The Transformation Stratosphere
Where Technology Meets Transformation
—One Bold Move at a Time
December 11, 2025
Leadership Message
From Christian's Desk
Perspectives
While we often talk about digital transformation in terms of operational efficiency, cost savings, return on investment, total cost of ownership, and more, the ultimate goal is usually to improve some aspect of the customer experience. Whatever the focus of a technology project, businesses of all sizes need to run better and faster because it allows them to better serve their most important stakeholder: the customer. We don’t automate key functions—from human resources to accounting—for automation’s sake. We do it to free up time and budget to improve our offerings, sell more products and services, and increase loyalty and retention. With that in mind, I’m pleased to introduce the December issue of our newsletter, which— as you may have guessed—takes a broader view of customer experience and how companies that prioritize it can increase revenue and market share. Whether you’re investing in ERP or considering agentic AI for the first time, if you’re looking to outperform your peers by 80%, read on…
The $127B CX Gap
The $127B CX Gap: Why Digital Leaders Outperform by 80%
Imagine two companies selling almost the same product, at almost the same price, in almost the same market.
One grows fast, keeps customers for years, and rides out downturns.
The other discounts constantly, quietly loses customers, and struggles to hit targets.
Often, the difference isn’t the product. It’s the experience.
Customer experience (CX) has become one of the biggest economic levers in business.
Companies strong in CX don’t just have happier customers—they grow faster, are more profitable, and are more resilient. Some studies show that those with superior digital experiences can generate up to 80% more revenue than peers.
Yet 68% of enterprises still live with fragmented journeys:
- One experience on the website
- Another in the app
- A different one with the call center
- Customers forced to “start over” every time
Analysts estimate that even a 1-point lift in CX score can be worth millions in extra annual revenue. Across industries, that adds up to a huge performance gap—easily in the $127B range.
And it’s rarely about who has the most tools.
The real differentiator is who designs, manages, and improves journeys in a disciplined way.
From Siloed to Self-Optimizing Journeys
Most organizations sit somewhere on a CX maturity curve:
You don’t have to leap from Siloed to Autonomous.
Even moving one level up can materially impact retention, share of wallet, and acquisition efficiency—especially in high-volume sectors like banking, telecom, retail, and healthcare.
This raises a natural question: what does life actually look like for organizations operating at the Connected, Intelligent, or even Autonomous end of this curve?
What “Good” CX Looks Like Now
Look at today’s CX leaders to see the new baseline: banks with near-instant mobile journeys and 90%+ satisfaction, healthcare providers offering easy portals and virtual care, logistics players with real-time tracking and reliable delivery. These aren’t “wow” moments anymore—they’re expected. Once customers experience this transparency in one sector, they expect it everywhere, powered by deliberate omnichannel design.
These examples aren’t accidents—they reflect a set of shared capabilities and choices that sit underneath the experience the customer sees.
The Architecture Behind Omnichannel Excellence
CX leaders don’t win by buying more tools—they build a few muscles: a unified customer data view, journeys that flow seamlessly across channels, AI that suggests the next best action in the moment, and analytics teams actually use. With that foundation, better NPS, faster resolution, and higher lifetime value stop being lucky wins and become how the business runs.
The good news is that you don’t need to rebuild everything at once—you can start small and still move meaningfully toward this kind of architecture.
A Practical 90-Day CX Agenda
Instead of launching a huge “CX transformation,” think in three 30-day waves.
In the end, the $127B CX gap isn’t mostly about missing technology. It’s about leadership intent and discipline.
The organizations that will pull ahead are those that treat customer experience not as a series of projects, but as a data-driven management system for the entire journey.
Those are the companies customers stay with, spend more with, and recommend. And in markets where products are easy to copy, that may be your most durable advantage.
Expert-led transformation
When the Mission Outgrows Spreadsheets: An Expert-Led ERP 2.0 Story
Digital transformation sounds shiny—until you’re the one trying to run a global nonprofit on email threads, Excel files, and systems that don’t talk to each other.
That was everyday life for one organization working in 80+ countries to improve smallholder livelihoods and global food security. The mission was ambitious. The operations behind it… not so much.
Purchase requests crawled through inboxes. Warehouses relied on manual counts. Finance pulled data from different tools at month-end and hoped the numbers lined up. HR and grants teams stitched together records just to keep donors satisfied.
Nothing was completely broken—but everything was just hard enough to slow the mission down.
That’s where our expert-led ERP 2.0 journey began.
Designing ERP 2.0: One Story, One System
We didn’t start by ripping everything out. The organization already had Dynamics 365 Finance & Operations live in several countries—what we nicknamed ERP 1.0.
Together, we reimagined it as ERP 2.0:
- One backbone for finance, procurement, HR, grants, and reporting
- One common way of working across headquarters and country offices
- One version of the truth leadership could actually trust
We followed a clear path: discover what’s really happening, design with users, test properly, train well, go live, then support intensely. Every initiative was tied to a Business Value Register, so “transformation” wasn’t a promise—it was something we could measure.
Numbers the Board Can Get Behind
Over a focused multi-month program, the organization moved from “patchwork operations” to a modern backbone:
- 60% faster month-end close
- Up to 50% efficiency gains in key finance and operations processes
- Up to 40% overall operational efficiency improvement across 10+ countries
- 25–30% savings in total cost of ownership
- 100% transparency in grant management
- 100+ real-time KPIs on unified dashboards
ERP 2.0 in Real Life: Change in People’s Day Jobs
The real test wasn’t in slide decks; it was in what changed on the ground.
Procurement:
Instead of chasing approvals across long email chains, teams used catalog-based purchase requests with vendor ratings and live dashboards.
- About 50% faster PR creation, and clear visibility into spend and approvals.
Warehousing & Assets:
Manual counts and scattered asset lists became a warehouse app, live transfers, digital cycle counts, and full asset tracking.
- Roughly 50% efficiency gains and less downtime, thanks to better maintenance planning.
Chargebacks & Finance
Complex spreadsheet gymnastics for chargebacks gave way to automated rates, built-in exceptions, and standard workflows.
- Around 80% faster chargeback processing and a 60% faster month-end close.
HR & Subgrants:
Paper-heavy HR processes and scattered subgrant records shifted to self-service HR and integrated, auditable grant workflows.
- Paperless HR and full visibility into donor funds and subgrant compliance.
People didn’t just get new screens. They got time back, fewer surprises, and clearer rules of the game.
And the new landscape is AI-ready—set up for assistants, automation, and advanced analytics without another big overhaul.
The People Side of “Expert-Led” Change
The tech made it possible. The people made it stick.
We worked with business champions in each function and region, invested in role-based training, and ran structured testing so users saw their real processes reflected in the system. After go-live, a period of “hypercare” support helped stabilize operations and build confidence.
With clear approval flows, delegated authority, and consistent policies, donor trust and internal accountability both went up. What started as “another system rollout” quietly became the backbone of how the organization runs—and proves—its mission.
Why This Story Defines Expert-Led Transformation for Us
Looking back, this project is one of our clearest examples of expert-led transformation:
- We understood the mission and how it’s measured
- We used proven patterns for integrated, secure, AI-ready operations
- We tracked value from the beginning, not just at the end
- We treated change management as essential, not optional
The result: a global nonprofit that can show its impact with clarity—to donors, partners, boards, and, most importantly, the communities it serves.
If your organization is feeling the strain of fragmented ERP, CRM, HR, or reporting, and you want change that’s measurable as well as meaningful, we’d love to explore what expert-led transformation could look like for you.
Leader Lens
The Agentic Enterprise Is Almost Here.
But Are You Ready for It?
Marc Benioff opened Dreamforce 2025 with a reality check. With AI agents becoming decision-makers, orchestrating workflows, triggering actions, and shaping outcomes in real time, the Agentic Enterprise is no longer a vision for the distant future. As Benioff made clear, the winners in this next era won’t be the companies experimenting the fastest. They’ll be the ones preparing the smartest by getting their data house in order, aligning priorities, and building governance that can stand up to both innovation and scrutiny.
Because the uncomfortable truth is that AI doesn’t fix what’s broken, but amplifies it.
Across industries, we’re already seeing how enthusiasm for generative and agentic AI can outpace operational readiness. In Healthcare, inconsistent or siloed EHR data can turn clinical decision support into a liability instead of an advantage. Agents that pull from unverified sources risk HIPAA violations or audit gaps when automated summaries, codes, or schedules can’t be traced back to a verifiable process.
In BFSI, the stakes are even higher. Model outputs tied to outdated customer profiles can trigger false positives in AML checks, introduce bias in lending, or create compliance blind spots that regulators will not overlook. When an AI agent can execute transactions or recommendations autonomously, the margin for error narrows to zero.
And in Logistics, where timing and accuracy define profitability, fragmented supplier or inventory data can derail forecasting and ESG reporting in a single stroke. Agents that orchestrate routing or scheduling without validated real-time input only end up accelerating operational drift instead of improving efficiency.
This is the moment Benioff was pointing to. It’s the inflection point where AI’s power makes governance, data discipline, and process clarity non-negotiable.
It’s Time to Get the Fundamentals Right
At Korcomptenz, we see the Agentic Enterprise not as a technology milestone, but as an operating model shift. Preparing for it starts with building a unified, trustworthy data foundation, resulting in one source of truth that gives every agent the confidence to act consistently. Governance must evolve from a checklist activity to a design principle, ensuring every automated action is explainable, auditable, and aligned with business priorities.
Workflows also need modernization before automation. Agents amplify existing processes; they don’t reinvent them. Streamlining handoffs, integrating systems, and removing friction ensures agentic automation generates value rather than chaos.
Finally, enterprises must adopt a platform-led AI strategy, leveraging ecosystems like Salesforce to deploy, monitor, and scale agents safely while preparing teams to collaborate with AI as a natural extension of their work.
The age of the Agentic Enterprise is approaching fast, and the future will belong to leaders who invest in the fundamentals today.
Innovation Radar
Zero-Trust AI: The New Compliance Baseline
AI isn’t just summarizing emails or suggesting next steps anymore. It’s quietly moving into the heart of regulated operations, including workflows like claims adjudication, clinical documentation, credit scoring, and compliance checks, where a single incorrect decision can trigger financial, legal, or reputational fallout. As AI systems start interacting with sensitive data and making judgment calls inside these environments, a simple truth is emerging: traditional security and governance models were never designed for this level of autonomy.
That’s why the new paradigm of zero-trust AI is taking shape, where no model, agent, workflow, or inference is assumed to be trustworthy by default.
A Shift Driven by Business Risk and Technological Maturity
Regulators across banking, financial services, and healthcare are no longer just asking if you use AI. They’re asking how you control it. For example, the NAACP’s recent report calls for “equity-first” AI standards in healthcare, including bias audits, transparency requirements, and governance councils, sending out a clear signal that compliance expectations are tightening beyond voluntary ethics codes. PwC’s Digital Trust Insights also highlight the growing need for transparency and control to ensure regulatory resilience as AI adoption accelerates. Boards are asking tougher questions too, focusing on model approval, dataset access, and accountability when AI gets things wrong.
At the same time, modern AI architectures are becoming multimodal, multi-agent, and connected across workflows. That also means they’re introducing new risk surfaces, such as unauthorized inference, cross-workflow data leakage, and AI agents that over-reach because boundaries weren’t clearly defined. Old access controls simply can’t keep up. That’s where zero-trust AI steps in by making trust dynamic, identity-aware, and continuously auditable.
How Enterprise Leaders Can Prepare for the Zero-Trust AI Era
Getting ahead of this shift isn’t about slowing innovation. It’s about innovating with guardrails that scale:
Zero-trust AI doesn’t have to be a constraint. Done right, it can become the confidence layer that decides who scales AI responsibly and who gets left behind, navigating preventable risks.
TREND Vs.TRUTH
The Reality of Real-Time Data: Speed Can’t Solve Everything
A recent global analytics industry survey found that nearly 70% of organizations now consider real-time data critical to their business operations. It’s easy to see why real-time capabilities are rapidly becoming the business world’s reflex answer to every analytics problem. In an era where speed signals competitiveness, the promise of instant insights feels irresistible. Faster decisions often mean sharper customer experiences and tighter operational control. In the right contexts, it can be transformative.
However, this surge in enthusiasm has also created the misconception that real-time is the pinnacle of analytics maturity and should be the default for every workflow. The truth is a bit more nuanced.
Not every decision benefits from real-time signals
From planning cycles to supplier scorecards, many business processes don’t gain any advantage from millisecond-level freshness. They need reliable, contextual data, not a live feed.
The cost curve rises faster than the value curve
Real-time ingestion, streaming, and compute architectures are expensive to build and maintain. The ROI makes sense only when the decision truly demands continuous updates.
The real-time approach accelerates bad data if quality is weak
If upstream data is inconsistent or poorly governed, streaming it faster doesn’t improve insight. It simply speeds up misalignment, noise, and flawed decisions.
Real-time still depends on strong foundations
Without trusted master data, clear metadata, and solid contextual modeling, real-time systems become distractions rather than decision engines.
Real-time data is necessary, but not universally necessary.
As Gartner highlights in its 2025 Data & Analytics trends, leading organizations ground their analytics choices in business outcomes, not in adopting technology for its own sake. Getting the best results means taking a strategic approach by calibrating the speed of insight to the speed of the decision. Some use cases demand live signals. Others run perfectly, or even better, on near-real-time or batch analytics. Making winning moves is about blending all three, investing where speed truly moves the needle, and prioritizing data quality and governance everywhere else. Real-time data is just one tool in a broader intelligence strategy. And it only becomes powerful when harnessed deliberately.
Event Spotlight
Carrying the Convergence 2025 Momentum into 2026: Doubling Down on AI-First ERP
This year at Convergence 2025, one message was impossible to miss:
The future of business applications is agentic, AI-first ERP.
Across keynotes, demos, and partner sessions, Microsoft reframed Dynamics 365 not just as a transactional system of record, but as a system of intelligent agents—automating decisions, orchestrating workflows, and connecting data across finance, supply chain, sales, service, and HR.
What we saw at Convergence
A few themes stood out clearly:
- Agentic ERP in action – AI agents handling tasks like order capture, collections reminders, supply chain risk alerts, and field service scheduling directly inside Dynamics 365.
- Copilot everywhere – Embedded in Finance, Supply Chain Management, Field Service, Commerce, and Business Central—not as an add-on, but built into everyday processes.
- AI with governance – Strong focus on evaluation, security, and risk mitigation so enterprises can trust the agents that are helping run their business.
- Partner-led transformation – Microsoft is leaning heavily on partners to design real-world solutions: packaged use cases, industry IP, and structured change management around AI + ERP.
How Korcomptenz is aligning for 2026
Based on what we learned and validated at Convergence, we’re evolving our Microsoft business in 2026 around three priorities:
- AI + ERP Value Blueprints
Industry-specific roadmaps for Finance, Supply Chain, and Service that identify where agentic workflows can cut cycle times, reduce leakage, and improve resilience. - Agentic Use Case Design & Build
Designing, piloting, and scaling AI agents and Copilot experiences on Dynamics 365—starting with high-impact scenarios like: - Change, Governance & Adoption
Treating every AI-ERP initiative as an operating model change, not just a tech project—covering role design, guardrails, evaluation, and frontline enablement.
If you’re planning your 2026 ERP and AI roadmap, we’d be happy to share key takeaways from Convergence and co-create a shortlist of agentic use cases tailored to your business.
Bring AI closer to your ERP.
Turn agents into outcomes.
Let’s start the 2026 conversation now.
Stay tuned for our next newsletter
Expert-led Transformations and Impact-led Growth
At Korcomptenz, we lead with expertise – in technology and domain to deliver solutions that align with your business goals. We leverage our experience and robust partner ecosystem to elevate your processes, powering your transformation journey toward impactful growth.
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December 11, 2025