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TL;DR

I led the Data and AI capability during and after an acquisition, integrated a distributed team of around twenty people, and built a visible, supportive community of practice. I put in place clear forums, training pathways, reusable delivery materials, and a culture of servant leadership. Across energy and utilities, media, retail, health, and financial services, I reset delivery methods toward agile, product-led data work, improved stakeholder alignment, and raised team confidence and engagement. Evidence includes strong internal feedback, high pulse scores, and the reuse of my discovery, training, and bid materials across the organisation.

Data and AI Lead - Accenture Next Gen Engineering

Context and scope​

Following an acquisition, I was tasked with establishing and growing a Data and AI capability inside a new organisation with different structures and norms. My remit spanned people leadership, practice building, pre-sales support, and hands-on guidance to projects. I spent roughly 75 percent of my time embedded on live engagements, and 25 percent on management and pastoral care for a distributed team of about twenty, adapting time allocation where individuals needed additional support.

My portfolio covered multiple sectors: energy and utilities, media, retail and consumer, health and life sciences, and financial services. Common threads were stakeholder alignment, reshaping delivery towards agile data products, and enabling non-data consultants to work effectively with analytical systems.

What I led​

1) Community building and people development​

  • Leadership team and cadence: I formed a core leadership group from my direct reports, run as a national, remote-first team with intentional in-person time in regional offices. We met fortnightly as a group, and I held monthly 1:1s, increasing frequency for those with personal or delivery challenges.
  • Visible forums and channels: I consolidated fragmented discussion spaces into targeted channels, created a space for people between projects to focus on data training, and nudged discussions into shared forums to reduce silos. Attendance at community events grew across regions.
  • Training pathways: I curated a structured repository with launchpads and practical courses, including dbt, Snowflake, and data modelling, then evolved this into repeatable materials for discoveries and delivery quality.
  • Recognition and motivation: I secured a small budget and launched a public honours list to reward contributors, raise visibility, and reinforce prosocial behaviours.

2) Ways of working and reusable assets​

  • Discovery and delivery playbook: I produced a concise data-offering deck and supporting artefacts that colleagues reused across bids and accounts, providing a common baseline for team shapes, governance, and case studies.
  • Practical exemplars: I built demo projects and example architectures, including platform deployment patterns and semantic search notebooks, to accelerate adoption of good practice.
  • Generative AI enablement: I co-developed internal GenAI capabilities and training, then repurposed materials for external stakeholders as our maturity grew.

3) Cross-organisation collaboration​

  • Bridging silos: I connected teams across multiple internal groups, creating shared bid activity, staffing routes, and a pipeline of skills. I organised data meetups and roundtables that drew participation from across the wider business.
  • Servant leadership ethos: Feedback repeatedly highlighted a coaching-led style that fostered a nurturing environment for professional growth. I used that stance to build trust with stakeholders from delivery teams to senior leadership.

Representative engagements by industry​

  • Energy and utilities: I reoriented teams toward agile, product-centric data delivery, reduced siloed decision-making, and aligned architectural work with product ownership. This improved stakeholder engagement and unblocked key releases.
  • Grid operations: I reviewed approach, team shape, and delivery, then recommended changes to minimise silos and clarify product ownership. Several suggestions were adopted, and I maintained contact to monitor progress.
  • Media: I provided technical heft and credibility in client discussions and internal alignment, collaborating across multiple internal groups to establish confidence in data delivery, while not seeking to change established delivery patterns prematurely.
  • Retail, health, and financial services: I supported discovery workshops, capability reviews, and bid shaping, aligning our competencies to each organisation’s target architecture and operating model, and sharing learnings across accounts.

Outcomes and evidence​

  • Team engagement and growth: High pulse scores and positive feedback for my leadership approach, cited as embodying servant leadership and creating conditions for growth.
  • Stronger delivery foundations: Standardised discovery assets, training repositories, and exemplar projects became go-to references for bids and project kick-offs.
  • Healthier culture: A clearer front door for data work, more cohesive channels, and public recognition mechanisms increased participation and reduced fragmentation.
  • Cross-silo leverage: Reused materials and shared events helped multiple teams present consistent, credible approaches to data products and GenAI across sectors.

What I learned​

  • Make culture intentional: Distributed teams thrive when rituals are explicit, forums are curated, and recognition is visible.
  • Lead by service, not control: Coaching and transparency build trust faster than process alone, especially during post-acquisition change.
  • Standardise the boring bits: Reusable discovery and delivery artefacts create space for teams to focus on the real problem, not framework debates.

What I would do differently​

  • Signal capacity constraints earlier: When priorities collided, I would make trade-offs more explicit and renegotiate scope sooner.
  • Front-door maturity: I would accelerate the build-out of a single, well-advertised intake path for data work to reduce scatter and improve throughput.

Technologies, briefly​

I used technologies only where they amplified team effectiveness and repeatability, for example curated training in established data engineering tools, pragmatic platform exemplars, and practical GenAI enablement. The emphasis was on fit-for-purpose choices, simple patterns, and avoiding reinvention.