Streamlining Support Processes: How AI Identifies Redundant Steps and Cuts Throughput Times in Half

Why Support Processes Are Often Too Slow – The Hidden Time Wasters

Most companies only track the actual handling time of their support tickets. That’s like only timing the drive, while ignoring traffic jams and detours.

True cycle time = Handling time + waiting periods + handovers + queries

The Five Most Common Time Wasters in Support

Our analysis of more than 200 mid-sized companies shows: these factors most often slow down support processes.

1. Manual Ticket Forwarding: Average of 4.3 hours wait per handover
2. Lack of Initial Information: 67% of all tickets require follow-up questions
3. Redundant Checks: Identical controls across multiple departments
4. Unclear Escalation Rules: Tickets sent to the wrong specialist
5. Media Breaks: Switching between email, phone, and in-house software

What’s Really Behind It

Each time waster has a logical reason. Manual forwarding, for example, often came about for safety: “Better to check one time too many than overlook a mistake.”

But what was meant to ensure quality becomes a bottleneck. One machine manufacturer from Baden-Württemberg found his service technicians spent 40% of their time coordinating—instead of actually repairing.

The Domino Effect of Delayed Processes

Slow support processes have consequences far beyond customer satisfaction:

• Your staff spend more time coordinating than resolving issues
• Escalations pile up, as simple problems take too long
• The cost per resolved ticket keeps rising
• Your team gets frustrated—skilled employees leave

But here’s the good news: AI can spot these patterns and suggest concrete improvements.

AI-Based Cycle Time Analysis: How the Technology Works

Imagine having an invisible observer documenting every step of your support process. That’s what process mining with artificial intelligence does.

Process Mining: An X-Ray View of Your Operations

Process mining analyzes the digital traces in your existing systems. Every email, every status change, every ticket handoff becomes a data point.

AI uses this to map your processes with precision—not as you think they happen, but as they actually do.

Process mining is like an ECG (electrocardiogram) for business processes. It shows not just the current state, but also the irregularities. – Prof. Dr. Wil van der Aalst, founder of process mining

What Data the AI Needs

To perform meaningful analysis, AI needs access to your existing systems:

The good news: You don’t need to deploy new systems. AI works with your existing data.

How the Analysis Actually Works

The analysis process follows a proven pattern:

1. Data Gathering (Week 1): Automated export from existing systems
2. Process Mapping (Week 2): AI identifies recurring patterns
3. Bottleneck Analysis (Week 3): Pinpoints bottlenecks and waiting times
4. Optimization Suggestions (Week 4): Concrete recommendations for action

A mid-sized IT service provider discovered after four weeks: 23% of their support time disappeared in a single, unnecessary approval step.

Machine Learning Detects Patterns

The key advantage: AI learns from your data. It spots which tickets follow similar paths, and detects where systematic problems occur.

Example: If 80% of hardware tickets end up back with level-1 support after initial handling, it’s not a fluke—it’s a system problem.

Identifying Superfluous Steps: The Systematic Approach

Not every time-consuming step is unnecessary. The challenge is to distinguish true waste from essential controls.

The Three Categories of Wasted Time

Our experience shows superfluous steps fall into three main categories.

Category 1: Duplicate Work

Identical tasks carried out by multiple people. Classic example: customer data is entered in both the ticketing system and the CRM.

Identifiable By:

• Identical information in several systems
• Similar checks in different departments
• Repeated follow-up questions to the same customer

Category 2: Waiting Loops

Tickets sit untouched even though all info for processing is present. These often result from unclear responsibilities or overly cautious escalation rules.

Typical Waiting Patterns:

• Tickets lingering over 24 hours in “waiting for assignment” status
• Multiple back-and-forth handovers between departments
• Unnecessary confirmation loops

Category 3: Over-Engineering

Processes designed for exceptions, but applied to standard cases. For example, every support ticket must pass through three layers—even for simple password resets.

AI-Based Identification Methods

Modern analysis software uses various algorithms to spot waste:

The Validation Step: Don’t Eliminate Everything

Beware slashing too much! Not every time-consuming step is dispensable. Compliance requirements, security checks, or quality controls often exist for good reason.

The right question isn’t, “Can we skip this?” but, “Can we make this more efficient?”

For example: Instead of removing three manual approval steps, one company implemented automatic rules for 80% of routine cases. Only exceptions still go to a human.

Quick Win Identification

Some superfluous steps can be eliminated instantly, others require more effort. Smart teams start with the simple ones:

1. Immediately actionable: Duplicate data entry, unnecessary CCs
2. Short-term (1–3 months): Automated routing rules, standard replies
3. Mid-term (3–6 months): System integrations, new workflows

The upside: Quick wins keep your team motivated and build trust for bigger changes.

Real-World Examples: Where AI Has Successfully Optimized Support Processes

Theory is great—practice convinces. Here are three real cases in which AI-based process optimization delivered measurable results.

Case 1: Machinery Manufacturer Cuts Service Times by 55%

Müller Fertigungstechnik GmbH (name changed) from the Black Forest had a problem: Service technicians spent more time on paperwork than on repairs.

The Situation:

• Average response time: 4.2 days
• 23 manual steps per service case
• 67% of time spent on coordination and documentation

What the AI Analysis Revealed:

The main time waster wasn’t with the technicians, but in the preparation. Each service call passed through eight different approval layers—even though 78% were standard repairs.

Additionally, identical spare parts were checked three times: once at request, once before shipping, and once on-site by the technician.

The Optimization:

1. Automatic categorization of standard vs. special cases
2. Express track for standard repairs (just one approval level)
3. Spares checked only at one central point
4. Mobile app for technicians eliminated media breaks

The Results After 6 Months:

• Response time: 1.9 days (-55%)
• Technician productivity: +34%
• Customer satisfaction: from 7.2 to 8.9 (on a 10-point scale)
• Optimization ROI: 287% in the first year

Case 2: IT Service Provider Eliminates 40% of Escalations

A mid-sized IT service provider from Munich was swamped with tickets. The problem: 43% of all requests ended up with expensive level-3 support—even though many were easily solved.

The AI Insights:

The routing system categorized tickets by keywords. “Server problem” automatically went to senior experts—even if just a restart was needed.

At the same time: 67% of supposedly complex tickets had identical solutions.

The Solution:

• AI-based ticket routing using historical solution patterns
• Automated suggestions from the knowledge base
• Self-service portal for the 20 most common issues
• Smart escalation—reserved for true exceptions

Measurable Improvements:

Case 3: SaaS Company Automates 60% of Customer Requests

A fast-growing software company from Berlin had a luxury problem: too many customers. Support couldn’t keep up.

The Challenge:

Over 200 support tickets were coming in every day. 78% were repeat questions about configuration, billing, or simple features.

The AI Strategy:

Instead of hiring more support staff, the company first analyzed six months of historical tickets. AI identified 127 different standard request types.

The Implementation:

1. Smart chatbot for the 50 most common queries
2. Automated solution suggestions based on ticket history
3. Smart routing: complex cases go straight to specialists
4. Proactive notifications for known problems

The Impressive Results:

• 60% of requests fully automated
• Support team could focus on complex consulting
• Customer satisfaction rose—despite fewer staff
• Scaled business by 340% with no extra support hires

The CEO sums it up: “AI didn’t cost us jobs, it created better ones. Our people now do what humans do best: advise and solve problems.”

Implementation Guide: Streamlining Support Processes Step by Step

From analysis to execution—here’s your practical roadmap for AI-driven process optimization.

Phase 1: Inventory & Assessment (Weeks 1–2)

You cant optimize what you don’t understand. This phase is critical—and often underestimated.

Gather Data

Export data from all relevant systems covering the last 6–12 months:

• Ticketing System: Ticket ID, creation time, status changes, assignee, category
• Email Traffic: Response times, forwarding, CC recipients
• Phone System: Call times, queues, transfers
• CRM/ERP: Customer history, contract data, escalations

Pro tip: Work closely with your IT department. Most systems have export functions—you just need to know where to find them.

Engage Stakeholders

Get your support teams on board from day one. The best ideas often come from those dealing with the process every day.

Conduct interviews with key roles:

1. Support manager: Strategic goals and budgets
2. Team leader: Operational challenges
3. Frontline staff: Real-world problems
4. IT: Technical options and limitations

Phase 2: AI Analysis (Weeks 3–6)

Now it gets detective-like. Modern process mining tools analyze your data and uncover hidden patterns.

Choose a Tool

Best options for mid-size companies:

Interpret Analysis Results

AI delivers lots of data. Focus on the key indicators:

• Cycle time distribution: Where are the longest bottlenecks?
• Process variants: How many different routes are there?
• Bottleneck analysis: Where do tickets pile up?
• Automation potential: Which steps are standardizable?

Create a priority list based on two criteria: implementation effort vs. expected benefit.

Phase 3: Implement Quick Wins (Weeks 7–10)

Start with simple changes that make an immediate impact. This builds momentum and wins over skeptics.

Typical Quick Wins

1. Email optimization: Remove unnecessary CCs, create standard responses
2. Routing rules: Automate basic categorization
3. Eliminate duplicates: Merge identical steps across systems
4. Expand self-service: FAQ for the 20 most common questions

Case in point: One company found that 34% of support emails were sent unnecessarily to five people. Adjusting the distribution list saved the team 8 hours a week.

Phase 4: Systematic Optimization (Weeks 11–20)

Now it’s time for the big stuff. This phase takes more planning—but also delivers high returns.

Implement Automation

Focus on high-volume, low-risk steps:

• Ticket routing: Automatic assignment based on content and history
• Standard solutions: Common problems get auto-resolved
• Escalation rules: Smart escalation for true exceptions only
• Status updates: Customers automatically kept in the loop

Optimize Integration

Eliminate media breaks via better system integration:

1. API connections between ticketing and CRM
2. Single sign-on for all support tools
3. Central knowledge base
4. Mobile solutions for field technicians

Phase 5: Monitor & Optimize (Ongoing)

Process optimization isn’t a one-time job—it’s continuous improvement.

Define & Track KPIs

Measure the key metrics regularly:

Important: Make sure efficiency improvements don’t come at the cost of quality.

ROI and Measurability: Is the Optimization Worth It?

Your management wants numbers. Understandably so—AI projects require time and money. Here’s how to calculate the return on investment for support process optimization.

Direct Cost Savings

Let’s start with the obvious: Faster processes mean less labor cost.

Calculating Personnel Costs

A simple example: Your support team (8 people at €55,000 annual salary each) handles 12,000 tickets a year.

• Baseline: On average, 2.3 hours per ticket
• After optimization: 1.4 hours per ticket (-39%)
• Time saved: 12,000 × 0.9 hours = 10,800 hours/year
• Cost saving: 10,800 hours × €35/hour = €378,000/year

But beware: That’s an oversimplification. In reality, you’ll use that saved time to handle more tickets or boost quality—not just cut jobs.

Realistic Cost Reductions

Genuine savings come from:

1. Avoiding new hires: Grow without extra staff
2. Reduced overtime: Especially during seasonal peaks
3. Fewer freelancers/temps: Peaks handled internally
4. Lower churn: Less frustrated staff

Indirect Value Gains

This is where things get interesting—and often overlooked. Improved support processes benefit more than just cost.

Customer Loyalty & Revenue

Concrete figures from our projects:

Staff Productivity

Your support team gets more productive—not just because things move faster, but thanks to higher motivation.

An optimized process delivers:

• Less frustration thanks to smoother workflows
• More time for challenging, fulfilling tasks
• Visible successes via happier customers
• Pride in a modern, efficient system

The result: 15–25% overall productivity boost—not just in support.

Planning Investment Costs Realistically

Transparency is part of the Brixon approach. Here’s what typical AI-based support optimization costs look like:

One-Off Implementation Costs

Ongoing Costs

• Software licenses: €500–2,000/month (depending on system and users)
• Maintenance & support: 15–20% of implementation costs/year
• Continuous optimization: €3,000–8,000/year

ROI Calculation: A Realistic Example

Let’s take a typical mid-sized company with 150 staff and 8,000 support tickets a year:

Year 1 investment: €95,000

Savings/Added Value (per year):

• Avoided new hires: €75,000
• Reduced overtime: €18,000
• Higher customer retention: €160,000 extra revenue
• Lower churn: €12,000 (saved recruitment costs)

Net annual benefit: €265,000
Year 1 ROI: 179%
ROI from year 2: 442% (since only running costs remain)

Soft Factors: Hard to Measure, but Valuable

Some benefits arent easily measured in Euros, but are just as real:

• Employer branding: You’re seen as an innovative, attractive employer
• Scalability: Your support scales easily with business growth
• Futureproofing: Prepared for rising customer expectations
• Data quality: Better processes yield better data for further optimization

Bottom line: AI-based support process optimization almost always pays off—when done right.

Common Pitfalls and How to Avoid Them

Not every AI project runs like clockwork. After 100+ optimization projects, we know the traps—and how you can sidestep them.

Pitfall 1: Technology Before Strategy

The classic: “We need AI!”—but no one really knows what for.

The problem: Companies buy pricey tools before defining their objectives, leading to over-engineering and team frustration.

A real-world example: One mid-sized company invested €80,000 in an “intelligent support automation” AI platform. Six months later, it turned out 70% of tickets were so bespoke that automation made no sense. The real issue was chaotic internal processes.

How to avoid it:

1. Define your concrete goals first (save time? cut costs? improve quality?)
2. Thoroughly analyze your current processes
3. Identify your main pain points
4. Then choose the right technology

Rule of thumb: If you can’t sum up your problem in one sentence, you’re not ready for a technical solution.

Pitfall 2: Ignoring Change Management

Even the best AI is useless if your team won’t use it.

The problem: New systems are forced from the top down, without consulting those affected. The result: passive resistance, workarounds, and failed projects.

Warning Signs:

• “We’ve always done it like this”
• “This system is too complicated”
• Staff still use old tools alongside new
• Artificially high “error rates” on new processes

Successful Change Strategy:

Pro tip: Identify “champions” on your team—colleagues open to innovation. They will become ambassadors and help win over skeptics.

Pitfall 3: Overblown Expectations

AI is powerful, not magical. Unrealistic expectations breed disappointment.

Common exaggerations:

• “AI will handle 90% of tickets automatically”
• “We’ll be twice as fast after a month”
• “We wont need any support staff at all”

Set realistic goals:

Good AI optimization boosts processes 30–60%, not 300%. That’s still excellent—but evolutionary, not revolutionary.

Communicate openly:

1. First improvements: Visible after 4–6 weeks
2. Significant effects: Measurable after 3–4 months
3. Full impact: Reached after 6–12 months

Pitfall 4: Underestimating Data Quality

AI is only as good as its data. Garbage in, garbage out.

Frequent data issues:

• Inconsistent categorization across systems
• Missing/incomplete histories
• Different data formats (date, time, text)
• Duplicates and orphaned records

Conduct a Data Quality Check:

Before running AI analysis, check:

1. Completeness: Is all relevant data available?
2. Consistency: Are similar facts recorded consistently?
3. Recency: How recent is your latest data?
4. Accuracy: Does the data reflect reality?

Rule of thumb: Set aside 20–30% of project time for data cleaning.

Pitfall 5: Forgetting Compliance & Data Protection

GDPR, works councils, internal compliance—AI projects operate in regulated territory.

Typical compliance traps:

• Customer data leaves the company unnoticed
• Staff behavior is analyzed without consent
• Algorithmic decisions are opaque
• Backup and deletion policies are missing

Compliance Checklist:

Important: Involve these stakeholders from the very start—not just at go-live.

Pitfall 6: The Pilot Trap

Endless pilot projects that never go into production—a classic in larger companies.

The problem: Pilot succeeds, but transition to live operation fails due to organizational hurdles or lack of budget.

How to structure pilot projects successfully:

1. Set clear success criteria: When is the pilot considered a success?
2. Plan the rollout in advance: Whats the next step after the pilot?
3. Secure implementation budget: Before the pilot, not after
4. Use a realistic test environment: Test “in the wild”, not the lab

Tip: Keep pilots short (no more than 8 weeks) and decision-ready. Long test phases dilute results and demotivate teams.

The Key Success Factor: Pragmatism over Perfection

The most successful AI projects arent the most elegant, but the most practical.

Start with an 80% solution that works—instead of a 100% vision that never gets launched.

Because in the end, only one thing counts: Your support processes must get better, not perfect.

Frequently Asked Questions

How long does it take for AI-based support optimization to pay off?

For most of our projects, the break-even is reached within 12–18 months. Quick wins are often noticeable after just 6–8 weeks. ROI depends greatly on your current setup—companies with very inefficient processes see quicker returns.

Do we need additional staff for the implementation?

No, normally not. Most implementations are run as projects with external specialists. Your internal team is involved and trained, but you don’t need to increase headcount. Once launched, the system runs independently.

What happens to our support staff? Will jobs be lost?

Our experience shows: AI doesn’t eliminate jobs—it transforms them. Support staff gain more time for complex consulting and problem-solving. Many companies use the added efficiency for better service or growth, without layoffs.

Can we use AI analysis with our existing systems?

Yes, that’s actually the norm. Process mining works with the log data from your current tools (ticketing, CRM, email). There’s no need to introduce new tools before knowing what needs to be optimized.

How do we ensure the optimization is GDPR-compliant?

Data protection compliance is considered from day one. Customer data is anonymized or pseudonymized. All tools are EU-GDPR certified. During implementation, we work closely with your data protection officer.

What if the AI gives the wrong recommendations?

Process mining AI only analyzes historical data and reveals patterns. It does not make automated decisions on customer requests. All optimization proposals are validated by experts before being implemented.

Can smaller companies also benefit from AI-supported optimization?

Absolutely. Especially companies with 50–200 staff often have huge optimization potential, as their processes are not yet fully standardized. Scalable solutions now exist for every company size.

How do we measure the success of optimization?

By tracking clear KPIs: average resolution time, first-call resolution rate, customer satisfaction, and cost per ticket. These are measured as a baseline before optimization and then monitored regularly. This gives you hard figures for your improvements.