AI CONSULTING / BUSINESS ENVIRONMENT, DATA, ADOPTION
AI Consulting for Business
We help you understand how AI is already used, select a suitable managed environment, establish data rules and move from individual use to shared assistants, applications and automation.
People already use AI, but the business often does not know how
Employee interest is rarely the main gap. The business lacks visibility, a managed environment and a shared path from personal experiments to company use.
Usage is outside company visibility
People work in different personal or free accounts. The business does not know who uses which tool, with what data, or what happens to access when a role changes or an employee leaves.
Licenses are selected without requirements
A familiar brand or one successful demonstration drives the decision. Identity management, SSO, roles, audit capabilities, data handling, integrations and real user needs are not evaluated.
There are no shared rules for company data
It is unclear what can be entered into AI, what may be shared, how long content is retained, which sources can be connected or when a person must review the result.
Experience remains with individuals
Everyone develops a separate way of working. The business does not share tested assistants, instructions, knowledge sources or approved connections, and it cannot decide what belongs in an application or automation.
What makes business AI use manageable
Buying licenses or delivering training is not enough. Technology, identities, data, rules, practical adoption and future solutions must work together.
Current-use inventory
We identify the tools and accounts people already use, the work they perform and where the business has lost visibility or control.
- Tools, accounts, licenses and user groups
- Common tasks and data sources in use
- Informal extensions and connected applications
- Risks, constraints and team requirements
Target AI environment
We translate business needs into criteria and compare suitable platform categories and license levels without automatically promoting one vendor.
- Central administration, domain, SSO and account lifecycle
- Roles, permissions, audit and reporting capabilities
- Shared knowledge, assistants and ways of working
- Connectors, APIs and open integration standards
Data and AI governance
We establish practical rules for AI use: who may use what, with which data, for what purpose and with what level of review.
- Approved tools and accountable owners
- Data classification, sharing and retention
- Approval of sources, extensions and connections
- Human review, accountability and incident response
Adoption, applications and automation
We test use in real work and prepare a path from approved practices to solutions connected to company data and systems.
- Practical scenarios and role-based training
- Shared instructions, assistants and knowledge sources
- Processes selected for integration or automation
- Pilot, measurement, operational owner and further development
A business license is more than an invoice addressed to the company
Depending on the platform and license level, a managed environment may add domain verification, single sign-on, centralized roles, automated account provisioning and deprovisioning, audit records, retention controls or administration of shared content.
These capabilities are not identical across products or plans. We first define what the business actually needs and then compare the available options. The outcome is a justified environment design that the company can manage safely, rather than a generic model ranking.
Shared know-how instead of private experiments
Value does not come from every employee maintaining a separate chat. A business can share tested instructions, templates, assistants, knowledge sources and ways of working. It also needs to decide who owns, tests, approves and updates them.
The same rules apply to extensions, connectors and integrations through APIs or MCP. Every connection may gain access to additional data or perform actions, so it needs a known owner, bounded permissions and a managed lifecycle.
AI governance means enablement with control, not a ban
AI governance combines approved tools, identities, data classification, accountability for outputs, human review, a register of approved uses, monitoring and a response to change or an incident. Rules must be specific enough for day-to-day work, not just a general policy stored on the intranet.
This is not a legal or security audit. For sensitive areas, we involve the appropriate internal or external specialists and place technical decisions within the controls for which the company is accountable.
From safe use to custom solutions
Not every need requires custom development. An approved practice in an existing application may be enough; another case may need a shared assistant over company knowledge. Repeated work across systems may require an integration, automation or custom application.
We compare opportunities by value, data, feasibility and risk. Only the selected use case receives a bounded pilot, real samples, failure scenarios, human review and success conditions defined in advance.
Experience with ERP, data and operations shapes the design
Business AI does not end in a standalone chat. It needs identities, permissions, documents, processes and connections to productivity tools, ERP, CRM or approvals.
David Máj connects AI with business systems and day-to-day operations. His published projects include controlled document processing, agents working with CRM and company data, and financial document automation with traceable status.
How to establish AI as a managed business capability
The exact scope adapts to company size and the current situation. The sequence prevents licenses from getting ahead of needs, data and accountability.
Inventory current use
We review tools, accounts, users, ways of working, data types, connected services and the expectations of different business areas.
Design the target environment
We define requirements for identity, administration, data, sharing, audit capabilities, integration, cost and user support.
Compare platforms and license levels
We evaluate specific plans against business requirements and recommend an option or combination that can be managed over time.
Establish rules and validate adoption
We prepare access management, data rules and approved scenarios. A selected group validates real use and the training required.
Prepare the solution roadmap
We prioritize suitable assistants, applications, integrations and automation, then define a measurable pilot for the first use case.
Experience Connected to Business Operations
David Máj’s published experience covers AI document processing, company data, CRM, productivity tools and Dynamics 365 Business Central in connected workflows.
David Máj / TechOne
Business systems, data and AI- SmartDocto: AI document extraction, validation, approval and transfer to a downstream system.
- AI agents: company data, web research, Dynamics 365 and Outlook in one traceable workflow.
- Financial documents: Outlook, SharePoint and Dynamics 365 Business Central connected with processing status.
Frequently Asked Questions
Where should a business start with AI adoption?
First determine what people already use, with which data and for what work. This produces requirements for the managed environment, account administration, rules and user support. Only then should the business select license levels and plan applications or automation.
What can a managed business AI environment provide?
Depending on the platform and plan, it may include centralized user administration, domain verification, SSO, automated provisioning, roles, audit and reporting capabilities, data controls and shared assistants or knowledge. Specific capabilities must always be verified because they are not included in every business license.
Why are personal accounts not enough for company use?
The issue is not only whether a provider uses content to improve models. The business needs to manage identities, access, sharing, connected applications, employee departures and rules for sensitive data. Terms and settings can also differ across personal accounts, while the company usually lacks centralized administration.
What is AI governance?
It is the management of business AI use: approved tools, owners, identities, data rules, human review, connection approvals, use-case records, monitoring and a response to change or an incident. Its purpose is to enable AI use with clear accountability and practical boundaries.
Does adoption include employee training?
Yes, but training follows the selected environment, rules and the real work of each role. A general demonstration does not create adoption. People need approved scenarios, practical guidance, shared resources and a place to return experience and issues.
Can you also design shared assistants and connections to business systems?
Yes. Once the environment, data and accountability are clear, we can design shared assistants, company knowledge access, connectors, API or MCP integrations, custom applications and automation. Each solution receives bounded permissions, an owner and a testing method.
What do we receive from the first phase?
Depending on the starting point, the output may be a current-use audit, requirements and an environment comparison, an administration and policy design, an adoption plan, or an application and automation roadmap. We agree on the exact scope and tangible deliverables before work begins.
Related Solutions
Other areas that can be part of the same project.
Describe how the business uses AI today.
You do not need to have a selected product or a finished brief. Describe current accounts and tools, data questions or the area you want to change with AI.