Target picture
What role should AI play in the company? Is it first about efficiency, better decisions, access to knowledge, service quality, sales preparation or internal automation?
AI strategy
An AI strategy that does not stay stuck in a concept paper
Many mid-sized companies know that AI is becoming relevant. The real question is where it makes sense to start. Aviando helps you assess AI potential, prioritise concrete use cases and build a roadmap that fits your processes, data and teams.
Impact →
Sales prepService supportKnowledge workMarketing processesManagement summariesWorkflow automation
Effort → Sales prep - quick win - low effort, tangible relief
An AI strategy does not begin with a tool
A good AI strategy does not first ask which model, which platform or which agent should be used. It first clarifies which work should become easier, faster or better.
In mid-sized companies in particular, this calls for a pragmatic framework. Many organisations have grown-up systems, scarce resources and teams that are already heavily loaded. That is why AI must not become yet another innovation project running alongside day-to-day work.
We build a strategy with you that starts close to real processes. Which tasks tie up time? Which decisions need better information? Where do manual handovers occur? Which risks need to be contained?
The result is not an abstract vision, but a clear roadmap for sensible AI use.
Overview
Why AI strategy is becoming important for mid-sized companies
AI has now arrived in many people’s everyday lives. Inside the company, however, productive use often stays unclear. Individual employees test tools, departments collect ideas and managers expect efficiency gains. But without a shared direction, isolated solutions appear quickly.
That is especially risky for mid-sized companies. When AI is used without direction, clear rules for data, quality, accountability and security are missing. But when planning drags on too long, opportunities go unused.
A good strategy creates the middle ground. It makes visible which use cases are realistic, which foundations are missing and where a first pilot can quickly show impact.
Typical starting points
Many ideas, but no prioritisation
Numerous AI ideas emerge in workshops, teams or leadership circles. But nobody knows exactly which of them are feasible in the short term, economically sensible or strategically relevant.
Approach
Our approach
First understand where work originates
We start with real workflows. Which activities repeat? Where do people search, copy, summarise or follow up? Which information is missing at the decisive moment?
Assess use cases in a structured way
Every idea is classified by value, effort, data needs, risk, user group and feasibility. The result is a prioritised list instead of a collection of good intentions.
Defer the tool decision deliberately
Whether Agentforce, an AI platform, custom AI agents or classic automation make sense follows from the use case. We avoid early commitments when the actual need is not yet clear.
Think about governance pragmatically
AI needs guardrails. These include rules for data, approvals, human control, responsibilities, documentation and quality. For mid-sized companies, governance has to stay understandable and usable.
Build a roadmap that stays implementable
In the end you get a plan with clear steps. What can be tested immediately? Which foundations need to be created first? Which use cases come later? Which teams are involved?
Overview
What an AI strategy should contain
Use case portfolio
Which concrete use cases exist in sales, service, marketing, management or internal workflows? Which of them are quick to test and which need more preparation?
Data and system map
Which information sits in Salesforce, ERP, documents, the website, email, knowledge bases or other tools? Which sources may be used and which stay protected?
Technology path
Which solution fits the use case? Agentforce in the Salesforce context, an AI platform for broader use, a custom AI agent for individual workflows, or classic automation to begin with.
Governance and responsibility
Who may use AI for what? Which data is allowed? How are results reviewed? Who decides on approvals, risks and further development?
Enablement plan
Which teams need training? Which roles do key users take on? How do we make sure AI is not just tried out but used sensibly?
Measurability
Which metrics show impact? Options include time saved, processing speed, answer quality, data quality, usage, acceptance or fewer manual steps.
Use cases
Typical use cases to start with
Sales preparation
Summarise account information, structure call notes, prepare follow-ups and derive relevant next steps from existing data.
Service support
Triage tickets, find knowledge articles, draft suggested replies or prepare complex cases with more context.
Marketing processes
Create briefings, prepare content variants, summarise campaign data and improve website content in a targeted way.
Internal knowledge work
Make documents, policies, project knowledge or past cases easier to find, so teams do not have to search for long.
Management summaries
Condense reports, trends, customer information or project status so decisions can be prepared faster.
Workflow automation
Connect recurring tasks across systems, transfer data, trigger notifications or prepare multi-step processes.
Services
Our services
AI strategy workshop
Together we analyse the starting point, goals, processes and first use cases. The result is a clear assessment of where AI can sensibly be tested in the short term.
Use case prioritisation
We assess ideas by value, effort, data needs, risk and acceptance. The result is a prioritised portfolio instead of a cluttered collection of ideas.
AI readiness check
We review data basis, systems, permissions, process quality and organisational prerequisites. This shows which use cases are immediately possible and where foundations should be improved first.
Technology recommendation
We help you classify Agentforce, AI platforms, custom AI agents, automation and existing tools. The recommendation follows the use case, not a preferred product.
Governance concept
We develop pragmatic guardrails for usage, data, quality, responsibility and human control. The aim is safety without unnecessary bureaucracy.
Roadmap and pilot design
We define concrete next steps, pilot scope, participants, success criteria and technical prerequisites. That way strategy connects directly to implementation.
Impact
What this improves
A good AI strategy creates orientation before time and budget flow into individual measures.
- Managers see more clearly which AI potential is realistic. Teams understand where AI can help in everyday work. IT and data protection get a framework for safe use. Departments receive concrete starting points instead of abstract promises.
- That turns uncertainty into an implementable roadmap.
Use case assessment Circle size = effort
- 01: Knowledge work: high value, low effort
- 02: Sales preparation: easy to implement, tangible relief
- 03: Service support: high impact, needs a clean knowledge base
- 04: Marketing processes: well plannable, medium effort
- 05: Management summaries: fast start, limited depth
- 06: Workflow automation: high impact, several systems involved
Right moments
When an AI strategy is especially worthwhile
When many ideas are on the table
You know AI is relevant, but a clear prioritisation is missing.
When tools are already in use
Teams experiment with AI, but rules, quality and responsibilities are not yet cleanly settled.
When Salesforce or other systems are to be involved
Data and processes do not sit in isolation. That is why it must be clarified early which system landscape is relevant.
When management expects measurable impact
AI should not just be tried out. It should save time, improve quality or support decisions.
When uncertainty is noticeable in the company
Employees need orientation on what is allowed, sensible and safe.
Honestly
When an AI strategy is not enough
Strategy does not replace implementation. If a use case is already clear, data is available and responsibilities are defined, a pilot can be the better next step.
In other cases, enablement comes first. If teams have little experience with AI, practical training and guardrails often help more than a large strategy document.
Aviando helps you choose the right entry point. Sometimes that is a strategy workshop. Sometimes a pilot. Sometimes training. Sometimes a better process.
Related topics
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Learn more Intro call
Want to know where AI can sensibly start in your company?
Chris Uhrig, CTO & Co-Founder of Aviando, looks at your processes, systems and possible AI use cases with you. Together you clarify which potential is realistic, which risks to keep in mind and what a pragmatic first step can look like.
Chris Uhrig
CTO & Co-Founder