Business impact
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AWSMTECH · Managed Intelligence
Data & AI strategy
Turn your data and artificial intelligence into tangible value for your business.
“Don't start with AI. Start with what your business wants to achieve with it.”
Managed Intelligence
Turn your data and artificial intelligence into performance drivers.
Artificial intelligence opens up new possibilities for businesses: automating certain tasks, finding information faster, assisting employees, analysing data or improving certain processes.
But deploying AI tools without a strategy can also multiply solutions, expose sensitive information and generate investments whose value remains hard to demonstrate. AWSMTECH helps you build a pragmatic Data & AI strategy aligned with your objectives, your data, your constraints and your level of maturity.
- Understand
- Prioritise
- Govern
- Deploy
- Measure
Business question
AI is not a strategy
Technology comes after the business question.
Microsoft Copilot, ChatGPT, AI agents, automation, intelligent analytics… The possibilities are many and evolving fast.
Yet the first question should not be: “Which AI tool should we buy?”
But rather: “Where can AI genuinely create value in our organisation?”
- Reduce the time spent on certain administrative tasks?
- Make better use of internal knowledge?
- Speed up information retrieval?
- Improve certain processes?
- Support analysis or decision-making?
AWSMTECH helps you move from an interest in AI to a clear, actionable roadmap.
Data
Your data is the starting point
Useful AI also depends on the quality of the information it can access.
Businesses already hold a significant amount of information:
- Emails
- Files
- SharePoint
- Teams
- CRM
- ERP
- Documents
- Financial data
- Knowledge bases
- Business applications
But this information may be scattered, duplicated, hard to find or accessible to more people than necessary.
Before introducing new AI uses, it is therefore essential to understand:
- What data do we have?
- Where is it?
- Who can access it?
- Which data is genuinely useful?
- Which data is sensitive?
- What information can we use with AI?
This is where Data strategy and AI strategy come together.
Roadmap
From ambition to roadmap
- 01
Understand
Identify the organisation's objectives, operational pain points and the processes that could benefit from data, automation or AI.
- 02
Assess
Review the digital environment, available data, Microsoft 365, existing uses and the organisation's level of maturity.
- 03
Identify
Select concrete use cases of genuine interest to the business.
- 04
Prioritise
Compare opportunities by potential value, complexity, risk and prerequisites.
- 05
Experiment
Start with controlled scopes to validate the relevance of uses before wider adoption.
- 06
Deploy & measure
Scale the uses that deliver enough value and track their adoption and results.
- Discover
- Assess
- Prioritise
- Experiment
- Deploy
- Measure
Use cases
Identifying the right use cases
Not everything that can be automated deserves to be.
One of the main challenges of an AI strategy is separating interesting experiments from uses capable of delivering real value. AWSMTECH helps you analyse opportunities across several dimensions:
Users involved
Who will genuinely benefit from the solution?
Data required
Is the necessary information available, sufficiently structured and accessible under the right conditions?
Complexity
What level of effort is needed to implement the use case?
Risk
Does the use case involve confidential information, important decisions or sensitive processes?
Value
Does the expected gain justify the investment and the changes required?
The goal is to build a realistic, prioritised portfolio of initiatives.
Microsoft Copilot
Microsoft 365 & Copilot: preparing the ground
For many SMEs already using the Microsoft ecosystem, the arrival of Copilot is gradually changing the way information is accessed and work gets done.
But switching on an AI feature is not a strategy.
AWSMTECH can support your thinking around:
- Your Microsoft 365 usage
- How information is organised
- Access rights
- Governance
- Planned Copilot uses
- Employee adoption
- Awareness of new risks
Before asking AI to use your information, make sure you control who can access that information.
Shadow AI
Shadow AI: AI may already be in your business
Even if you have not officially deployed anything yet.
Employees naturally experiment with new tools when they help them save time. The challenge for the business is to frame these uses without needlessly blocking innovation.
A Data & AI strategy must therefore also answer simple questions:
- Which tools may be used?
- With which data?
- For which uses?
- What requires human validation?
- Which information must not be entered into certain services?
The goal is not simply to prohibit. It is to enable innovation within a controlled framework.
Governance
Govern before you scale
Responsible AI starts with understandable rules.
Governance does not have to take the form of a complex document dozens of pages long. For an SME, it must above all give employees clear answers.
Uses
Which AI uses are authorised, discouraged or prohibited?
Data
Which categories of information may be used?
Tools
Which services and platforms are approved by the organisation?
Accountability
Which decisions always require human validation?
Security
How do we protect the identities, access and information involved in these new uses?
Adoption
How do we help employees use the authorised tools effectively?
This governance moves you from “everyone experiments on their own” to a structured company-wide approach.
Security
Data & AI: security from the outset
Security should not be added once the AI project is finished. It must be part of its design.
- Business
- Data
- Security
- Governance
- AI
This is precisely where our dual expertise in managed IT and cybersecurity sets us apart from a pure AI consultancy. AWSMTECH brings a view of the environment as a whole: technology, users, identities, access, data, cybersecurity and governance.
Pilot
From strategy to pilot
Start small. Measure. Then decide.
Not every artificial intelligence idea immediately requires a large-scale project. For some uses, the best approach is to start with a controlled scope:
- One process.
- One team.
- One clearly identified problem.
- One measurable objective.
Experimentation then helps you understand:
- Is the solution genuinely useful?
- Are users adopting it?
- Is the data suitable?
- What risks or constraints emerge?
- Does the use case deserve to be scaled?
AI thus moves from a technology demonstration to a business decision based on results.
Value
Measure value, not just adoption
Buying licences is not an indicator of transformation. Even the number of active users tells only part of the story.
A Managed Intelligence strategy seeks to understand what the technology actually changes.
Depending on the use case, indicators may cover:
- Time spent on certain tasks
- Reduction in manual steps
- Speed of access to information
- Adoption of a new process
- Quality or consistency of certain deliverables
- Reduction in repetitive tasks
The goal is not to use more AI. The goal is to get more value from it.
Maturity
From generative AI to agents
As uses mature, artificial intelligence can go beyond individual assistance. An organisation may start by using AI to search, summarise or produce content, then gradually explore scenarios combining AI, data and automation. Our 4-level journey:
- 01
Assist
AI helps the employee.
- 02
Augment
AI enriches certain tasks and decisions.
- 03
Automate
Certain repetitive steps are automated within a defined framework.
- 04
AI agents
Agents can take part in certain processes with appropriate controls and supervision.
Why AWSMTECH
Why AWSMTECH?
Business before technology
We start by understanding the objectives and the problems to solve.
Data + AI
We see data as a central element of any artificial intelligence strategy.
IT + Cybersecurity + Intelligence
New uses are integrated into a broader view of your digital environment.
A pragmatic approach
Prioritising a few relevant use cases rather than multiplying experiments.
A focus on governance
Developing uses while maintaining a framework suited to the organisation's data and risks.
A local partner
An approach designed to support Swiss SMEs and organisations in their digital transformation.
Frequently asked questions
Frequently asked questions
It means defining how an organisation wants to use its data, artificial intelligence and automation to support its objectives, while setting the priorities, governance rules and prerequisites.
Not necessarily. It depends on the use case. The key is to identify the information genuinely needed and check that it can be used under the right conditions.
Not always. Copilot can be relevant for organisations already deeply integrated with Microsoft 365, but the strategy must start from needs and uses rather than from a product.
Strategy mainly answers: why and where do we want to use AI? Governance answers: under which rules and with which controls do we want to use it? The two are complementary.
Yes. That is precisely one of the goals of our support: starting from the company's processes and challenges to identify and prioritise relevant opportunities.
That is exactly why the approach should be structured: to clarify authorised tools, data and uses, and to raise employee awareness of the associated risks.
Managed Intelligence
Don't do AI just because everyone is talking about AI.
Do it when it can solve a problem, improve a process or create genuine value for your organisation. AWSMTECH helps you move from “We should be doing something with AI.” to “We know where AI can bring us value, what data it needs and how to deploy it in a controlled way.”
Schedule a callA question about your Data & AI strategy? Write to us