Trupeer Blog
Samenvatten
Worldwide IT spending was forecast to reach $5.435 trillion in 2025, up 7.9% from 2024, according to Gartner's spending forecast. That's a forecast of investment, not proof of business improvement. I think that distinction belongs at the center of every enterprise digital transformation discussion: buying capability and turning it into durable value are different jobs.
The hard work starts when employees must use the new system to serve customers, resolve exceptions, and make decisions. My recommendation is to manage transformation as an operating-model and workforce change, with technology enabling the work rather than defining success.
Table of Contents
How Should the Operating Model Change Around New Technology?
How Do You Govern Security and Knowledge During Transformation?
What Does Enterprise Digital Transformation Really Mean?
Enterprise digital transformation is the deliberate reshaping of how a large organization creates value, serves customers, and runs operations using digital technology, data, and new ways of working. It changes how the business works, not just what software employees open.
I'd compare it to renovating a busy hospital while patients are still receiving treatment. The building must stay open, the wards must keep working, and contractors must schedule changes around clinical routines. Moving a wall is straightforward compared with deciding how patients, staff, equipment, and information will move afterward.
What has to move together?
I'd manage these levers together rather than handing each to an isolated project team:
Technology stack: Connect applications, identities, and data so employees can complete work without unnecessary copying.
Business processes: Change routing, approvals, escalation paths, and handoffs.
Skills and incentives: Teach people the new responsibilities and stop rewarding behavior that preserves the old process.
Say a company replaces its customer relationship management system. A tool upgrade moves account records into Salesforce while leaving qualification, technical validation, and customer handover unchanged.
A transformation changes those responsibilities too. Sales engineers document validation consistently, customer success receives an actionable handover, learning and development prepares role-specific guidance, and IT operations manages access and integrations. Shared services can then work from the same approved information.
Where does AI fit?
I wouldn't start with AI as the definition of modernization. Build connectivity and dependable cloud infrastructure, establish usable analytics, then introduce AI-enabled operations where the underlying workflow supports them. That's a practical sequencing choice, not a requirement to replace everything before experimenting.
For executives connecting AI decisions to wider business strategy, GoReplay's enterprise AI guide provides an additional planning resource.
The transformation test: Does the way a team works change measurably, or only the software it logs into?
My working definition is simple: technology-enabled changes to work that produce sustained business improvement. The sustained part is what separates a transformation from an installation.
Why Do Most Enterprise Digital Transformations Fall Short?
Programs fall short when leaders treat technical deployment as the finish line. In McKinsey's 2018 transformation analysis, only 16% of respondents said their organizations improved performance and sustained it, while another 7% reported improvements that didn't last.
The same analysis reported earlier transformation success rates of 20% in 2012, 26% in 2014, and 20% in 2016. Digitally intensive sectors achieved success rates no higher than 26%, while traditional industries, including oil and gas, automotive, infrastructure, and pharmaceuticals, reported 4% to 11%. These historical findings aren't a prediction for your program, but they're a strong reason not to equate deployment with durable success.
How a successful launch becomes a stalled program
Say a regional retailer implements a new ERP. The migration works, employees can sign in, and the steering committee celebrates go-live.
Later, finance still reconciles orders through side spreadsheets because nobody changed the approval rules. Store managers email exceptions to familiar colleagues because the official queue doesn't identify who can decide. The application works; the operating arrangement doesn't.
I wouldn't call those employees resistant without investigating the workaround. Sometimes the spreadsheet contains the exception logic the implementation team never captured.
The sponsor's job: Resolve conflicts between departments, not merely approve milestone reports.
The process owner's job: Make the approved route workable, including exceptions.
The program lead's job: Test whether people can complete real tasks, not merely attend training.
What should count as readiness?
I'd require evidence that employees can execute the new process, managers can handle escalations, and owners can maintain guidance after the implementation partner leaves. A launch checklist that stops at migration, uptime, and attendance is incomplete.
Software installed is a technical milestone. Work changed is an operational result.
The practical response isn't to abandon ambitious modernization. It's to give adoption, accountability, and ongoing enablement the same seriousness as architecture and procurement.
Which Technology Layers Are Enterprises Actually Using?
Technology layer | OECD average adoption | Country range | Reference year |
|---|---|---|---|
Cloud computing | 49% of firms with 10+ employees | 16% to 78% | 2023 |
Big-data analytics | About 14% of enterprises | 3% to 40% | 2022 |
AI | About 8% of all firms; 28% of ICT-sector firms | 2% to 20% | 2023 |
The OECD Digital Economy Outlook 2024 reports these figures for firms across OECD countries, not enterprise-only adoption. Cloud use is more widespread than analytics or AI, though the measures cover different populations and reference periods. Treat them as adoption indicators, not a maturity scorecard.
Access to modern platforms does not show whether employees trust the data, understand the process, or have authority to act. Those operating conditions determine whether new technology changes work and produces value.
Readiness matters more than the next purchase
Cloud infrastructure can make systems accessible without making information useful. Analytics depends on agreed definitions, reliable inputs, and people who know which decisions a dashboard should support.
AI adds a governance question: which actions may the system take, and when must a person intervene? Plausible output alone does not establish accountability. For migration planning, Trupeer's cloud adoption strategies guide covers the foundation layer. Teams exploring agent-based approaches can review AI agents for enterprise automation. In either case, evaluate the approach against a specific workflow, not a broad modernization label.
A manufacturer's capability gap
A manufacturer moves its ERP to the cloud but keeps forecasting in spreadsheets. Planners distrust the demand categories, regional teams interpret adjustments differently, and the official forecast does not explain overrides.
An AI forecasting assistant cannot settle those disagreements. First assign ownership of demand definitions, document how overrides work, and confirm that planners can reproduce an approved forecast. Otherwise, the organization risks adding a new interface to an unresolved process.
Before adding another technology layer, confirm:
People use the foundation: Required roles can complete their work in the intended system.
Data has accountable owners: Definitions and correction routes are explicit.
Exceptions are understood: Teams know when automation stops and human judgment begins.
Guidance is accessible: Employees can find the procedure while doing the task.
Buy the next capability when the organization can operate it, not when procurement can obtain it.
How Should the Operating Model Change Around New Technology?
Model | How work is organized | Best fit | Main risk |
|---|---|---|---|
Functional silos | Each department optimizes its own tasks and handoffs | Stable, low-change processes | Automation speeds steps but not the end-to-end outcome |
Product-aligned teams | Cross-functional teams own a customer or business outcome | Onboarding, renewals, product education | Teams lack clear decision rights over shared systems |
Value-stream redesign | The full path from request to result is rebuilt with AI and automation | Processes spanning sales, support, and operations | Requires leadership to change incentives and approvals |
Choose the model that gives someone authority over the actual bottleneck. I wouldn't reorganize the enterprise just to make the organization chart look more digital.
Functional departments remain useful when specialist expertise, control, and consistency matter. Finance should retain accounting standards; security should retain security policy. The problem begins when nobody owns what happens between departments.
When cross-functional ownership helps
Product-aligned teams suit outcomes that require repeated cooperation. An onboarding team might include customer success, implementation, education, and operations, with a shared objective of helping customers reach productive use.
But a shared objective without decision rights is mostly a meeting invitation. The team needs clarity about what it can change, which resources it controls, and which conflicts require executive resolution.
I'd write those rights down. Can the onboarding owner alter the handover requirements? Can they prioritize an integration fix? Who decides when sales promises conflict with implementation capacity?
When the entire path needs redesign
Value-stream redesign goes further. Instead of improving each department's task, leaders examine the complete journey from request to customer result and remove unnecessary waiting, duplicate checks, and fragmented ownership.
Say sales introduces AI-assisted proposal drafting. Sellers produce polished documents quickly, but legal and pricing approvals still sit in separate queues. Buyers don't receive proposals sooner because drafting wasn't the limiting step.
I'd examine the approval path before investing in more generation capability:
Standard terms: Let authorized sellers use approved language without unnecessary escalation.
Exception routing: Send unusual liability or pricing requests directly to the responsible decision-maker.
Shared evidence: Give reviewers the same customer context rather than separate email summaries.
Queue ownership: Make someone accountable for unresolved requests, including their age and business impact.
That redesign has trade-offs. More delegated authority can reduce waiting, but it requires clear limits, reviewable decisions, and a reliable escalation route. Central control may be appropriate for sensitive commitments even when it slows the transaction.
My recommendation: Don't automate a handoff until you've asked whether the handoff should exist.
Before choosing an operating model, answer: Who owns the outcome? Who can decide? Where will released capacity go? If those answers remain departmental while the customer journey crosses departments, local productivity improvements may never become enterprise value.
How Do You Govern Security and Knowledge During Transformation?
Govern security and operational knowledge as part of everyday delivery, not as a final publication check. The NIST Cybersecurity Framework 2.0 organizes cybersecurity risk management into Govern, Identify, Protect, Detect, Respond, and Recover.
I'd use those functions as a coverage checklist, not a strictly linear project schedule. Governance applies throughout, and detection, response, and recovery need preparation before anything goes wrong.
Apply the functions to a recording or procedure
An internal walkthrough can expose customer information, privileged screens, or instructions that shouldn't reach every employee. I'd apply this publication checklist:
Govern. Assign a content owner, approver, and retention rule. The owner must know what event triggers a review, such as a software release or policy change.
Identify. Classify the information and intended audience. Check the recording for customer records, credentials, personal information, and restricted operational details.
Protect. Apply SSO, least-privilege access, and redaction. Give reviewers access to drafts without automatically granting the wider audience access.
Detect. Monitor unusual sharing and access where the platform supports it. Confirm who investigates alerts rather than collecting logs nobody reviews.
Respond. Define how to withdraw content, revoke access, and notify security. Preserve relevant evidence before deleting or replacing an exposed artifact.
Recover. Restore the approved version and validate its permissions. Update the publishing process so the same failure is less likely to recur.
For broader controls around enterprise applications and content, Trupeer's enterprise security guide provides additional context.
Document more than the normal path
A critical process needs a normal path, an exception path, and an incident path. Otherwise, employees receive guidance only for the easiest version of the job.
Take customer support. The normal path explains how an agent verifies identity and updates a case. The exception path explains what happens when identity can't be verified, including which actions the agent must not take.
The incident path covers suspected account compromise: stop sensitive changes, notify security through the approved channel, and preserve case records. I'd keep those instructions distinct so an agent doesn't mistake an ordinary verification failure for an instruction to continue.
Keep authority visible
Record the last validation date, relevant system version, process owner, and approved audience. Archive obsolete instructions instead of leaving contradictory versions searchable.
If local teams need variations, label them by role, region, or regulatory context and assign an approver. Don't let a translated copy become an unowned alternative procedure.
Good documentation explains who may perform the work, what data they may use, and what happens when something goes wrong. Button-by-button instructions are only part of that responsibility.
How Do You Keep People Current Through Continuous Change?
The World Economic Forum's Future of Jobs Report 2025 estimates that 39% of workers' existing skill sets will be transformed or become outdated between 2025 and 2030, and 59 out of every 100 workers will need reskilling or upskilling by 2030. I'd therefore budget for continuous competence, not just launch training.
Consider a CRM replacement. An announcement tells everyone Salesforce is live, but an account executive needs to update opportunities, a solutions consultant needs to attach technical validation, and support needs to escalate renewal risk. Those are different jobs with different learning needs.
Turn rollout into a learning lifecycle
Trupeer's transformation stages provide a practical sequence for connecting process understanding to operational readiness:
Discover (Baseline Intelligence Audit). Establish how work happens today, including informal workarounds and dependencies.
Intelligent Process Capture & SOP Generation. Capture the current procedure so reviewers can inspect it before approving changes.
Design (Process Design Studio). Define the future workflow, responsibilities, and exception routes.
Build (Test Case Suite). Turn expected tasks and failure conditions into testable scenarios.
Transfer (AI-Led Training Suite). Prepare role-specific learning rather than sending every employee the same generic demonstration.
Go-Live Certification. Confirm people can perform the required work, not merely access the application.
Operationalize (Live Process Guidance). Maintain guidance where employees need it during daily execution.
I'd make the CRM exercise task-based. Ask the account executive to update a realistic opportunity and the support agent to route a renewal concern, then inspect the result.
Close the confidence gap
In WalkMe's enterprise technology survey, 79% of executives expressed confidence in meeting AI-transformation goals, while only 28% of employees said they were adequately trained. I wouldn't use executive confidence as a substitute for employee proficiency.
Managers should review errors, repeated questions, and failed tasks, then request targeted updates. A role-specific walkthrough tied to an indexed procedure is easier to maintain than disconnected slide decks, recordings, and email explanations.
For planning reusable walkthroughs, Trupeer's training videos guide offers a useful starting point.
Protect funding for ongoing learning and reusable knowledge content. Licenses and implementation services won't keep employees competent when workflows change.
How Do You Measure Whether Transformation Pays Off?
Measure durable business impact, not adoption counts. In McKinsey's 2025 State of AI survey, 64% of respondents said AI enabled innovation, but only 39% reported enterprise-level EBIT impact, and most of those organizations attributed less than 5% of EBIT to AI.
The financial downside deserves equal attention. A McKinsey survey of more than 1,700 C-suite executives found an average transformation had a 45% chance of delivering less profit than expected and about a one-in-ten chance of exceeding expectations; organizations following defined transformation practices raised the latter likelihood to more than 50%.
Trace the value chain
I'd build the measurement chain before scaling any workflow automation:
Measurement level | Question to answer |
|---|---|
Frontline usage | Are employees completing the intended task correctly? |
Process performance | Does work finish sooner, with less rework or fewer escalations? |
Workforce capacity | What work can people now take on? |
Business outcome | Did service, revenue, or margin improve? |
Financial realization | Can finance validate the benefit after implementation and operating costs? |
Say an AI assistant reduces support handling time, but customer renewals don't improve. That doesn't automatically invalidate the assistant. It means the team must investigate whether repeat contacts increased, whether complex cases received more attention, and whether released capacity was redeployed.
I'd agree a revenue, margin, or customer outcome with finance, track behavior inside the workflow, and review the chain quarterly. Separate estimated time savings from realized cost reductions or additional productive output.
Trupeer's enterprise AI use cases guide can help frame candidate workflows, but the business case still needs your baseline, costs, and accountable benefit owner.
If you can't explain how frontline usage contributes to a business result, you're reporting activity rather than proving transformation.
Which Workflow Should Your Team Transform First?
Start with a repeatable, measurable workflow that has a named owner. I'd choose customer onboarding, support resolution, or sales-engineering demo preparation before attempting an enterprise-wide change without clear boundaries.
For example, demo preparation works as a starting point when solution consultants repeatedly reconstruct the same environment, gather the same evidence, and wait for the same approvals. The scope is concrete enough to inspect without pretending every customer scenario is identical.
Make the first decision concrete
Choose an expensive delay. Identify where waiting causes lost capacity, customer friction, or avoidable rework.
Record the current path. Include handoffs, approvals, and exceptions, not just screen actions.
Set outcome and adoption measures. Pair a business result, such as completed onboarding, with correct execution of the new workflow.
Assign ownership and review. Name the process owner and the person who validates guidance.
Run a bounded pilot. I'd review it after a quarter before expanding its scope.
For prioritization, use Trupeer's automation opportunity identification guide. For sequencing dependencies across the wider program, Rite NRG's digital transformation roadmap guide provides a complementary planning resource.
For Genpact, Trupeer accelerated a global Workday rollout by 6 months. That's a specific delivery result, not a universal promise or a substitute for measuring your own business outcomes.
What's the first step in enterprise digital transformation?
I'd map a costly workflow and name its owner before selecting technology. Trupeer's Discover stage provides a baseline intelligence audit for that starting point.
How do I reduce employee resistance?
I'd investigate confusing responsibilities and impractical procedures before blaming attitudes. Trupeer's process-capture approach helps make the work explicit so teams can review what needs to change.
How can I prepare employees for go-live?
I'd test whether each role can complete realistic tasks, including exceptions. Trupeer's Go-Live Certification stage puts operational readiness into the transformation lifecycle.
How do I keep procedures current?
I'd assign an owner and trigger reviews when systems or policies change. Trupeer's Operationalize stage focuses on live process guidance rather than treating launch materials as permanent.
How can I prepare workflows for AI agents?
I'd clarify inputs, decisions, permissions, and escalation rules before delegating execution. Trupeer captures operational know-how to prepare both people and AI agents to execute.
This week, map the first workflow and agree on who owns it. That decision is more useful than another enterprise-wide ambition statement.
Trupeer Inc. turns screen recordings into product videos, structured procedures, and searchable knowledge, with enterprise controls to support governed rollout and workforce learning. I'd start with a workflow your team struggles to explain consistently, then visit Trupeer Inc. to discuss capturing it and preparing role-specific guidance for your next transformation milestone.
