by Fiona Gordon & Sarah Burnett | Co-Founders, Dub Dub Data

Let’s be clear. Many enterprise AI projects fail to deliver on their promises. Teams become stuck in endless pilots, budgets spiral, and executives are left questioning whether artificial intelligence is more hype than reality.

The issue isn’t AI itself, but the way it is being implemented. Too often, organisations attempt to bolt AI onto existing systems, which only delivers small improvements at best. The real breakthroughs come from a different mindset altogether: AI-native architecture.

If your business is finding it hard to make AI stick, join the Dub Dub Data community and discover how AI-native design can help you break out of pilot purgatory. Our team can finally help you harness the power of artificial intelligence to drive real results.

🎧 Want to hear the full episode?
Listen to unDUBBED: AI for Enterprise: How Craig Turrell is Cracking the Code here

We’ve dedicated an entire episode of our unDUBBED podcast with Craig Turrell to this very topic, where we unpack the future of AI and machine learning in enterprises.

AI-Native vs AI-Enhanced: Unlocking the Difference

Think of AI-enhanced systems as adding smart features onto outdated foundations. A little automation here, a chatbot there. While these AI tools may streamline minor workflows, the impact is limited because you are still working within legacy constraints.

AI-native architecture, by contrast, starts from a clean slate. It focuses on designing processes for data and analytics in ways that machines can understand from the ground up. In practice, this means:

• Structuring business data for machines first, not just dashboards.
  
• Enabling AI agents to collaborate and verify each other’s outputs. 
• Reducing manual tasks by automating all but the most critical human judgements
  

This approach is not about layering AI technologies onto what already exists. It is about reimagining the organisation’s digital DNA so that AI is no longer an add-on, but the operating system itself. Done right, it creates smart AI solutions that deliver actionable insights and drive measurable change.

Escaping the 20/80 Trap with AI-Powered Solutions

Here’s a common frustration: enterprises buy expensive AI solutions, use 20% of what’s available, then end up custom-building the other 80% themselves. It’s slow, expensive, and unsustainable.

This 20/80 trap explains why so many initiatives stall. Leaders assume they’re making progress, but the architecture itself keeps them trapped in pilot mode.

With AI-native design, enterprises flip the model. By focusing on automation and machine-to-machine communication, what used to take two years and millions of dollars can now be delivered in a matter of days.

As Craig Turrell explains, “One banking program that would traditionally have required 15 people and a $5 million budget was built in less than a week by orchestrating multiple AI agents.” He continues, “That speed isn’t just about efficiency, it makes it possible to test dozens of ideas without betting the farm.”

What’s Powering This Shift in AI Technologies?

The technology foundations are evolving fast, but three stand out as critical enablers of AI-powered solutions:

1. Agent-to-Agent Collaboration for Smarter AI

Instead of one giant AI model doing everything, organisations are creating ecosystems of specialised AI agents. Each agent has a clear role, validates others’ work, and passes outputs along the chain. This creates trust, auditability, and resilience.

Some enterprises are even registering agents on secure ledgers like blockchain, making it possible to track, verify, and (one day) even pay agents for the work they perform.

2. Unlocking the Hidden Potential of AI Models

Large language models are often treated like chatbots. But in reality, they’ve been trained on far richer frameworks than most people ever use. By communicating in their “native languages” such as semantic web standards, organisations can tap into reasoning power that’s been sitting dormant.

The result? Machines that not only answer questions but also explain why, challenge assumptions, and suggest new pathways. By doing so, businesses can leverage AI far beyond surface-level interactions 

3. Real-Time Experimentation and Automation at Low Cost

In traditional enterprise IT, failure was expensive. A project that failed after two years and millions of dollars was career-ending. In the AI-native world, dozens of failed iterations cost almost nothing, and every failure teaches you something. And because we’re all still at the beginning of this AI journey, those failures aren’t setbacks, they’re the fastest way to learn, adapt, and uncover what really works.

This agility allows teams to optimise quickly, test AI use cases in real time, and uncover the best AI opportunities without betting everything on a single approach.

Cultural Influences and the Human Side of AI Business

Technology is only part of the equation. Culture shapes both how AI behaves and how it’s adopted. For example, ethical AI considerations, cultural influences on AI personality, and workplace trust all impact how people respond to AI systems.

Leaders need to be deliberate in shaping AI culture. That means creating a safe environment where teams can experiment, fail, and learn. It also means ensuring AI aligns with your brand’s values and enhances the customer experience, rather than undermining it.

The future of successful AI implementation isn’t just technical — it’s cultural. Leaders who embrace this shift will unlock new AI opportunities and stay ahead of the competition.

Why Enterprises Still Struggle with AI Adoption

If AI-native is so powerful, why aren’t all organisations doing it? Three barriers come up again and again:

1. Cutting Through the Hype of AI Solutions 
   

   Vendors promise the world. Leaders get dazzled. Then delivery disappoints. Enterprises need consulting services that cut through the noise and deliver solutions that deliver.
   

2. Overcoming Resistance to Automation and Data Analytics

   Teams are comfortable with legacy processes, and asking them to trust “machine-native” workflows feels like a leap into the unknown. At the same time, cultural bias in AI systems can create distrust if users feel the technology doesn’t “speak their language” or reflect their values. Unless organisations streamline operations and embrace intelligent automation, adoption will stall.
   

3. Building the Best Teams for Enterprise AI Success

   Organisations overvalue certificates and titles, when what’s really needed are people who are curious, adaptable, and have the ability to focus on strategic initiatives instead of repetitive work.
   

Until leaders tackle these cultural and mindset barriers, the technology itself won’t save them.

Practical Steps for Implementing AI in Your Enterprise

If you’re responsible for AI in your organisation, here are some practical steps to make the shift:

1. Rethinking Success Metrics in AI Business

Don’t judge pilots purely on output accuracy. Measure how quickly your teams can learn, iterate, and scale. Speed of experimentation is a better leading indicator of success.

2. Blended Teams Unlock the Potential of AI and Analytics

AI-native projects need business minds, designers, and technologists in the same room. The magic happens when someone asks, “But why are we doing it that way?”

3. Treating Data as a Product to Grow With Your Business

Data can’t just sit in warehouses. It needs to be catalogued, enriched, and packaged as products that machines can consume. A strong data strategy ensures the potential of your data is realised. This is the foundation of scalability.

4. Driving Viral Adoption of AI-Powered Solutions

Instead of rolling out in tiny, controlled waves, put early versions into the hands of hundreds of curious users. Let adoption spread seamlessly and watch new ideas emerge.

5. Foster a Learning Culture

Leaders set the tone. If curiosity, experimentation, and continuous learning are rewarded, teams will feel safe to try new approaches. If failure is punished, innovation stalls. The organisations that thrive will be those that treat AI as a learning journey, not a one-off project.

6. Normalising Failure to Unlock AI Opportunities

Don’t punish failed attempts. Celebrate the learning they create. In AI-native, every failure teaches you something valuable about your AI journey

These steps help organisations utilise AI effectively and deliver analytics solutions that grow with your business

AI-Native in Action: Solutions Across Industries

Imagine preparing for an investor Q&A. Traditionally, leaders might guess at likely questions, brief their talking points, and hope for the best.

An AI-native uses a predictive analytics approach. Machine agents scan years of analyst questions across the industry, analyse leadership communication styles, and predict 90% of the questions you’ll face, even suggesting the answers most likely to maintain market confidence.

Or take compliance in financial services. Instead of teams manually cutting and pasting data from a data warehouse into spreadsheets, AI agents catalogue, enrich, and validate data products automatically. What once took 500 man-hours now takes seconds, with higher accuracy and better audit trails.

These aren’t hypotheticals. They’re real world use cases of AI-native design and innovation in some of the most risk-averse industries on the planet. If it can happen there, it can happen anywhere.

The Role of Design in AI

AI-native systems are only as effective as the experiences they create. Business intelligence and AI are converging, but if the output is still endless dashboards, adoption will stall. What people need is design-centric AI that communicates in stories, not spreadsheets.

This is where user experience becomes critical. The way information is presented, whether through narrative summaries, natural language, or visual storytelling, determines how easily humans absorb and act on it. Leaders who embrace design thinking in AI won’t just deliver insights, they’ll deliver clarity and confidence.

The Future of Enterprise AI Technologies

Looking ahead, three big shifts are on the horizon:

1. AI That Feels Invisible but Transforms Your Business

Forget dashboards with endless charts. Future AI-powered systems will surface the one piece of information you need in the moment, then get out of the way.

2. Personalised Intelligence and Smart AI Experiences

Smart AI tailors outputs to how leaders make decisions. Not personalised in a “choose your theme colour” way, but tuned to your cognitive style. For some leaders that means deep data detail, for others, clear narratives and decisions.

3. BI and AI-Powered Analytics Converge

Business intelligence and AI won’t be separate anymore. Together, they’ll deliver stories with data that humans can absorb, remember, and act on. Data and analytics will merge with AI to provide clarity, context, and confidence

For leaders, the signal is clear. Stop bolting AI onto legacy systems. Start building for a world where AI isn’t an enhancement, it’s the foundation.

Enterprises that act now will gain a competitive advantage. Those who delay risk falling behind

Take the Next Step

If your organisation is tired of circling in pilot purgatory, now’s the time to act.

At Dub Dub Data, we help enterprises design AI-native systems that are tailored to your business, scale fast, cut costs, and deliver genuine transformation.

Book your free 30-minute consultation to explore how AI-powered solutions and intelligent automation can transform your business, reduce costs, and help you stay ahead in today's competitive market.

With the right AI strategy, you can finally harness the power of artificial intelligence, unlock the potential of AI, and deliver solutions across industries that are designed to grow with your business. 

Want more insights? Check out our pages on data strategy and intelligent automation.

unDUBBED Podcast - D22 AI for Enterprise: How Craig Turrell is Cracking the Code

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🎙️ Unscripted. Uncensored. Undeniably data.

Summary

In this episode of unDUBBED, hosts Sarah Burnett and Fiona Crocker sit down with Craig Turrell, Head of AI and Design at Standard Chartered Bank, to explore his journey in artificial intelligence. Craig shares how the concept of AI-native architecture is reshaping enterprise adoption and why so many organisations struggle to move beyond endless pilots into meaningful, scaled outcomes.

He also unpacks some of the less-discussed dimensions of AI, from the importance of understanding machine-to-machine communication to the cultural influences that shape AI personality. The conversation highlights the unique challenges of driving innovation in heavily regulated environments, and the critical role of leadership in fostering a learning culture. Craig also looks ahead to the future of business intelligence, where design-centric approaches and user experience will be just as important as technical capability.

Takeaways

• Craig Turrell emphasises the importance of AI native architecture in enterprise settings.
  
• The conversation highlights the disparity between AI hype and its actual implementation in organisations.
  
• Craig's journey showcases the evolution of AI technologies over decades.
  
• The need for organisations to understand machine communication for effective AI deployment is crucial.
  
• Cultural influences shape the personality and responses of AI models.
  
• AI transformations in banking can lead to significant efficiency gains.
  
• Leadership plays a vital role in fostering a culture of learning and innovation in AI.
  
• The future of business intelligence lies in integrating AI with traditional data practices.
  
• User experience design is essential for effective AI applications.
  
• Continuous curiosity and a willingness to learn are key traits for success in AI. 

Chapters

00:00 Introduction to Enterprise AI Transformation

01:40 Craig Turrell's Journey in AI and Innovation

05:17 The Hype Cycle and Reality of AI Implementation

09:10 Understanding Machine Communication and Interaction

12:46 Exploring Machine Personalities and Cultural Influences

18:31 AI Transformations in Banking: A Case Study

26:04 Navigating Innovation in a Regulated Environment

36:17 The Power of Language in Business Communication

37:14 Acceptance of AI in Leadership

39:14 Generative Intelligence vs. Traditional Business Intelligence

40:48 The Importance of Storytelling in Data

42:56 Data Literacy and Its Impact on Decision Making

45:30 Personalizing Business Intelligence for Diverse Needs

46:38 The Future of Business Intelligence

53:54 Leadership and Team Dynamics in the Age of AI

01:01:20 Cultivating a Learning Culture in Organisations

Links

Craig's LinkedIn

Keywords

AI, enterprise AI, AI native architecture, machine learning, data science, banking innovation, leadership in AI, business intelligence, cultural influences on AI, AI communication