Artificial intelligence · Microsoft Copilot and Azure AI
Artificial intelligence applied to your operation, not to a demo
The useful question is not whether your company should use artificial intelligence, but in which specific process and with what expected result. We identify where AI frees up time or improves a decision, test it with a narrow pilot and scale only what proves results.
Who it is for
Who this is for
For executives under pressure to adopt artificial intelligence who need to turn it into a business case they can defend in front of the committee. Also for companies already paying for Copilot licenses that see no difference in the operation. AI stands for artificial intelligence: here, software that drafts, classifies, summarizes, searches and anticipates using the data your company already has.
Signs you need it
- Your company generates data that nobody turns into decisions.
- Skilled staff spend hours reading, classifying, summarizing and replying.
- You already bought Copilot licenses and do not know how to measure whether they paid off.
- You have an AI initiative in this year’s plan, but no use case and no owner.
- Legal or risk stopped an AI trial because there were no usage rules and no control over the information.
- You need to decide between using the capabilities included in your licenses and building something of your own.
Problems it solves
- Data that exists and goes unused
- The information sits in the ERP, in email, in documents and in tickets, but nobody consults it when deciding because getting to it costs more than the decision itself. AI changes the cost of asking.
- Repetitive cognitive work that does not scale
- Reading requests, classifying email, drafting standard replies, summarizing meetings and extracting data from documents consume the time of people whose judgment should be spent on the exceptions.
- Trials that never reach production
- Many initiatives stop at an impressive demo with no owner, no integration with the real systems and no metric that justifies continuing. The result is internal skepticism and burned budget.
- Risk of exposing information
- When the team turns to public AI tools on its own to work faster, company information leaves without any control. Banning it does not solve the problem: offering a governed alternative does.
- Answers that sound right and are wrong
- An assistant without access to the right data, or without verification rules, produces content that is plausible and incorrect. With no review mechanism, that error travels through the company as if it were validated data.
Capabilities by process
Copilot at the desk
- Assisted drafting and review in Word and Outlook
- Assisted analysis and formulas in Excel
- Summaries of meetings, decisions and action items in Teams
- Search across the company’s documents and conversations
- Use cases defined by role, with real usage measured
Copilot in Dynamics 365
- Assistance in Business Central for reconciliations, descriptions and administrative tasks
- Opportunity summaries and drafted sales follow-up in Dynamics 365 Sales
- Suggested replies and case summaries in Dynamics 365 Customer Service
- Cash flow, late payment and inventory forecasting inside the ERP
Agents and automation
- Conversational agents over your company’s knowledge base with Copilot Studio
- Automatic classification and routing of incoming requests
- Data extraction from invoices, orders and scanned documents
- Triggering Power Automate flows from the result of the analysis
Data and analytics
- Data preparation and source unification with Microsoft Fabric
- Natural language questions over Power BI dashboards
- Anomaly detection in transactions and consumption
- Forecasting models for demand, collections and turnover
Models and development
- Custom solutions on Azure AI Foundry and Azure OpenAI
- Semantic search over your own documentation
- Document processing and image recognition
- Integration of the models with Business Central, Dynamics 365 and internal applications
AI governance
- Review of permissions and information classification before enabling assistants
- An internal acceptable use policy and training for the team
- Audit logging of what is queried and what is generated
- Mandatory human review in the processes where an error has consequences
Outcomes and KPIs
Hours freed from repetitive cognitive work
Classifying, summarizing, drafting and extracting data stop taking up the team’s time. The saving is measured on the process you choose, before and after the pilot: we do not publish general percentages, because they depend on the starting point.
Suggested indicators
- Handling time per request
- Documents processed per person
- Cases resolved without escalation
- Hours spent drafting and summarizing
Decisions made with the information available right now
Asking questions about your own data stops requiring an available analyst. Leadership gets the context when it needs it, not when the report is ready.
Suggested indicators
- Time between the business question and the answer backed by data
- Queries resolved without involving the data team
- Decisions documented with evidence
Pilots that reach production
A narrow scope, with an owner, a metric and real integration from the start, is what separates a trial that gets adopted from a demo that gets forgotten.
Suggested indicators
- Use cases taken to production versus use cases tested
- Active users of the implemented use case
- Acceptance rate of the suggestions generated
AI use that is governed and auditable
The team works with approved tools, on information it is entitled to see, with a log of what is queried. The risk stops being off the radar.
Suggested indicators
- Use of unauthorized AI tools
- Information exposure incidents
- Percentage of processes with a defined human review step
How BETABOX works
Identifying and prioritizing use cases
We review your processes and rank the candidates by impact and by effort. We discard the ones that sound good and move no indicator. The initial assessment is 100% free.
Verifying data and permissions
Before building anything we review what information exists, what shape it is in and who can see it. If permissions are disorganized, that is the first job: an assistant inherits the access of whoever uses it.
A narrow pilot with an agreed metric
We implement one specific use case, with real users, integrated into the systems where the process happens, and with a metric defined up front to decide whether it continues or is dropped.
Integration with your systems
We connect the solution to Business Central, Dynamics 365, Microsoft 365 or your own applications, so the result lands where the work happens and not in a separate tool.
Adoption, governance and scale-up
We train by role, leave the usage policy in writing and scale only what proved a result. Whatever did not work is documented and closed: that is useful information for the committee too.
Continuous quality monitoring
We periodically review the quality of what the system produces, the cases where it fails and the cost of running it, and we adjust. AI is not a project you hand over: it is a capability you maintain.
Integrations
- Microsoft 365 Copilot in Word, Excel, PowerPoint, Outlook and Teams
- Copilot in Dynamics 365 Business Central, Sales and Customer Service
- Copilot Studio for agents over your company’s knowledge base
- Power Automate and Power Apps to bring the result into the process
- Microsoft Fabric and Power BI for data and analytics
- Azure AI Foundry and Azure OpenAI for custom solutions
- Microsoft Purview for information classification and auditing
- Your own applications and third-party systems through APIs
Security, governance and local adaptation
- AI does not create new permissions
- An assistant reaches exactly what the user who invokes it can reach. That is why the first deliverable of any AI project is the access review and the classification of sensitive information.
- Confidentiality of your company’s information
- We work on Microsoft’s enterprise services, inside your organization’s environment and with the security controls of Microsoft 365 and Azure. The specific contractual terms on data processing are reviewed with you and your legal counsel before signing.
- Human review where errors are costly
- In processes with financial, legal or human consequences, the system proposes and a person decides. We explicitly define the points where that review is mandatory.
- Acceptable use policy
- We put in writing which tools are approved, what information may not leave the company and how an incorrect result gets reported. Without clear rules, the team improvises with public tools.
FAQ
Frequently asked questions
- Do we need our data in order before we start?
- You do not need a data project first, but you do need to know what information exists and who can see it. We start by assessing that reality and design the pilot around it, improving data quality only where the use case requires it.
- Should we use Copilot or build something of our own?
- It depends on the case. If the work happens inside Microsoft 365 or Dynamics 365, the sensible move is usually to take advantage of what is already included before developing anything. When the process is specific to your business or requires integrating your own data, we evaluate a solution on Azure. That comparison is part of the assessment.
- What happens to the confidentiality of our information?
- We work inside your organization’s enterprise environment, with the identity, permission and audit controls of Microsoft 365 and Azure, and we define what information each assistant can reach. The contractual terms for data processing are reviewed with your legal counsel before any deployment.
- How long until we see results?
- It depends on the use case, on data availability and on user participation. That is why the pilot is defined with a metric and a decision point agreed up front: if it does not move the indicator, it is dropped and the lesson is documented.
- Is artificial intelligence going to replace our team?
- The goal we pursue is to free your team from repetitive work so they can apply judgment where it is needed. In practice, the cases that work are the ones that absorb volume and leave the exceptions to people.
- How do we keep the system from giving incorrect answers?
- By narrowing the scope to your company’s information, citing the source of each answer whenever possible and requiring human review in processes with consequences. We also measure output quality continuously and adjust.
- What do we need internally to sustain this?
- A business owner for the use case and users willing to give feedback. We cover the technical side and the maintenance; what we cannot replace is the judgment of the person who knows the process.
Which specific process would artificial intelligence help you with today?
Request a use case assessment. We review your processes and your data, prioritize by impact and effort, and propose a pilot with an agreed metric. The initial assessment is 100% free.
Contact
Request a complimentary assessment
Tell us what your company needs and a BETABOX advisor will get in touch to schedule a call at your convenience. The initial assessment is 100% free of charge.
- +1 (754) 209-2071
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