The processes to digitize first are usually the ones with repeated manual work, frequent errors, long waiting times or direct customer impact. The best starting point is not the most modern-looking process, but the most visible bottleneck with measurable value. A good decision combines effort, data quality, risk, acceptance and impact on customers or employees.
Why is the sequence of digitization more important than the tool?
Many digitization projects in SMEs start with a software demo. A CRM looks organized, an ERP module promises automation, an AI tool replies to emails, a form replaces paper, a dashboard shows numbers. It quickly feels as if the main question were: Which tool should we buy first? That is often the wrong beginning.
The real question is not which tool comes first. The better question is: Which process deserves digital attention first? A poorly understood process remains poor with software. An unclear approval path does not automatically become faster because it is digital. Bad data does not become better with AI; it is only processed faster. And a process that nobody follows consistently does not become reliable because it has been automated.
SMEs rarely lack ideas. They usually have too many issues at the same time. Proposals take too long. Customer requests disappear in email inboxes. Documents are spread across folders, chats, paper files and personal drives. Invoice approvals depend on one person. Field service reports arrive too late. Phones interrupt the day. Warehouse, purchasing, service, sales and accounting work side by side instead of through one shared flow.
That is why the sequence matters. If the wrong process is digitized first, time, money and trust are consumed. If the right process is digitized first, the company experiences visible relief. This first visible effect often decides whether the organization will support the next steps.
Which processes should be digitized first when everything feels urgent?
The best starting point is usually a process that happens often, is clearly bounded and visibly costs time today. The goal is not to rebuild the most strategic process immediately. The goal is to find a process with enough impact and manageable complexity.
A good candidate meets several criteria. It happens regularly. It involves several people or departments. It creates waiting time, follow-up questions or rework. It uses information that is already digital or can be captured digitally without major effort. And it has a clear outcome: request answered, appointment planned, proposal created, ticket resolved, invoice approved, document filed, order prepared.
Poor starting points are often too large, too political or too dependent on unclear master data. Replacing the entire ERP system as the first move rarely creates a calm start. Structuring inbound customer requests is usually easier. Instead of rebuilding the whole service organization, a company can first standardize field service feedback. Instead of solving every document problem, it can standardize recurring proposal documents.
The core rule is simple: the loudest process does not automatically win. The best candidate is the process with the strongest combination of value and feasibility.
How can companies identify processes with high digitization value?
A process has high digitization value when its current manual form visibly slows the business down. This should not be judged by gut feeling alone. It can be recognized through recurring symptoms.
A strong signal is a media break. If information is copied from emails into spreadsheets, typed from paper forms, extracted manually from photos or copied out of PDFs, there is usually potential. A second signal is idle time. The task itself may be simple, but it sits around because someone must forward, check, search or approve it. A third signal is repeated clarification. If the same information is missing again and again, the process input is weak. A fourth signal is error cost. If wrong data leads to rework, complaints, duplicated effort or incorrect orders, digitization becomes especially valuable.
Customer impact also matters. Internal processes can be important, but processes at the customer interface often produce visible value faster. Customer requests, appointment scheduling, proposal status, service communication, complaints and document requests are strong candidates. Customers notice quickly when a company works in a structured way.
For SMEs, another factor is especially important: dependency on individual people. If a process only works because one experienced employee knows everything, the company has an operational risk. Digitization should not only speed up work. It should move knowledge out of individual heads and into robust workflows.
Which processes should not be digitized first?
Not every painful process is a good starting point. Some processes are important but unsuitable as the first project. This is especially true when responsibility is unclear, data quality is weak or internal politics are strong.
One example is a grown ERP problem. If product data, customer records, warehouse logic, pricing, roles and workflows have become messy over years, new software can look tempting. But it does not automatically solve the underlying issue. Old disorder often gets transferred into new screens.
Processes with too many exceptions are also difficult. If every order is handled differently, every department uses its own rules and nobody can define the standard, simplification should come before digitization. Otherwise, the company digitizes exceptions instead of a process.
Caution is also needed with sensitive processes such as HR evaluations, automated decisions, personal data analysis or safety-critical workflows. These areas can be digitized, but they require stronger privacy, governance and review. They are rarely ideal for the first visible digitization success.
How does a simple prioritization matrix work?
A prioritization matrix makes discussions more objective. It prevents decisions based only on volume, hierarchy or personal pressure. For SMEs, a simple scoring model with five criteria is enough: value, effort, data quality, risk and acceptance.
| Process candidate | Value | Effort | Data quality | Risk | Recommendation |
|---|---|---|---|---|---|
| Structure customer requests | High | Medium | Medium | Low | Very strong starting point |
| Digitize incoming invoices | High | Medium | High | Medium | Good starting point |
| Replace the entire ERP | High | Very high | Often weak | High | Not as first project |
| Standardize proposal templates | Medium to high | Low | Medium | Low | Fast entry point |
| AI-based applicant evaluation | Medium | Medium | Sensitive | High | Later, with governance |
| Capture field service feedback | High | Medium | Medium | Low to medium | Good operational start |
| Clean up internal knowledge | High | Medium | Uneven | Medium | Good if clearly limited |
This table does not replace analysis. But it prevents a company from starting with the biggest or technically most attractive topic. The best first candidates usually combine repetition, clear value and limited complexity.
What role does data quality play in the decision?
Data quality is the quiet success factor of digitization. Many processes look easy to digitize until it becomes visible that names are spelled differently, customer IDs are missing, documents are named inconsistently, responsibilities are outdated or master data has not been maintained.
A process with weak data quality can still be a good starting point if the digitized process directly improves the data. A digital request form can reduce missing information. A structured service capture can connect photos, location, device data and description. A digital invoice intake can standardize documents. In such cases, digitization improves both the process and the data.
It becomes harder when a process depends on good data that does not exist. AI-supported analytics, automated proposals, forecasts, dashboards and process automation perform poorly when the foundation is unreliable. Then the first step should be to improve data intake and master data logic.
The decision rule is: digitize processes first when either sufficient data already exists or the digital workflow will directly improve data quality.
Why are customer requests often a good first process?
Customer requests are often an underestimated bottleneck in SMEs. They arrive by phone, email, web form, WhatsApp, personal contact or referral. They often contain important information, but not in a structured form. A name is missing, the address is incomplete, photos arrive later, the desired appointment is hidden in the text, urgency is unclear and the responsible employee has to search, ask or call back.
A digitized request process can create quick relief. It collects the right information, classifies requests, creates tasks, documents status and prevents requests from disappearing in individual inboxes. This is especially relevant for skilled trades, technical service providers, construction-related businesses, field service organizations and companies with high inbound request volume.
The advantage is that the process is defined enough to improve, yet close enough to customers to create visible impact. Employees feel the relief. Customers receive faster responses. Management sees which requests are coming in and where capacity is missing.
When is proposal and order preparation the better starting point?
Proposals are often the point where revenue slows down. Not because nobody wants to sell, but because information is missing, templates are inconsistent, calculations must be searched, technical details are incomplete or internal questions take too long.
Digitizing proposal preparation is especially useful when many similar proposals are created. Templates, text modules, checklists, pricing logic, documents and approvals can then be structured. AI can support later, but only after the basics are organized. An AI-generated proposal text is not useful if scope, prices, exclusions, photos, measurements or customer data are incomplete.
A good start is not full automation. A better first version is a digital proposal workspace: request, photos, notes, calculation basis, templates, internal questions and approval are in one place. This reduces search time, makes proposals more comparable and helps new employees contribute faster.
When should internal knowledge processes be digitized?
Internal knowledge processes should be digitized early when many questions repeat and decisions depend on specific individuals. This happens often in mature SMEs. One person knows which customer has which special rule. Another knows old proposals. An experienced technician remembers a solution to a recurring issue. An office employee knows where the current template is stored.
A digital knowledge system or company brain can help, but only with a clear scope. The mistake would be to collect every file at once and put a search layer on top. A better start is one defined area: frequent service cases, proposal modules, internal checklists, technical documents, project handovers or standards for customer communication.
Knowledge processes are good candidates when they create a lot of search time. They are poor candidates when the company is not willing to clean up outdated, duplicated or contradictory content. Digitization makes knowledge accessible, but it does not automatically decide which knowledge is correct.
Which numbers show the pressure to act?
Eurostat reported that in 2025, 74.65 percent of small EU enterprises reached at least a basic level of digital intensity; for medium-sized enterprises it was 89.57 percent, and for large enterprises it was 97.51 percent. The gap shows that smaller organizations still need to catch up in digital maturity.
The OECD 2025 report “SME digitalisation for competitiveness” emphasizes that smaller businesses are held back by low awareness, limited internal resources, skill gaps and financial constraints. This is exactly why prioritization matters.
McKinsey’s classic transformation research states that less than 30 percent of digital transformations succeed. The figure is older, but still useful as a warning: digitization often fails not because of technology, but because of focus, execution and change capability.
BCG reported in 2025 that only 26 percent of companies had developed the capabilities needed to move beyond proofs of concept and generate value from AI. This matters for process digitization because many companies start with AI before process, data and ownership are stable.
How can an SME start tomorrow?
A pragmatic start begins with a process list. Not all processes, but ten to fifteen candidates that visibly create friction today. Typical examples are customer requests, proposal preparation, appointment scheduling, incoming invoices, field service reports, service cases, document storage, project handovers, approvals, internal knowledge search and complaints.
Each process then receives a simple assessment: How often does it occur? How much time does it consume? How often do errors happen? How strongly does it affect customers? How good is the data? How large is the implementation effort? How high is the risk? How open are employees to change?
In the end, the company should not start ten projects. It should select two: one quick process with visible relief and one strategic process with longer-term value. This creates progress without overwhelming the organization. Digitization in SMEs works best when it becomes calm, specific and repeatable.
Further reading
OECD: Digitalisation of SMEs
https://www.oecd.org/en/topics/sub-issues/digitalisation-of-smes.html
European Commission: Digital Decade and digital transformation of businesses
https://digital-strategy.ec.europa.eu/en/policies/desi
BPM CBOK: Business Process Management overview
https://www.abpmp.org/page/BPMCBOK
Sources for the statistics used
Eurostat: Digital intensity by size class of enterprise
https://ec.europa.eu/eurostat/databrowser/view/isoc_e_dii/default/table
OECD: SME digitalisation for competitiveness
https://www.oecd.org/en/publications/sme-digitalisation-for-competitiveness_197e3077-en.html
McKinsey: Unlocking success in digital transformations
https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/unlocking-success-in-digital-transformations
BCG: AI Radar 2025
https://www.bcg.com/publications/2025/ai-radar-global-survey-artificial-intelligence
Which processes should be digitized first?
The first processes to digitize should be frequent, manual, error-prone or directly visible to customers. Strong starting points include customer requests, proposal preparation, incoming invoices, appointment scheduling, field service reports and internal knowledge search. The decision should not be based on technical appeal, but on value, feasibility and employee acceptance.
Why should companies not start with the biggest problem?
The biggest problem is often too complex for the first step. A full ERP replacement, company-wide data cleanup or new end-to-end process may be important, but can overwhelm people and budgets. A bounded process that creates visible relief is usually a better first move and builds trust for larger initiatives.
How can the value of a process be assessed?
Value can be assessed through time savings, fewer errors, shorter cycle times, better customer experience and fewer follow-up questions. It also matters whether the process captures knowledge or reduces dependency on individual employees. The more often a process occurs and the more it burdens employees or customers, the higher its value tends to be.
What role does effort play in prioritization?
Effort determines whether a process is suitable as a first candidate. High value is not enough if data is missing, many systems are involved or responsibilities are unclear. Good starting processes have limited scope, few interfaces and clear ownership. Larger topics can follow once the organization has built confidence.
Why are customer requests often a good starting point?
Customer requests are frequent, visible and close to revenue. They arrive through different channels, often contain incomplete information and create follow-up work. A digital structure can capture, prioritize, route and track requests. This reduces search time, improves response speed and gives employees a calmer working flow.
When is incoming invoice processing a good first process?
Incoming invoices are a good starting point when documents are manually distributed, checked or searched. The process has clear steps, recurring rules and measurable cycle times. It also connects accounting, purchasing and approvals. Before implementation, responsibilities, approval logic and document quality should be clarified.
Should companies introduce AI first or digitize processes first?
In most cases, the process should be clarified first. AI can help when data, roles and goals are well defined. If AI is placed on top of disorder, it often creates faster errors. A good sequence is structure first: intake, data quality, responsibilities and standard cases. AI can then support targeted steps.
Which processes are unsuitable as first candidates?
Unsuitable first candidates usually have unclear responsibility, weak data, too many exceptions or high legal risk. Examples include full ERP replacement, unclear HR evaluation processes, automated decisions or politically sensitive internal workflows. These topics may be important, but they are rarely the best first visible digitization step.
How many digitization projects should run in parallel?
For SMEs, fewer parallel projects are usually better. One quick process with visible relief and one strategic process with long-term value are often enough at the beginning. Too many simultaneous initiatives create change fatigue, overload key people and make disciplined implementation harder.
How can companies tell whether a process should be digitized?
A process should be digitized when it occurs regularly, contains manual transfer work, creates repeated questions, makes customers wait or depends heavily on individual employees. Recurring errors, media breaks and lack of transparency are also strong signals. Before implementation, however, the standard process should be understood.
