of backing the “wrong” decision. When course correction is expected, leadership becomes about enabling learning rather than defending certainty.

This confidence compounds. As teams experience successful adaptation, they become more willing to engage, experiment, and innovate. Adoption improves because people understand not just what has been delivered, but why it works.


Ultimately, adaptability doesn’t just protect projects, it strengthens the organisation’s ability to change again in the future.


From Delivery Method to Cultural Advantage


Organisations that embrace adaptable delivery don’t just deliver better projects; they build stronger cultures.


They develop people who are comfortable with ambiguity, confident in decision-making, and skilled at collaboration. They reduce dependency on heroics and create repeatable ways of working that scale.


In a world where data, AI, and business needs evolve constantly, adaptability is no longer a “nice to have”. It is a core capability, one that reduces risk, builds confidence, and leaves teams better equipped for whatever comes next.



Get in touch with the Eden Smith Team. We’d love to explore how adaptable, human-centred delivery could support your teams and outcomes.

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Responsible by Design - Keeping AI Human

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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