Data & AI
Turn the data you already have
into decisions you can act on.
DataDecision
Your ERP, CRM and operational systems already contain signals about what is happening across the business. This page helps you decide which decision should improve, which capability is appropriate, what control that consequence requires and whether it is worth scaling.
AI does not remove weak-data risk. It can amplify it.
In short
Data is not the outcome. A better decision is.
This is the decision layer above our specialist analytics, automation, predictive and agent propositions. It exists to help you choose the simplest capability that would genuinely change something, and to be honest about when the answer is not AI.
What this page helps you answer
- Which decision would create value if it improved?
- Is this a reporting, automation, information, prediction or coordination problem?
- Is the data behind that decision usable enough?
- What level of AI is actually justified?
- What control does the consequence require?
- Can the outcome be measured?
- Should this scale, stay small or stop?
The data problem
Most organisations do not have a shortage of data. They have a shortage of usable decisions.
The gap is rarely the absence of information. It is the distance between the information and the moment somebody has to act.
If several of these are familiar, the first question is which decision should get better, not which technology should be bought.
Reports arrive too late
The business understands the problem after the opportunity to act has passed.
Data does not agree
Different teams produce different versions of the same number.
Reporting is manual
Teams spend time assembling information instead of understanding it.
Dashboards show what happened
But not necessarily what needs attention next.
AI experiments are disconnected
Interesting prototypes exist without a clear operating decision behind them.
Automation stops at simple tasks
Workflow, insight and decision support have not yet been connected.
AI does not fix weak data. It makes the consequences of weak data faster.
Data does not need to be perfect. The information required for the specific decision must be sufficiently understood and reliable for the risk involved.
Decision first
Do not start with the model. Start with the decision.
A technically impressive AI model with no operational decision attached to it has limited business value.
- What decision are we trying to improve?
- Who makes it?
- How frequently?
- What information do they currently use?
- What happens if the decision is late?
- What happens if it is wrong?
- Can the outcome be measured?
- What action follows the insight?
From data to action
Build intelligence in the right order.
Each stage makes the next one credible. Skipping straight to prediction usually produces something impressive that nobody acts on. Not every organisation needs to reach every stage.
01
Trust
Make the relevant information usable and governed.
02
Understand
See what is happening and why.
03
Automate
Remove predictable repetitive coordination.
04
Predict
Identify what may happen next.
05
Act
Put the insight into the workflow where a decision can be made.
06
Learn
Measure what happened and improve the capability.
Choose the capability
Five capabilities. One question each.
These are distinct choices rather than a ladder. Most organisations need one or two of them well, not all five.
Data & analytics
What is happening and why?
Governed reporting with agreed definitions, so the same question produces the same answer across teams.
- Power BI
- Microsoft Fabric where justified
- Governed data
- Cross-system insight
Automation
What predictable work should happen consistently without manual coordination?
If a process is deterministic, automation may be better than AI. It is more reliable, cheaper to run and easier to govern.
- Power Automate
- Power Platform
- Dynamics 365 workflow
- Integration
AI assistance
Where does understanding or creating information consume unnecessary effort?
Grounded in approved organisational information, with a person still accountable for what is produced.
- Summarisation
- Retrieval
- Drafting
- Classification
- Extraction
- Knowledge assistance
Predictive intelligence
What future risk, constraint or opportunity would be useful to know earlier?
The value is warning time: enough notice for the accountable person to change something while there is still a choice.
- Operational history
- Current signals
- Focused models
- Attention, not answers
Agentic AI
Where can information, reasoning and approved actions be coordinated towards a defined task?
Boundaries before autonomy. An agent is designed around identity, permitted actions, approval and audit.
- Investigate
- Assemble context
- Prepare action
- Request approval
- Execute a controlled action
Plain definitions
These technologies work together. None of them supersedes the others.
- Reporting & analytics
- Understand what happened and what is happening.
- Automation
- Execute known deterministic rules.
- Generative AI
- Interpret, retrieve, summarise and create information.
- Predictive models
- Estimate a defined future outcome, risk or opportunity.
- AI agents
- Coordinate information, reasoning and approved actions towards a defined task.
Trust the foundation
The business owns meaning. Technology implements it.
For every important data concept, qualification establishes what it means, where it comes from and who is accountable for it.
- What does this concept actually mean?
- Which source is authoritative?
- Who owns the definition?
- Who owns its quality?
- Who is permitted to use it?
- How current does it need to be?
Deeper analytics governance, semantic definitions and report ownership sit on the Power BI & Microsoft Fabric page.
A prediction can be mathematically correct and operationally useless.
Control by consequence
The higher the consequence, the stronger the control.
This is a conceptual model to guide qualification. It is not a scoring system and it is not a legal classification.
Lower impact
Summarise internal information.
Standard access controls, source visibility and normal review.
Medium impact
Recommend which operational exception deserves attention.
Named owner, evaluation against real cases, monitored acceptance rates.
Higher impact
Prepare or perform a transactional action.
Human approval, execution limits, full audit trail and rollback path.
Restricted or additionally governed
Employment, clinical, safeguarding, eligibility, regulated or similarly high-impact decisions.
Separate assessment before any design work. Autonomy is not assumed and may not be appropriate at all.
What decides the tier
- Business impact
- Data sensitivity
- Autonomy
- Reversibility
- Confidence
- Regulatory context
- External or customer impact
Human accountability
Design the level of autonomy. Do not inherit it from the technology.
Not every action requires the same human involvement, and Level 4 is not appropriate everywhere. High-impact decisions should not be automated simply because they could be.
01
Level 1: Information
AI provides information only. The person does everything else.
02
Level 2: Recommendation
AI suggests. A person decides.
03
Level 3: Prepared action
AI prepares the action. A person approves execution.
04
Level 4: Controlled low-risk automation
A pre-approved, reversible action inside defined boundaries.
AI suggests
The capability proposes, with the reasoning visible.
Person reviews
Someone accountable checks the suggestion in context.
Person decides
Ownership of the decision stays with the business.
System acts
The approved action is executed in the connected systems.
Outcome recorded
What happened becomes evidence for the next decision.
Agent governance
An agent should have boundaries before it has autonomy.
The question is not whether the agent can do it. The question is under what conditions the agent should be allowed to do it, and who owns the answer.
Design controls
- Approved actions
- Approved systems
- Data access
- Role and identity
- Human approval
- Execution limits
- Audit trail
- Retries
- Error handling
- Escalation
- Model choice
- Data classification
Production considerations
- Purpose
- Named business owner
- Model and version
- Approved users
- Tool permissions
- Evaluation set
- Monitoring
- Cost
- Incident handling
- Change control
- Suspension
- Retirement
Design
Purpose, boundaries, data, tools and approval defined.
Evaluate
Tested against a representative set before anyone relies on it.
Approve
A named owner accepts the capability and its limits.
Deploy
Released to approved users inside the workflow.
Monitor
Behaviour, cost, acceptance and failures watched in operation.
Change
Model, prompt, tool or scope changes go through control.
Suspend / retire
Withdrawn when value, risk or conditions change.
Agent security
An agent should not treat every piece of information it reads as an instruction it is authorised to follow.
- Untrusted content
- Prompt injection
- Excessive permissions
- Unintended action chaining
- Tool misuse
- Sensitive-data exposure
- External content influencing actions
- Malformed tool output
- Execution loops
- Approval bypass
Evaluation
A technically successful model is not automatically a successful business capability.
Evaluation happens at three levels, and a capability can pass one while failing another.
Technical
Does the capability perform its intended task? Accuracy, completeness, false positives and negatives, hallucination, grounding quality, consistency, latency, regression and behaviour by model version.
Operational
Can people use and trust it inside the real workflow? Human acceptance, tool-selection correctness, action correctness, approval behaviour, access-boundary adherence, failure recovery and guardrail behaviour.
Business
Does it improve a measurable decision or outcome enough to justify what it costs to operate?
InteliSense Nexus
A framework for connecting data, knowledge, agents and actions.
InteliSense Nexus is our architecture and governance framework for connecting enterprise data, organisational knowledge, AI agents and controlled actions. It is a way of structuring the design, not a software product.
01
Data
The operational information the organisation already creates.
02
Knowledge
Approved organisational context a capability may draw on.
03
Agents
Defined tasks working within permitted boundaries.
04
Actions
Controlled activity in connected systems.
Governance surrounds the whole chain
- Identity
- Data access
- Model choice
- Knowledge
- Approved tools
- Permitted actions
- Human approval
- Logging
- Evaluation
- Monitoring
Nexus is described here as a framework and method. We do not present it as a software product, a SaaS platform, a Microsoft product or an autonomous-agent platform.
Prove before scale
Without a baseline, improvement cannot be evidenced.
Before anything is built, the value hypothesis makes the commercial argument explicit. No ROI is claimed until it has been measured.
Current decision
The decision or problem as it works today.
Current cost
The delay, risk, rework or effort it currently carries.
Behaviour change
What somebody would do differently.
Capability
The simplest capability that could support that.
Measure
The agreed measure of whether it improved.
Cost to operate
What running it will cost, not just building it.
Scale decision
Whether the evidence justifies going further.
Potential value measures, where relevant
- Warning time
- Avoidable administration
- Backlog
- Decision latency
- Rework
- Intervention rate
- Accepted recommendations
- Service performance
- Working-capital exposure
Proof of value
A small working capability is more useful than a large AI strategy nobody has tested.
- Decision
- Data
- Baseline
- Capability
- User
- Action
- Measure
- Review
Confidence gates
Four decision points, each with a permitted answer of stop.
A gate is a decision point rather than a warning. Each one re-confirms the conditions before the next stage of investment.
Gate 1
Before qualification closes
Value and fit
Establish that there is a real decision here, and that AI is not being applied to something simpler capability would solve better.
Confirmed at this gate
- The business decision
- The current problem
- The decision owner
- The action that would follow
- A measurable outcome
- Whether reporting or automation could solve it instead
Decision
Proceed · Use a simpler capability · Reframe the problem · Stop
Gate 2
Before design
Data and risk readiness
Establish that the required information exists, is owned and can be used safely for a decision of this consequence.
Confirmed at this gate
- Required data and its owner
- Quality sufficient for the risk
- Access, security and architecture
- Risk classification
- Human control model
- Regulatory constraints where relevant
Decision
Proceed · Prepare the data foundation first · Reduce scope · Stop
Gate 3
After the proof
Proof of value
Establish whether the capability changed a real decision, not whether the demonstration was impressive.
Confirmed at this gate
- Baseline comparison
- Technical performance
- Operational usefulness
- User acceptance
- Control effectiveness
- Measurable decision impact
- Expected operating cost
Decision
Production candidate · Improve and repeat · Stop
Gate 4
Before deployment
Production readiness
A capability entering the operation needs an owner and an operating model, not just a working model.
Confirmed at this gate
- Named owner
- Identity and access
- Model and version
- Integrations
- Monitoring and logging
- Human approval
- Incident handling and rollback
- Cost controls, support, adoption and change governance
Decision
Deploy · Remediate first · Do not deploy
Operating lifecycle
A model or agent is an operating capability, not a one-off development project.
Identify
Find decisions worth improving.
Qualify
Assess value, data, risk and feasibility.
Prepare
Create the required data and knowledge foundation.
Prove
Test against a baseline with realistic scenarios.
Govern
Define access, oversight and controls.
Integrate
Put the capability into the operating workflow.
Deploy
Release to the people who will rely on it.
Adopt
Prepare the people using it.
Measure
Compare outcomes with the baseline.
Operate
Monitor behaviour, cost, drift and failures.
Improve / scale
Extend only where evidence supports it.
Re-evaluate / retire
Withdraw a capability that no longer earns its place.
Retirement is a successful outcome
Withdrawing a capability is a legitimate governance decision, not a failure, where any of the following becomes true.
- Operating conditions changed
- The value disappeared
- Costs exceeded benefits
- A simpler capability replaced it
- The risk became unacceptable
Data & AI assessment
Which capability is the likely starting point?
Seven questions about the decision, the problem, the data, the action, the risk, readiness and measurement. The output is a recommended next route, not an architecture decision.
Indicative signal
Answer the questions and we will show a likely starting point. This is a recommended next route rather than an architecture decision, and sometimes the honest answer is that AI is not the right tool. Please keep every answer at organisational level: no customer, personal or case information.
Your answers are carried through, so you will not be asked to repeat them. Final qualification is always a conversation.
Customer routes
Where the work starts depends on what is already true.
Data & AI is a capability domain beneath the three routes. Most existing Microsoft customers start with Optimise.
Optimise
Where existing Microsoft systems already hold data and processes that could produce better reporting, automation, prediction, assistance or governed agent workflows.
Explore OptimiseTransform
Where the data strategy, operating model, priorities and decision ownership still need defining.
Explore TransformRecover
Where a data, BI or AI programme has lost confidence, become over-engineered, over budget, poorly adopted, untrusted, unowned or stuck in proof-of-concept.
Explore Recover
Specialist propositions
Power BI & Microsoft Fabric
Reporting design, KPI and semantic definitions, report governance, data freshness, the dashboard estate and when Fabric is genuinely justified.
See the analytics and data decision modelPredictive Intelligence
Operating models, demonstrations, the model workspace, limitations, evaluation, monitoring and predictive maturity.
See the operating models and demonstrationsPower Platform
Where Power Apps, Power Automate and Dataverse fit, and how low code is governed so it reduces complexity rather than adding to it.
Explore Power PlatformIntegration & data migration
Moving and connecting information between systems, and deciding what history genuinely needs to move.
Explore Integration
After it works
Scale on evidence, not on enthusiasm.
A deployed capability can progress through operate, monitor, optimise, extend and scale only where the evidence supports it. Not every successful proof should automatically scale.
Operate
The capability runs with an owner and a support model.
Monitor
Behaviour, acceptance, cost and drift are watched.
Optimise
Improve what the evidence says is weak.
Extend
Additional decisions, teams, data or workflows where value is proven.
Scale
Only where measured evidence supports it.
Microsoft ecosystem
Technology follows the use case.
None of this is mandatory, and more platform is not automatically more value. Where enterprise agent governance is required, Microsoft platform capabilities such as Agent 365 may form part of the architecture, but they are not assumed for every agent solution.
- Dynamics 365
- Business Central
- Power BI
- Microsoft Fabric
- Power Platform
- Dataverse
- Microsoft 365
- Microsoft Foundry
- Azure services where genuinely required
Evidence
Say what each piece of evidence actually proves.
We classify Data & AI evidence in four levels so nobody has to guess how strong a claim is.
Level 1: Demonstration
Shows what the capability could look like. Proves design intent, not outcome.
Level 2: Proof of value
Tested against representative or customer data, against an agreed baseline.
Level 3: Production use
Operating inside a real workflow.
Level 4: Verified outcome
Measured against an agreed baseline and confirmed.
The predictive models shown on our Predictive Intelligence page are demonstrations and illustrative examples. They are not customer evidence. The testimonials below prove general InteliSense Microsoft delivery and customer relationships. They do not prove AI, Microsoft Fabric, agent deployments, predictive models, Nexus or any specific business outcome.
Customer voice
What customers say about working with us.
Customer voices
InteliSense customers
Hear our customers talk about InteliSense
How we work
We start with the operational decision, not the AI demonstration.
Business first
Find a decision worth improving.
Data reality
Understand what information actually exists.
Simplest suitable capability
Do not use AI where automation is more reliable.
Prove early
Test the use case against a baseline before scaling it.
Control by consequence
The higher the impact, the stronger the control.
Integrate into work
Put insight where people can act on it.
Measure the outcome
Determine whether anything useful changed.
Re-evaluate
Improve, scale or retire on evidence.
Common questions
Questions leaders ask about Data & AI.
Start with the decision
What would you decide differently if you knew earlier?
Show us a business decision that depends on ERP, CRM or operational data. We can help determine whether reporting, automation, AI assistance, prediction or a governed agent would make that decision easier to act on, and whether it is worth doing at all.