Why Progressive AMS

Closing the ticket isn’t the same as fixing the problem

SAP support has been built around the ticket for twenty years. Something breaks, a user raises it, someone investigates, it gets fixed, the ticket closes. The model works — and it can hit every SLA in the contract while the same categories of incident keep arriving month after month.

Progressive AMS is our answer to that. We keep critical SAP processes stable, and we treat the work of removing recurring problems as part of the service rather than a project you fund separately. AI is doing more of the routine handling than it was a year ago, which is what makes the second half affordable.

Why now

Three things are squeezing SAP teams at once

01 · The deadline

ECC mainstream maintenance ends.

The migration or extension decision can’t be deferred much longer. Every month spent defending the current estate is a month not spent moving it.

02 · The capacity trap

Most capacity goes on keeping things running.

The work that would reduce that load is the work that never reaches the top of the list.

03 · The AI expectation

Boards are asking for AI.

The data and the operating model underneath usually aren’t ready — in recent UKISUG, ASUG and DSAG member research, data management was the most-cited reason adoption will be slow.

What SAP user groups are reporting

6%

of UKISUG members rate themselves expert in AI

34%

say their organisation uses AI in some or many areas of the business

86%

say data management challenges will slow their adoption of AI

Source: UKISUG, ASUG and DSAG member research.

The appetite is real. The operating model that has to deliver it has barely moved.

Five signs your AMS may still be too reactive

  • The same categories of incident keep appearing, quarter after quarter. Resolution times may look fine; the volume tells you nothing has been removed.

    Symptoms are resolved. Root-cause action is deferred to the next quarter, and then the one after.

    Support begins when a user reports something. Monitoring exists, but someone internal is still the detection layer.

    There is no visible list of what has been automated, what is next, and what it saved.

    Effort goes into keeping the lights on. Improvement work has no protected capacity.

optical-fiber-background

The cost of staying reactive

A reactive service costs you twice

The visible cost is the support fee. The larger cost is the change you never got round to.

Your team

Cost to your team

−Senior people on repeat incidents
−Knowledge held in individuals, not systems
−Out-of-hours effort that never falls
−Problem management deferred again
−No capacity to reduce the backlog
−Attrition among the people who know the estate

The business

Cost to the business

−The same disruptions, monthly
−Workarounds that became permanent
−Slower period close
−Users who stop reporting
−Shadow processes outside SAP
−Change queued behind fixes

Your roadmap

Cost to your roadmap

−Clean core work never starts
−S/4HANA readiness slipping
−BTP paid for and unused
−AI use cases stay theoretical
−Budget defends the run
−Technical debt compounding

What good looks like

What good AMS is actually for

A modern service should work at three levels at once, with capacity protected for all three.

01

Run

Keep critical SAP processes stable, available and supported.

02

Improve

Remove recurring problems, automate repeatable work and simplify processes.

03

Innovate

Use data, AI and new SAP capability to make the environment progressively more intelligent.

The test

The service should become more efficient over time, not simply process more tickets.

How it works

Five commitments that make this real

None of this works as a bolt-on. It has to be how the service is designed, staffed and measured.

01

A standing improvement backlog

Every engagement carries a named list of problems to eliminate and processes to automate, reviewed with you monthly.

02

Knowledge captured as we go

Resolutions, SOPs and SAP guidance land in a governed knowledge base. No knowledge base, no useful AI.

03

Detection built into the run

Monitoring signals and service desk behaviour analysed together, not in separate tools by separate people.

04

Automation with control

Defined scenarios only, human approval where data changes, every automated action logged and reviewable.

05

Measured in front of you

Tickets avoided, recurrence removed and automation delivered, reported alongside the SLAs.

Maturity model

Five levels, and the right one depends on the process

Where would you put your current service?

01

Reactive

Respond effectively when incidents occur.

Stability, SLA performance, resolution

02

Proactive

Identify recurring problems and intervene earlier.

Monitoring, root cause analysis, problem management, automation

03

Predictive

Use data patterns to anticipate issues before business impact.

Anomaly detection, predictive insight, AI-assisted diagnosis

04

Intelligent

Combine enterprise knowledge, service data and AI to recommend the best action.

Intelligent assistance, automated workflows, decision support

05

Autonomous

Resolve selected scenarios automatically, within defined controls.

Self-healing, agent-based orchestration, continuous optimisation

Not every process needs to become autonomous. This is about applying the right level of intelligence to the right problem.

Roadmap

AI-powered AMS is an evolution, not an overnight switch

The objective is controlled autonomy, not automation for its own sake.

Available now

  • Enterprise knowledge assistants
  • Intelligent ticket classification
  • Automated routing
  • Knowledge retrieval
  • Incident trend analysis
  • AI-assisted investigation
  • Automated workflows for defined scenarios
  • Proactive monitoring and anomaly detection

Emerging next

  • More advanced AI-assisted root cause analysis
  • Predictive incident management across multiple signals
  • Agent-based orchestration across support tools
  • Wider automated remediation

Longer-term direction

  • Selective self-healing
  • Autonomous resolution within defined controls
  • Business-process-aware predictive support
  • Continuously optimising support environments
Floating Red Cube Cluster

Where AI takes the load

  • Problem: Users raise tickets because the answer is buried across SOPs, process documentation, SAP guidance, previous resolutions, policies and SharePoint.

    Instead: The question is answered from approved enterprise content, with source references, and only unresolved cases become tickets.

    Outcome: No ticket, no queue, no escalation.

    Problem: Warning signs sit in monitoring data that nobody has time to interpret until users feel the impact.

    Instead: Signals from S/4HANA, BTP and SAP Cloud ALM are analysed continuously for unusual patterns, assessed for whether they could cause disruption, and acted on before they do.

    Outcome: The pattern is handled while it is still a pattern.

    Example scenarios

    • Background job degradation in S/4HANA — execution time climbing over several days, signalling a likely failure
    • Recurring integration failures on BTP — separate tickets across interfaces resolving to one underlying cause
    • HANA memory and capacity growth — a sustained trend indicating an upcoming constraint

    Problem: A large share of ticket effort is classification, routing, standard checks and known fixes.

    Instead: AI classifies and prioritises, retrieves the relevant knowledge and previous fixes, recommends a resolution, triggers the known workflow where one exists, and captures the resolution for reuse — with human approval where it matters.

    Outcome: Less manual handling, faster resolution, more consistent support.

    AI handles knowledge search, classification, pattern recognition, data correlation, routine workflow execution and known resolutions.

    People handle complex root cause analysis, business judgement, architecture, process redesign and high-risk decisions.

Switching provider

Moving provider is less painful than it looks

The risk of moving is usually overstated. The risk of staying put is rarely counted at all.

The worry What happens with a structured handover
Knowledge loss Knowledge captured, not inherited
The incumbent stalls Shadow and reverse shadow first
My team carries it Your team signs off readiness
Service will dip Incumbent dependency falls weekly — and backlog work starts during transition

The transition plan

1

Discover

Weeks 1–2

Landscape, processes, ticket history, stakeholders, knowledge sources

2

Transfer

Weeks 2–4

Knowledge sessions, documentation, shadow support, access and tooling

3

Validate

Weeks 4–6

Reverse shadowing, scenario testing, SLA readiness, escalation testing

4

Operate

Go-live

Service handover, governance, performance tracking

5

Improve

Post go-live

Backlog reduction, problem management, automation roadmap, AI use cases

Timelines vary with landscape complexity and scope.

The commitments underneath ours

Every provider shows you the same five phases. These are what we commit to inside them.

Knowledge

Live on day one

A governed knowledge base, live on day one. Not a folder of runbooks.

Baseline

Published, not assumed

We publish what we inherited: ticket profile, recurrence rate, automation candidates.

Improvement

Delivered by month three

Named automations agreed during discovery and delivered by month three, not after a six-month stabilisation period.

AI governance

AI in support only works if the controls are real.

AI handles more of the routine work in the service. The controls around it are agreed up front and written into the service description.

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Where your data goes

  • Approved enterprise content only
  • Defined tenancy and data residency
  • No customer data used to train public models
  • Access follows existing SAP authorisations
  • Prompts and responses logged
  • Retention aligned to your policy
  • Named systems in scope, nothing implicit
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Where humans stay in control

  • Human approval before data changes
  • Automation limited to defined scenarios
  • Answers returned with source references
  • Escalation to a person when confidence is low
Data residencySAP authorisationsAudit loggingRetention policyHuman approvalDefined scenariosSource referencesEscalation

Track record

Built on an established AMS foundation

Since 2006

We are Providing SAP Application Management Services.

24/7/365

Follow-the-sun support, multilingual service desks across Europe, Asia and the Middle East.

Global SAP expertise

ECC, S/4HANA, BTP and the wider SAP ecosystem.

Business and technology

Functional, technical, data and AI capability in one organisation.

ITIL v4

Incident, problem and change management, with documentation and knowledge transfer.

What's covered

Full-spectrum SAP support from one team

 

Functional

The business processes

Across Finance, Supply Chain, Spend and CX.

Technical

Development and integration

Development, integration, BTP and extensions kept upgrade-safe.

Basis and platform

System administration

System administration, performance, upgrades and monitoring.

Clean core only holds if the support model stops putting things back into the core. We keep extensions upgrade-safe, absorb SAP updates as routine, and return fixes as improvements.

From service desk to self-improving SAP operations

Each cycle should make the next one cheaper.

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01 — Observe

Signals from S/4HANA, BTP and SAP Cloud ALM are analysed alongside service desk behaviour, rather than watched separately.

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02 — Understand

Patterns, anomalies, repeated incidents and likely causes are identified and attributed.

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03 — Act

The appropriate response is recommended, or executed automatically within defined scenarios.

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04 — Learn

What happened, what worked and what didn’t is captured as the work is done, not written up afterwards.

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05 — Improve

Those insights go back into monitoring, knowledge and automation.

Free AMS review

Not sure where your service sits?

A one-hour review of your current AMS: what’s recurring, what could be automated, and what you should expect from a provider. No pitch deck required.

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The future of AMS isn’t about resolving tickets faster. It’s about creating fewer reasons for tickets to exist.

Bring your current AMS setup. An AMS specialist will walk through it with you and suggest a practical next step.