With Elena Alikhachkina — 4x Chief AI & Data Officer and Board Advisor

What does it really take to move from data projects to data products?

In this episode, Ben Parker speaks with Elena Alikhachkina about one of the biggest shifts happening across Data and AI and why technical expertise alone is no longer enough.

Drawing on more than 25 years in the industry, Elena explores how organisations can build more customer-focused, commercially relevant Data and AI products through stronger product thinking, business understanding and collaboration.

You’ll hear practical insights on:

• Why Data and AI teams need to think in products, not projects 
• How to connect technical work to business outcomes 
• Why product skills are becoming essential in AI 
• Bridging the gap between business and technology 
• The growing importance of communication and commercial awareness 
• The skills future Data and AI leaders need to develop 

Chapters

00:00 Introduction
01:33 Elena’s career and leadership journey
09:17 From data projects to data products
15:06 Building a product mindset in Data & AI
22:34 The skills Data & AI professionals need next
29:50 Bridging business and technology
34:00 Turning product thinking into business value

Thank you for listening! 

With Phoenix Pei — SVP, Analytics Manager at Truist

What will the Data Scientist and Data Engineer of the future look like?

In this episode, Ben Parker speaks with Phoenix Pei about how AI and automation are changing data roles — and why technical expertise alone may no longer be enough.

Phoenix explores the growing importance of business understanding, trust and leadership alignment, why many data initiatives still struggle to create meaningful impact, and how organisations may need to rethink the structure of their data teams.

You’ll hear practical insights on:

• How AI and automation are changing Data Science and Data Engineering 
• Whether the future belongs to specialists or full-stack data professionals 
• The technical, business and leadership skills that will matter most 
• Why so many data initiatives struggle to deliver business value 
• What prevents Data Science projects reaching production 
• How Data Scientists and Data Engineers will work together in the future 

Chapters

00:00 Introduction
02:18 Phoenix’s career and leadership journey
09:34 How AI is changing Data Science & Engineering
11:08 Why business understanding matters more than ever
24:54 Why Data Science initiatives struggle to deliver
25:07 The importance of leadership alignment
33:53 Preparing Data teams for the future

Thank you for listening! 

With Durai Rajamanickam — Senior AI Leader

How do leaders make better decisions about AI when the technology, risks and expectations are changing so quickly?

In this episode, Ben Parker speaks with Durai Rajamanickam about what it takes to turn AI ambition into something organisations can trust, scale and create value from.

They explore why AI initiatives can go wrong before technology is even the problem, the danger of hype-driven decisions, and why clear business objectives and leadership alignment matter.

The conversation also examines build vs buy, balancing speed with governance, when leaders should trust AI outputs, and the decisions organisations can't afford to delay.

You’ll hear practical insights on:

• Why organisations misdiagnose the problems they want AI to solve 
• How to make better build-vs-buy decisions 
• Why promising AI initiatives fail 
• Balancing speed, innovation, governance and trust 
• When leaders should trust or challenge AI outputs 
• The AI decisions organisations need to make now 

Chapters

00:00 Why AI strategies go wrong
01:09 Meet Durai Rajamanickam
03:46 Build vs buy in AI
05:36 Avoiding hype-driven AI decisions
07:41 Aligning AI with the business
09:11 Building trust and governance
12:22 Balancing speed with control
15:22 Making better decisions with AI
21:03 Advice for AI leaders

Thank you for listening! 

With Nayan Paul — Managing Director & Chief Architect, Generative AI at Accenture

Why are so many organisations experimenting with Generative AI, but so few turning it into meaningful business impact?

In this episode, Ben Parker speaks with Nayan Paul about what it really takes to move GenAI from experimentation into production and scale.

They explore why successful adoption isn't simply a technology challenge. It requires business ownership, the right operating model, strong data foundations and a clear approach to governance.

The conversation also examines how organisations can move quickly without sacrificing trust and responsibility and what separates AI experimentation from genuine business transformation.

You’ll hear practical insights on:

• Why GenAI pilots struggle to reach production 
• Moving from experimentation to measurable business value 
• Why business ownership matters as much as technology 
• Building the foundations for GenAI at scale 
• Creating an effective AI operating model 
• Balancing speed, governance and responsibility 
• Turning GenAI from an experiment into an organisational capability 

Chapters

00:00 Why scaling Generative AI is difficult
01:08 Meet Nayan Paul
05:08 Early GenAI experiments and lessons
07:02 Moving from experimentation to business value
10:14 Driving adoption across the business
16:15 Building the foundations for AI at scale
29:24 Balancing speed with responsibility
34:09 Moving from curiosity to impact

Thank you for listening! 

With Sujit Narapareddy — Head of Data & Analytics, AWS Sales

What separates organisations experimenting with AI from those actually changing how work gets done?

In this episode, Ben Parker speaks with Sujit Narapareddy about what it really takes to embed AI into an organisation and why technology is only part of the challenge.

Sujit explores the importance of human judgement, strong data foundations and leadership alignment, alongside the organisational changes required to move from AI experimentation to real adoption.

The conversation also examines how AI could reshape everyday work by embedding intelligence directly into workflows, helping people move faster from insight to action without removing the need for human judgement.

You’ll hear practical insights on:

•  Why organisations underestimate what AI adoption really requires 
•  What separates AI experimentation from real adoption 
•  Why strong data foundations still matter 
•  How AI can complement people rather than simply replace roles 
•  How leaders should rethink teams and decision-making 
•  Why human judgement becomes more important, not less 
•  What organisations should be doing now to prepare 

Chapters

00:00 The challenge of AI transformation
01:42 Meet Sujit Narapareddy
02:32 Sujit’s journey from technology to leadership
09:21 How AI changes human roles
16:17 Why organisations struggle to integrate AI
28:28 Preparing organisations for what comes next

Thank you for listening! 

With Carlos Pineda — Head of Data Analytics & Insight, Diageo North America

Where is AI actually creating value today and where are organisations still getting distracted by the hype?

In this episode, Ben Parker speaks with Carlos Pineda about the reality of implementing AI inside large organisations and what separates experimentation from meaningful business transformation.

Carlos explores why successful AI starts with understanding the business problem, having the right data foundations and embedding AI into real processes rather than simply adopting the latest technology.

The conversation also covers leadership, stakeholder engagement, experimentation and why organisations need to think about AI transformation end-to-end if they want to create lasting value.

You’ll hear practical insights on:

• Where AI and GenAI are creating genuine business value today 
• Why AI initiatives need to start with the business problem 
• The importance of strong data foundations 
• Why business understanding matters alongside technical expertise 
• What prevents organisations from successfully integrating AI 
• How experimentation can lead to scalable transformation 
• How leaders should evaluate the cost and potential value of AI 

Chapters

00:00 The reality of AI today
01:53 Meet Carlos Pineda
02:51 Carlos's career and leadership journey
08:02 Where AI is actually creating value
09:58 Why business understanding matters
13:13 The challenges of implementing AI
30:38 Understanding the cost and value of AI
33:39 Final thoughts

Thank you for listening! 

With Misha Trubskyy — Head of Claims Data Science, Mercury Insurance

Why is hiring exceptional AI and Data talent still so difficult even in a market full of candidates?

In this episode, Ben Parker speaks with Misha Trubskyy about what organisations are getting wrong when hiring, assessing and retaining AI and Data professionals.

Drawing on his experience leading Data Science in insurance, Misha explores the disconnect between companies struggling to find the right skills and candidates who feel hiring processes have become too selective.

The conversation examines what leaders should really look for beyond technical ability, why critical thinking and authenticity matter, and what organisations need to do differently to keep their strongest people once they've hired them.

You’ll hear practical insights on:

• Why AI and Data talent remains difficult to hire 
• The disconnect between employers and candidates 
• What actually separates exceptional candidates 
• Why technical skills alone aren't enough 
• How to assess critical thinking and real-world capability 
• The case for investing in junior talent 
• What keeps top AI and Data professionals from leaving 
• How the talent market could evolve over the next few years 

Chapters

00:00 Introduction
02:24 Misha's career and leadership journey
07:37 Lessons in leading Data Science teams
29:52 Why hiring AI & Data talent is so difficult
37:02 What to look for beyond technical skills
44:21 What's happening in the talent market
48:55 Why organisations should invest in junior talent
01:04:33 How to retain top performers
01:10:03 The future of AI & Data hiring

Thank you for listening! 

With Carol Kim — Executive Director, IBM

Can an organisation really be data-driven if its data can't be trusted?

In this episode, Ben Parker speaks with Carol Kim about why Data Governance and Data Quality are fundamental to better decision-making and what happens when organisations fail to get them right.

Carol explores how poor-quality data affects far more than technology, creating hidden costs across decision-making, operations and the wider business.

Drawing on her journey from finance into technology and Data and AI leadership, Carol also discusses the importance of curiosity, storytelling and authentic leadership — and the lessons she's learned navigating transformation across different cultures and environments.

You’ll hear practical insights on:

• Why trusted data is essential for better decision-making 
• The hidden business costs of poor Data Quality 
• Where Data Governance typically breaks down 
• How people, process and technology need to work together 
• What organisations need to build effective Data Governance 
• Why storytelling matters for Data leaders 
• The importance of curiosity and continuous learning in leadership 

Chapters

00:00 The importance of continuous learning
02:01 Carol's career and leadership journey
07:54 Why storytelling matters in Data
09:59 Leadership and career transformation
15:13 Why Data Governance & Data Quality matter
24:06 Final thoughts

Thank you for listening! 

With Ilya Meyzin — SVP, Head of AI Solutions at Dun & Bradstreet

Are AI agents genuinely the next major shift in enterprise AI or are many still sophisticated workflows wrapped around LLMs?

In this episode, Ben Parker speaks with Ilya Meyzin about what AI agents can actually do today and what it takes to turn experimentation into real business value.

Ilya explores how agents differ from traditional AI systems, where the strongest use cases are emerging, and why many promising pilots struggle when organisations attempt to scale them.

The conversation also examines data quality, enterprise architecture, cross-functional collaboration and whether AI agents will become a fundamental part of how organisations operate.

You’ll hear practical insights on:

• What AI agents can do that traditional LLMs cannot 
• How to identify genuinely valuable agentic use cases 
• Why AI agent pilots struggle to scale 
• The difference between orchestration and genuine intelligence 
• Why data quality remains critical 
• How teams need to collaborate to deploy agents successfully 
• Whether agents will become embedded in enterprise architecture 

Chapters

00:00 Introduction
01:14 Ilya's career and leadership journey
14:45 Balancing technical expertise with business understanding
23:18 What AI agents actually are
28:40 Where AI agents create real value
32:14 Building and scaling agents in the enterprise
37:21 Why data quality matters
45:12 The future of AI agents

Thank you for listening! 

With Allison Olson — SVP, Analytics Solutions at Merkle

How much of what we buy is influenced by AI without us even realising it?

In this episode, Ben Parker speaks with Allison Olson about how AI and real-time data are transforming personalisation and changing the way brands understand, influence and interact with their customers.

Allison explores the shift from static recommendations to dynamic experiences that adapt to customer behaviour in real time, the data required to make this possible, and where personalisation can cross the line from helpful to intrusive.

The conversation also examines how Generative AI could transform customer experiences further and what the next generation of personalisation might look like.

You’ll hear practical insights on:

• How AI influences the decisions customers make 
• The shift from static to dynamic personalisation 
• How real-time data creates adaptive customer experiences 
• Balancing personalisation with privacy and trust 
• The challenges of personalising experiences at scale 
• How Generative AI could reshape customer interactions 
• What the future of personalisation could look like 

Chapters

00:00 Introduction
02:12 Allison's career and leadership journey
08:37 Learning from failure and taking risks
13:45 Lessons in leadership
21:01 How AI is changing personalisation
23:23 Static vs dynamic personalisation
30:42 Balancing personalisation with privacy
35:12 Delivering personalisation at scale
38:16 How Generative AI changes what's possible
43:57 Final thoughts

Thank you for listening!