EN NO

For Organizational Leaders

Transform How DNB Competes

The Organizational Opportunity

Radical AI isn't just building AI platforms—we're proving a new operating model that fundamentally changes how DNB competes. The question isn't whether AI will transform banking. The question is whether DNB will lead that transformation or follow someone else's playbook.

From Vendor-Dependent to Self-Sufficient

20X Acceleration

Deploy AI use cases in days instead of months

100X Reusability

Build once (knowledge, agents), deploy everywhere

12-18mo Head Start

Competitive advantage over traditional approaches

Traditional vs. DNB's Foundation
Traditional AI
DNB's Foundation
Vendor lock-in
Dependent on 2018-era chatbot technology
Complete ownership
Use any AI model (GPT, Claude, proprietary)
6-12 months per AI use case
Custom development from scratch
Days/weeks per use case
Template-driven agent deployment
Every department rebuilds the same capabilities
Build once, deploy everywhere
100X multiplier on reusability
Proof-of-concepts never reach production
Fast iteration from concept to production
Governance built-in

The Operating Model Challenge

Most organizations struggle with what we call "loose-tight-loose" management:

  • Loose goals: Unclear outcomes, conflicting priorities
  • Tight execution: Over-prescribed how work should be done
  • Loose follow-through: Weak measurement of whether it worked

AI-speed development requires "tight-loose-tight":

  • Tight goals: Crystal clear outcomes and success criteria
  • Loose execution: Autonomy on how to achieve outcomes (trust small teams)
  • Tight follow-through: Rigorous measurement and learning

What This Means for Radical AI

If we operate in "loose-tight-loose" mode:

  • We'll deliver incremental proof points but miss the transformation opportunity
  • We'll spend more time aligning stakeholders than building
  • We'll prove AI works for features but not for organizational change

If we operate in "tight-loose-tight" mode:

  • We can move at AI-speed (days, not months)
  • We can demonstrate a new model that DNB can scale
  • We can deliver both customer value AND transformation

The Strategic Impact

Competitive Advantage

DNB builds what competitors will buy from vendors

  • 12-18 month head start
  • Full control over roadmap
  • Ability to innovate at AI speed

Organizational Efficiency

1-2 person teams deliver what takes 20+ today

  • Faster delivery (days vs. months)
  • Lower coordination overhead
  • Higher autonomy and ownership

Market Differentiation

DNB recognized as AI-first bank

  • Customer experience that competitors can't match
  • Talent attraction (engineers want to work here)
  • Industry leadership in AI adoption

Multiplier Effect

Foundations enable every AI initiative

  • Factflow: 100X knowledge reuse
  • Agent Prism: 100X agent deployment
  • Compound value with every new use case

What Needs to Change

Protected Space to Operate at AI-Speed

Radical AI needs decision autonomy on execution (with clear outcome accountability)

Specifically:

  • Streamlined stakeholder engagement (advisory, not approval-based)
  • Trust small teams to deliver, measure outcomes, iterate
  • Clear decision rights: What Radical AI can decide autonomously vs. what needs approval

Why this matters:

  • If we continue as-is: We'll prove AI works incrementally but miss the transformation opportunity
  • If we adjust: We can demonstrate a new operating model that DNB can scale

The Clear Mandate We Need

Mission

Radical AI is a transformation lab. We prove that AI fundamentally changes product development at DNB. We build shared infrastructure and demonstrate a new operating model.

Scope

We build bank-wide platforms (Factflow, Agent Prism) that enable AI adoption across DNB. We do not own individual product features long-term—we build, prove, and transition.

Delivery Model

We ship customer value as proof points, not as the primary output. Weekly deliverables validate that our platforms work; the platforms themselves are the transformation.

Operating Principles

  • Small teams: 1-2 people per initiative, AI-augmented capabilities
  • Fast cycles: Days to ship, not months
  • Strong governance: Automated compliance, encoded architecture, exception-based reviews
  • Measured outcomes: Speed, quality, customer impact—not activity or process compliance

Success Criteria

We succeed when:

  • Other teams adopt our platforms (Factflow, Agent Prism)
  • We demonstrate measurable efficiency gains (smaller teams, faster delivery)
  • DNB leadership commits to scaling the operating model
  • Digital channels are reimagined based on what AI makes possible

The Bottom Line

Vision: Personal Banking Assistant transforming how customers interact with DNB

Strategy: Three-pillar foundation providing knowledge, orchestration, and evaluation

Impact: 20X+ acceleration enabling rapid use case deployment

Advantage: 12-18 month head start over traditional approach

What Happens Next → Technical Details →