Experienced large-scale finance transformation leader, supporting enterprises in translating transformation strategy into execution in complex, regulated environments.

My expertise spans finance transformation toward intelligent operating models, with AI and automation embedded selectively to create measurable, sustainable value while maintaining governance and control at scale.

I focus on simplifying complex finance environments, modernizing processes, strengthening data foundations, and embedding governance early to ensure resilience under regulatory pressure, organizational change, and growth.

I have led and contributed to multi-region programs, including post-ERP transformations and post-acquisition integrations, where consistency, control, and execution discipline are critical to success.

My background combines operational excellence and advanced technology. I hold a Lean Six Sigma Black Belt certification and bring hands-on experience across applied AI, RPA, operational analytics, and enterprise data environments, delivering improvements in financial performance, efficiency, and resilience.

I place strong emphasis on data governance and AI governance, with a focus on transparency, accountability, and decision integrity to support trust, compliance, and long-term performance.

I hold an MSc in Applied Artificial Intelligence and am currently pursuing an MBA with a focus on AI Project Management.

AI positioned as a governance or compliance solution needs a different evaluation lens
because the risk profile is fundamentally higher.

The market is crowded with AI tools claiming to enable governance, compliance, and data protection.

→ “Privacy-first”
→ “Security-first”
→ “Compliance-by-default”

These claims do not lower the bar.
They raise it.

When an AI tool handles sensitive or regulated data, approval is no longer technical.

It is a governance decision.

The bar for governance-positioned AI is higher, not lower.

If a tool:

→ Processes documents
→ Touches sensitive or regulated data
→ Positions itself as a compliance or privacy enabler

Then due diligence becomes mandatory, not optional.

What executive teams must verify before approval,

Data traceability

→ You can clearly explain where data is processed
→ You understand how it flows and under which legal basis
→ Transfers are lawful, certified, and defensible under GDPR and EU AI Act-style regimes

If you cannot explain this simply, you cannot approve the tool.

Verified integrations, not assurances

→ Identity, access, OAuth, and SSO are production-proven
→ Logging and audit trails exist in real customer environments
→ “In progress” and “under review” are risks, not features

Governance by default, not by exception

→ Data retention, encryption, breach notification, and continuity are transparent upfront.
→ Governance does not rely on custom clauses or post-demo discovery.

If governance needs exceptions, it is not governance by design.

Alignment between promise and reality

→ A tool claiming to reduce compliance risk must pass compliance scrutiny itself.
→ If it cannot survive the same review as your internal systems, risk is not removed.
→ It is merely relocated

AI accelerates decisions.
Executive accountability does not.

When things fail, regulators look to boards and leadership, not vendors.

The real risk today is not using AI.

It is delegating governance to tools that have not earned that responsibility.

For leaders accountable for data, compliance, and reputation, caution is not resistance.

It is what allows innovation to scale without breaking trust.

Joanna Miler

Finance Transformation Strategy | Intelligent Operating Models | Governed AI for Business Outcomes

I have completed my Master’s in Applied Artificial Intelligence.

The last two months moved fast. Between stepping into my role at Capgemini and the pace of ongoing work, I didn’t pause to mark this milestone.

This was not a theoretical program.

It was a technically rigorous, hands-on journey focused on how AI operates in real business environments.

Core areas covered:

→ Machine Learning and Deep Learning
→ Natural Language Processing and Generative AI
→ Computer Vision and Recommendation Systems
→ Time Series Analysis and Data Science
→ AI Platforms and applied implementation in Python

What mattered most to me was not learning about AI.

It was learning how to design, evaluate, and govern AI systems that work in complex organizations.

My capstone combined classical ML and GenAI, with a strong focus on operational feasibility, accountability, and business relevance.

This journey deepened my technical foundation and sharpened my ability to bridge technology, operating models, and leadership responsibility.

I am grateful for the discipline it required, and ready to keep building.

85% of AI transformation projects fail—not because of bad technology, but because of poor implementation.

AI is not just a buzzword. It’s a business tool—but only if it’s used right.

I am an emerging AI transformational leader helping companies bridge the gap between AI strategy and real business transformation. 

Whether it’s a global blue-chip organisation struggling with automation or a fast-moving startup looking for scalable AI solutions. 

I turn AI into a practical efficiency, compliance, and growth tool.

🔹 AI Adoption & Digital Transformation 

→ Most companies struggle to integrate AI without disrupting operations.

 → I provide strategic AI guidance to ensure measurable ROI and business impact.

🔹 Process Optimisation & Automation 

→ Outdated, manual processes cost time and money. 

→ I specialise in automating Finance (O2C), Customer Service, and Data Governance using AI to enhance efficiency and reduce costs.

🔹 AI Ethics, Compliance & Accountability 

→ AI should be transparent, fair, and compliant. 

→ I help businesses establish ethical AI frameworks to mitigate risk and build stakeholder trust.

With leadership experience in Fortune 500 companies (Johnson Controls, UPM, Arvato) and high-growth tech startups (Oyster), I’ve seen firsthand what works—and what doesn’t—when it comes to AI transformation.

Why Work With Me? 

✅ I don’t just design AI roadmaps—I make AI work for your business. 

✅ I combine strategic vision with hands-on implementation. 

✅ I ensure AI initiatives align with ethical guidelines and compliance standards.

I am currently pursuing a Master’s in AI, with a focus on applied AI, including the development and optimisation of AI models to solve real-world business challenges.

Ready to turn AI into a competitive advantage? 

DM ‘AI Transformation’ now.

EU AI governance is not “EU AI Act + done”
because the EU runs AI through a stacked control regime.

If you operate in the EU, the real question is not:
“Which AI Act category are we in?”

It is:
“Which EU control stack are we willing to run and defend?”

The moment AI touches finance, risk, customer outcomes, or critical operations, multiple layers apply at once:

→ EU AI Act: risk-based obligations

→ GDPR: profiling, automated decisions, lawful basis, data subject rights

→ NIS2: cyber and resilience expectations for many entities

→ DORA: resilience rules for regulated financial firms and ICT dependencies

→ Sector regulators + national enforcement: different tolerance by industry and member state

And that is before the wider digital stack adds more constraints.
Now the uncomfortable part.

You are already choosing a control approach, even if you are not calling it a strategy.

Pick your posture:

Option A: AI Act-only

→ Treat everything else as someone else’s problem.

→ Let conflicts surface through audits, incidents, or customer escalations.

Option B: Layered compliance

→ Assume overlap and build controls across privacy, security, and resilience.

→ Accept friction in exchange for defensibility.

Option C: Control-first design

→ Assume the stack will keep expanding.

→ Build AI to be inspectable, interruptible, and accountable by default.

None is universally “right.”

But treating the EU AI Act as the whole story creates unowned risk fast.

So the leadership question becomes:

Which EU control stack are we prepared to operate
and who is willing to sign their name under it?

Most “AI-powered” Q2C systems would fail an audit in one week.

If your AI touched revenue today, it would probably break trust before it improves margin.

Production-ready AI in Quote-to-Cash is not about smarter models.

It is about building the right layers in the right order.

Here are the 7 layers that actually make AI usable in Q2C.

1. Clean data

If pricing, contracts, and orders disagree, AI will confidently recommend the wrong thing.

2. Stable processes

AI only follows rules.

If approvals and handoffs are unclear, mistakes scale faster.

3. Business signals

Margins, discount risk, delays, and exceptions must be clearly defined.
Raw data alone is noise.

4. Explainable logic

If sales and finance cannot understand a recommendation, they will ignore it.

5. Embedded actions

AI must live inside CRM and CPQ.
Insights without action do not change outcomes.

6. Human control

Overrides, thresholds, and escalation paths are mandatory.
Revenue decisions always need accountability.

7. Governance and audit

Every decision must be traceable and compliant.

This is what keeps AI alive after pilots.

Skip one layer, and the system looks smart but fails in production.

Quote-to-Cash does not reward hype.
It rewards structure.

Which layer is missing in most Q2C AI programs you have seen?

The first “perfect” model I shipped failed quietly.

The validation metrics were strong.

Accuracy was high.
Stakeholders were confident.
Production exposed everything we missed.

For the first few days, nothing looked wrong.
Predictions were flowing.
Dashboards were green.

Then outcomes started drifting.

Not because the model was broken.
Because reality had moved.

Customer behavior shifted.
Input distributions changed.
Edge cases became the norm.

The model was still accurate by yesterday’s standards.

It was irrelevant by today’s conditions.

That moment changed how I think about machine learning.

A model does not fail when accuracy drops.

It fails when nobody notices why performance is changing.

What I learned the hard way:

✔ Validation is a snapshot, not a guarantee
✔ Production is a living environment
✔ Drift is inevitable, silence is optional

Monitoring is not a technical add-on.

It is the difference between confidence and control.

Today, I care less about perfect metrics at launch
and far more about visibility after deployment:

→ input drift
→ output stability
→ decision impact
→ alerting before trust erodes

Models age.
Environments evolve.
Monitoring is what keeps intelligence relevant.

The real failure was not the model.

It was assuming “done” existed in production ML.

Invoice disputes do not begin in SAP.

They begin quietly inside customer emails that finance teams usually ignore.

Below is a clean case card, written in simple, direct points.

Case: Early dispute detection using NLP on emails

Business problem:

• Invoice disputes appear late in SAP, after cash is already delayed.

• Finance teams only react once the dispute is officially logged.

Hidden risk signal:

• Customers express concern days earlier through email language.

• These messages reach AR teams but are treated as routine communication.

What NLP checked:

✓ Phrases indicating confusion or disagreement on charges.

✓ Mentions of incorrect pricing, missing credits, or contract mismatch.

✓ Negative or uncertain tone combined with billing keywords.

How the system worked:

• All inbound finance emails were scanned in real time.

• Each email received a dispute-risk score based on language and intent.

• High-risk emails triggered alerts before any SAP dispute was created.

Action taken early:

✓ Finance clarified invoices proactively
✓ Sales and billing aligned before escalation
✓ Customers received responses before frustration built up

Result:

• Fewer formal disputes in SAP
• Faster collections and improved cash flow
• Reduced friction between customers, sales, and finance

Core insight:

Disputes start as language, not transactions.

AI that listens early prevents problems that systems only see too late.

Where else in your Q2C flow are early signals being missed?

Finance teams do not move to the cloud because it is fashionable.

They move because fragmented ERPs, weak controls, slow closes, and regulatory pressure leave no alternative.

Once the move is decided, a tougher question appears.

Does the cloud provider actually matter?

The answer is yes, but not for the reasons vendors usually sell.

All three platforms offer the basics.

Compute. Storage. Security frameworks. Automation. ML.

The real differences show up in architecture, integration, automation, and governance.

Those choices quietly shape how finance actually operates under SOX, IFRS, and sector regulations.

What finance teams really get

AWS

→ High flexibility and event-driven automation
→ Strong for complex, multi-ERP environments
→ Fits cash application, reconciliation, credit scoring at scale
→ Best when customization and global control matter more than simplicity

Azure

→ Natural fit for Microsoft-centric finance teams
→ Tighter alignment with ERP, identity, access, and reporting
→ Strong auditability and access control
→ Works well under SOX-style internal control frameworks

Google Cloud (GCP)

→ Clear strength in analytics and data platforms
→ BigQuery and native ML enable fast insights
→ Strong for FP&A, forecasting, anomaly detection, and risk models
→ Suits finance teams driven by data-led decisioning

Where the differences really show up:

→ Credit risk and scoring
→ Cash application and reconciliation
→ Fraud and anomaly detection
→ Collections and dispute prevention
→ FP&A and scenario modeling

Different processes reward different strengths.

That is why many large organizations go hybrid by design, not by accident.

Finance transformation does not start with AI or tools.

It starts with architecture.

Cloud will not solve every problem.

But it gives finance something it has rarely had:

A consistent, scalable foundation to run the next decade of operations on.

Cybersecurity used to be treated as a technology problem
and AI turns it into a control and accountability problem.

AI introduces a new class of risk because it creates systems that act, not just systems that store data.

Three shifts most companies underestimate

1) The attack surface moves from networks to decisions

→ Prompt injection, poisoned inputs, and manipulated retrieval can steer outcomes.

→ “Secure” becomes decision integrity, which means you must trust actions under adversarial pressure.

2) Identity becomes the perimeter, especially for AI agents

→ The key question is not “is the model safe?”

→ The key question is what this AI identity can access, execute, approve, and escalate.

→ Permissions, logs, and human overrides become core security controls.

3) Vendor risk becomes operational risk

→ Every AI tool adds integrations, data flows, and hidden dependencies.

→ If you cannot trace what it connects to, what it reads, and what it can trigger, you cannot control it.

→ If you cannot control it, you will not be able to defend it after an incident

The uncomfortable takeaway is simple.

AI does not just increase cyber risk.

It changes what “secure” even means.

So the leadership question is no longer:
“Are we protected?”

It is:
“Can we prove control over AI-driven actions before an incident proves we cannot?”

AI stops being neutral the moment it touches money.

In Q2C, that moment comes faster than most teams expect.

When AI influences credit decisions, disputes, or collections, it becomes a decision system with legal, financial, and reputational consequences.

At that point, the real question is no longer:
“Does the AI work?”

It becomes:
“Are we entitled to let it decide?”

What many teams miss is this simple truth.

AI governance in Q2C is sector-driven.

The same AI capability can be acceptable in one industry
and unacceptable in another
because accountability and impact are not equal.

How sector context changes what is allowed

Financial Services

→ Decisions must be explainable, auditable, and reversible
→ If you cannot defend it to regulators, it should not exist

Healthcare & Pharma

→ Governance prioritizes risk containment and human authority
→ AI can support decisions, not replace final control

Manufacturing & Industrial

→ Governance depends on causality and commercial fairness
→ Unexplained deductions or dispute outcomes destroy trust

B2B SaaS & Subscriptions

→ Governance sits at contracts, entitlements, and revenue recognition
→ A wrong collections action can trigger churn or misstatement

Retail & Distribution

→ Volume amplifies risk
→ Without thresholds, AI quietly erodes consistency and control

What invalidates AI here is not model accuracy.
It is missing sector-appropriate governance.

The same use case carries different consequences based on:

→ Regulatory exposure
→ Customer vulnerability
→ Auditability requirements
→ Tolerance for automation
→ Clarity of human accountability

That is why generic AI governance fails in Q2C.

The leadership question is not:

“Is the AI accurate?”

It is:

“Are we prepared to own the decisions this AI makes, in our sector, under our obligations, with our customers?”

Without that answer, AI has no legitimate place in credit, disputes, or collections.

“You have to run as fast as you can just to stay in the same place.”

That line from Through the Looking-Glass captures what I see in AI transformation work today.

The challenge is not speed. 

It is trying to meet exponential change with linear operating models.

1. Effort does not move AI outcomes. Structure does.

Across operations, the pattern repeats:

→ More reports → same delays
→ More people → same bottlenecks
→ More sprints → same drag

❌ Activity increases
❌ Pressure rises
❌ Outcomes stay flat

2. AI works only in processes designed for intelligence

In legacy processes:

❌ Decision logic is informal
❌ Ownership is fragmented
❌ Data reacts instead of predicts

AI delivers marginal gains.

After redesign:

✔ Decisions are explicit
✔ Ownership is clear
✔ Data drives action

Impact scales.

3. What actually moves the needle

✔ Unify the process before scaling AI
✔ Redesign decision architecture

✔ Shift work:
→ AI handles volume
→ Humans handle exceptions

This redistributes 50–70% of the effort.

The Red Queen lesson for AI

Progress does not come from running faster.

It comes from:

→ eliminating work AI should own
→ redesigning processes for intelligence
→ moving people into oversight and judgment

That is when organizations move forward.

Without it, they stay in place- no matter how hard they run.

If your product can be reverse-engineered by observing behavior, it is not a secret.

because AI has made behavioral cloning fast, cheap, and scalable.

AI does not need to break encryption or steal source code.

AI just needs to watch the system work.

→ Observe inputs
→ Observe outputs
→ Infer rules, thresholds, workflows, and decision logic
→ Replicate how you score fraud, route access, or prioritize alerts

What used to take months now takes weeks, and sometimes days.

That creates one uncomfortable question for product and security leaders.

→ What exactly is your IP protected by?

If your answer is any of these, your advantage is already shrinking:

✅ Features
✅ Workflows
✅ UI logic
✅ Decision behavior

In places like the EU, reverse engineering a lawfully acquired product can be lawful in many cases, especially for interoperability or error correction, as long as contract terms and confidentiality obligations are not breached.

AI does not change the legal baseline.

AI changes the speed and scale of exploitation.

So you are already choosing an IP posture, even if you are not calling it that.

Pick your posture:

Option 1: Feature-based IP

→ You win by shipping first.
→ You accept fast imitation as the cost of growth.

Option 2: Trade-secret-dependent IP

→ You hide logic, tuning, thresholds, and heuristics.
→ You bet they cannot reliably infer it from behavior or side channels.

Option 3: Evidence-based IP

→ You assume features and behaviors will be copied.

→ You compete on what cannot be cloned quickly:

✅ Auditability
✅ Data provenance
✅ Governance controls
✅ Certifications and trust posture
✅ Service quality and operational maturity

AI makes one thing clear.

If your “secret sauce” can be reconstructed from how the system behaves, it is not a secret.

It is a temporary asymmetry.

So the leadership question is no longer:
“Is our IP protected?”

It is:
“What would still differentiate us if someone copied our behavior, and we had to prove trust?”

The hardest trade-off I have managed was between customer empathy and collections performance.

For a long time, it felt like a forced choice. Be understanding and miss numbers. Push numbers and damage trust.

That framing was wrong.

The real issue was the absence of structure.

Here is what I saw repeatedly.

• Customers had real reasons for delays.
• Collections teams treated every delay the same.
• Empathy turned into open-ended patience.
• Performance pressure turned into friction.

Nothing improved because clarity was missing.

What we changed:

✔ We stopped treating all late payments as equal.
Cash-flow issue ≠ approval delay ≠ invoice confusion.

✔ We acknowledged the situation once, clearly and respectfully.
Empathy was explicit, not emotional.

✔ Every conversation ended with a next step.
Owner. Date. Action.

✔ We changed the question.
From: “Can you pay?”
To: “What needs to happen for payment to be released?”

What happened next:

✔ Customers felt respected, not chased.
✔ Collection cycles shortened.
✔ Escalations reduced.
✔ Trust actually increased.

The lesson was uncomfortable but important.

• Empathy without structure creates avoidance.
• Performance without empathy creates resistance.

The best results came from designing the middle ground.

Clear expectations. Human conversations. Firm timelines.

Where have you seen empathy help performance once structure was put in place?

“When will AI take over the world?” is the wrong question.

After completing a Master’s in Artificial Intelligence, that question still comes up all the time.

I get why. Generative AI sounds human, and the hype is loud.

But the more technical you get, the less convincing that fear becomes.

AI is not intelligence in the human sense.

It is mathematics and statistics operating at scale.

Models predict likely outputs from patterns in data. They do not understand meaning, form intent, or exercise judgment.

That is why AI can sound confident and still be indifferent to truth, ethics, and context.

The real limitation is dependence.

AI does not “notice” when the world changes.

AI keeps producing outputs until humans correct the frame.

When inputs degrade, outputs degrade.

✅ Incomplete or biased data produces incomplete or biased decisions.

✅ Data drift and concept drift quietly break performance over time.

✅ Poorly defined problems create confident but wrong answers.

✅ The system does not question assumptions unless we design controls.

This is why governance matters more than imagination.

The real AI conversation should be about:

→ Data governance and quality
→ Debiasing, weighting, and balancing
→ Continuous monitoring and drift detection
→ Clear human accountability for decisions
→ Retraining strategy and model lifecycle controls

AI will not take over the world.

But people can over-delegate decisions to systems they do not supervise properly.

AI works best as an augmentation.

✅ Humans define the problem clearly.
✅ Humans control the inputs and constraints.
✅ Humans monitor outputs over time.
✅ Humans stay responsible for the final decision.

So a better question than “When will AI take over?” is this:

“Do we know what we have already delegated to AI, and do we have the discipline to stay in control as the world changes?”

taplio

Joanna Miler's Linkedin Analytics

Joanna Miler

Performance ScoreJoanna Miler's LinkedIn Performance

Joanna Miler's Best Posts (last 30 days)

Ready to grow your LinkedIn presence?

taplio

.css-6ytdur{background-image:linear-gradient(284deg, rgba(39, 43, 72, 1) 0%, rgba(33, 47, 183, 1) 100%);color:transparent;-webkit-background-clip:text;background-clip:text;}Joanna Miler's Linkedin Analytics

Joanna Miler

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Joanna Miler's LinkedIn Performance

Joanna Miler's Best Posts .css-1emoo84{font-size:var(--chakra-fontSizes-md);font-weight:400;color:var(--chakra-colors-gray-600);}(last 30 days)

Ready to grow your LinkedIn presence?

Joanna Miler's Linkedin Analytics

Performance ScoreJoanna Miler's LinkedIn Performance

Joanna Miler's Best Posts (last 30 days)