AM
ENES

Impact

Programmes are judged on what moved, and by how much.

Four executive case studies: enterprise network transformation, value transformation, AI and autonomous networks, and M&A and investment.

28+

Years of technology leadership

Enterprise scale, end to end

$100M+

Cumulative operational savings

Across enterprise transformation programmes

~450+

Technology professionals led

Distributed, multi-region organization

$30M+

Annual CapEx / OpEx portfolio

Under management, ROI-governed

Business outcomes

Four dimensions the work is judged on.

Technology programmes are easy to describe and hard to assess. These are the axes I hold them to.

01

Enterprise value

  • $100M+ cumulative operational savings
  • $30M+ annual CapEx and OpEx portfolio under ROI governance
  • Platform consolidation that removes licence cost rather than relocating it
  • COTS licence displacement through in-house product engineering
02

Growth

  • Digital platforms and network monetization
  • Customer experience treated as an operational output, not a campaign
  • Time to market shortened through product-oriented engineering
03

Transformation

  • 10+ AI and ML use cases delivered into production, not pilots
  • Network performance data latency from ~12+ hours to ~15 minutes
  • Closed-loop operations running supervised, with Level 3 autonomy as the target
  • Large-scale platform modernization delivered under live production load
04

Leadership

  • ~450+ technology professionals across multiple regions
  • Leadership development and succession built into the operating model
  • Forrester Technology Strategy Impact Award, North America finalist 2025
  • Led technological due diligence and post-merger integrations
Data Platform~12+ hours to under ~15 minutes

~12+ hours of latency, cut to ~15 minutes

Challenge
Legacy platforms, fragmented data and slow operational decision-making.
Leadership
Enterprise transformation strategy across network systems and data.

Set the architecture for the global data platform of Verizon's Network Services Business Unit. Reporting that previously arrived the next working day now lands inside a ~15-minute window, putting network performance decisions back into the same shift as the event.

Outcomes

  • ~15-minute data availability across the network estate
  • Decisions returned to the same shift as the event that triggered them
  • Single integrated platform serving multiple business units
  • Modernization sequenced around continuous availability
Enterprise Value$30M+ annual CapEx/OpEx portfolio under management

$100M+ Value Transformation

Challenge
High-licence COTS platforms and previously siloed business units carrying structural technology cost.
Leadership
CapEx and OpEx portfolios governed on ROI, scalability, and sustainable growth, not headcount growth.

$100M+ cumulative operational savings delivered across enterprise transformation programmes. Savings came from consolidation, in-house build over licensed COTS, and automation of operational toil.

Outcomes

  • COTS licence displacement through in-house product engineering
  • Platform consolidation across previously siloed business units
  • Operational toil removed through automation, not offshoring
AI OperationsMultiple AI use cases in production

AI / Autonomous Network

Challenge
Reactive ticket handling and incident queues in network operations.
Leadership
Leading the implementation of a multi-agent AI model spanning open- and closed-loop operations for network performance.

Multiple AI use cases in production — spanning regulatory compliance, network planning, engineering, and performance operations, built on a multi-agent architecture. The operating model runs from data platforms → ML models → multi-agent architecture → human supervision → closed-loop operations. The models are the visible part; the work was done by data science, platform, and operations engineers who had to agree on what a machine may do unsupervised.

Outcomes

  • Reactive operations → predictive operations → supervised closed-loop operations
  • Working toward Level 3 network autonomy — from today's supervised, closed-loop operations toward autonomous remediation
  • Multi-agent orchestration across open and closed loops
  • Delivered by three disciplines working to one definition of done
M&ADue diligence → Integration → Synergy realization → Investment

M&A / Investment

Challenge
Deciding whether an acquisition thesis survives contact with the estate.
Leadership
Led technological due diligence and post-merger integrations.

Technological due diligence, post-merger integrations, and synergistic platform consolidation, alongside a healthcare investment. Operating experience and capital allocation informing each other.

Outcomes

  • Due diligence: led technological due diligence
  • Integration: led post-merger integrations
  • Synergy realization: synergistic platform consolidation to accelerate time-to-value
  • Investment: invested in a healthcare company and exited after three years

The operating envelope

~70M+

Customers supported by modernized platforms

~40M+

Daily transactions processed

~400 Gbps+

Data throughput sustained

~12+ h → <~15 m

Network-performance data latency, same-shift decisions

10+

AI and ML use cases delivered into production

Level 3

Autonomy target. Supervised closed-loop operations today

What is next

The next number I am interested in moving is somebody else's.

Enterprise transformation, AI-enabled delivery, or a data estate that has outgrown its architecture.

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