STREAM 02 AI-ENABLED BUSINESS PROCESSES
Improve the work. Then see what AI could do.
We analyse one existing business process from trigger to outcome, identify where work should be eliminated, simplified, automated or augmented, and turn the best opportunity into a controlled pilot proposal.
AI is one possible intervention — not the default answer.
Every activity is challenged before a tool is selected. This prevents an inefficient process being reproduced with expensive technology and makes the AI proposal more credible.
Remove work that no longer creates value.
Reduce steps, hand-offs and unnecessary variation.
Use rules or workflow where the decision is deterministic.
Use AI where language, patterns or judgement support creates value.
Retain accountable human work where consequence or context demands it.
The process stream runs on the AI-ready data stream.
A useful AI function depends on the information available at each step of the process. The proposal therefore tests the same Outcome, Data, Control and Proof structure already used across AI DATA FOR YOU.
01OutcomeA process result worth improving
A named owner, an operating baseline and a measurable target define why the process should change.
Open the full readiness test ↗02DataEvidence the AI can rely onThe required records, documents and knowledge are trusted, traceable, permitted and available at the right moment.
Open the full readiness test ↗03ControlA safe functional boundaryPeople know what AI may read, create or recommend, who approves actions and when the process must stop or escalate.
Open the full readiness test ↗04ProofA pilot result the business can judgeCycle time, quality, value, overrides and incidents are compared with the current process before any scale decision.
Open the full readiness test ↗DATA READINESS GATE
No trusted evidence, no responsible pilot.
If profiling reveals unreliable identity, definitions, quality, lineage, permissions or ownership, the proposal makes those dependencies explicit and routes the work to the relevant data solution before AI is allowed to act on it.
Diagnose. Fix. Make It Last.
The second stream uses the same three-stage structure as our data services. Each stage keeps the business function, required data and accountable people connected.
Understand the work before choosing the AI.
Reveal how the process actually operates, where value is lost and which activities deserve to change.
Map the trigger, activities, decisions, hand-offs, roles, systems, documents and data used from start to finish.
Measure time, cost, delay, rework, errors and risk, then classify each activity: eliminate, simplify, automate, augment with AI or keep human.
WHAT YOU LEAVE WITH
- Current-state process and dependency map
- Business-value and friction baseline
- Prioritised improvement opportunities
Design the smallest AI capability worth proving.
Connect the target process improvement to the exact information, function, integration and control it requires.
Define what the AI must read, retrieve, classify, predict, recommend or create — and test whether the necessary data is trusted, permitted and accessible.
Design the future workflow across people, rules, conventional automation and AI, including exceptions, hand-offs and prohibited actions.
Specify one bounded use case, representative evidence, tool approach, acceptance tests, measures, cost, timing and a clear go / prepare / stop gate.
WHAT YOU LEAVE WITH
- Future-state process and AI role
- Use-case data contract and functional requirements
- Decision-ready controlled pilot blueprint
Keep people accountable and the value visible.
Build ownership, monitoring and change controls into the proposal so a successful pilot can become dependable work.
Name who reviews, approves, corrects, escalates and can stop the AI, then prepare the people affected by the redesigned process.
Compare results with the pre-AI baseline and define the quality, value, risk and data thresholds that must be met before scale.
WHAT YOU LEAVE WITH
- Accountable operating model
- Process, AI and data monitoring scorecard
- Controlled adoption and scale pathway
Business function first. AI action made explicit.
Each proposal describes the operational task in plain language: what AI can do, which data makes it possible, where a person remains accountable and what result must improve.
Finance
Invoice and payment exceptions
- What AI can do
- Extract invoice facts, compare them with purchase orders and supplier rules, explain likely mismatches and prepare a prioritised review queue.
- AI-ready data dependency
- Supplier master, purchase orders, receipts, invoices, approval limits and historical exception outcomes.
- Human control
- Finance approves corrections, supplier communication and every payment change.
- Prove it with
- Handling timeFirst-pass matchPayment accuracyCost per exception
Customer Service
Case understanding and response drafting
- What AI can do
- Assemble a source-linked case summary, retrieve approved knowledge, draft a response and route unusual or high-risk cases to the right specialist.
- AI-ready data dependency
- Verified customer identity, case history, product or service records, entitlements, policies and approved knowledge.
- Human control
- An adviser verifies facts and retains control of material commitments, complaints and vulnerable-customer decisions.
- Prove it with
- Resolution timeCorrection rateCustomer outcomeEscalation accuracy
Sales
Opportunity preparation and follow-up
- What AI can do
- Create account briefs, identify evidence-backed next actions, draft meeting follow-up and flag pipeline records that need attention.
- AI-ready data dependency
- Customer and contact master, consent, CRM activity, products, pricing, proposals, service history and pipeline definitions.
- Human control
- The account owner validates context, chooses the action and approves every external communication.
- Prove it with
- Preparation timeFollow-up timePipeline completenessConversion quality
Operations & Supply
Demand, inventory and service exceptions
- What AI can do
- Forecast a range, detect unusual demand or delivery patterns, explain the drivers and recommend which exceptions planners should review first.
- AI-ready data dependency
- Orders, inventory, lead times, suppliers, capacity, service levels, product hierarchy and exception history.
- Human control
- Operational owners approve schedule, stock, supplier and customer-impacting changes.
- Prove it with
- Forecast errorStock-outsExpedite costService level
People Services
Employee policy and request support
- What AI can do
- Retrieve the applicable approved policy, summarise a request, draft routine guidance and route sensitive or ambiguous matters to an authorised specialist.
- AI-ready data dependency
- Current policies, employee identity and role, request history, entitlements, effective dates and access permissions.
- Human control
- HR makes employment, performance, remuneration and eligibility decisions; the AI provides support, not judgement.
- Prove it with
- Response timeRouting accuracyPolicy consistencySensitive-case containment
Risk & Compliance
Control evidence and issue triage
- What AI can do
- Collect linked control evidence, compare it with policy requirements, summarise gaps and draft an auditable review pack for accountable owners.
- AI-ready data dependency
- Policies, controls, obligations, evidence, incidents, decisions, owners, dates and approved risk taxonomy.
- Human control
- Risk owners determine compliance, accept risk, approve remediation and sign off formal reporting.
- Prove it with
- Review effortEvidence completenessIssue ageingFalse alerts
AI Process Discovery & Pilot Blueprint.
The engagement ends with a proposal the business can approve, challenge or stop — not a vague list of AI ideas and not an obligation to buy implementation.
Process & value baseline
A current-state map with volumes, time, cost, quality, rework, risk and accountable owners.
AI opportunity decision
A transparent recommendation showing what to eliminate, simplify, automate, augment or retain as human work.
Data dependency map
The exact sources, fields, documents, owners, quality thresholds, permissions and gaps behind the proposed capability.
Functional & control design
Required AI actions, integrations, exceptions, human checkpoints, prohibited actions and escalation paths.
Pilot plan
A bounded cohort, representative cases, tool approach, acceptance tests, delivery plan, cost range and risk controls.
Proof & next-step gate
Baseline comparison, success measures and a clear go, prepare or stop recommendation before implementation spend.
DELIVERY BOUNDARYBlueprint first. Pilot implementation separately approved.
If the recommendation is to proceed, the controlled pilot can be scoped as a separate engagement. If the data is not ready or the value is weak, the proposal says so and identifies the smaller preparation step instead.
BRING ONE REAL PROCESS
Discuss the process ↗