People - expertise
Routine to the machine
Context.
When does AI become necessary?
AI is needed where there is already a process, but it is stuck due to manual work and the human factor.
Your current foundation
Your current situation
- Slows down because of routine
- Depends on the individual
- Gives different results
- Stops outside of working hours
How AI fixes it
- AI performs monotonous tasks
- The same result for every customer
- The process works without interruption
- People are engaged in expertise
«We have been working in the field of IT automation of business processes for 20 years. Thanks to the implementation of ERP and CRM, we observe the same picture: at first, the process is based on people, then it accumulates routine, and then delays, errors, overload, and quality degradation begin:
- people get tired;
- New employees take a long time to get into the context;
- the rules are unevenly enforced;
- The process begins to depend on a person's memory, attention, and discipline.
This is where the need for AI arises. It helps to reduce the impact of routine, ensuring consistent results.
Issues.
Where AI gives a quick and clear effect
Sales
Some leads do not reach the manager on time and die at the start. AI closes the initial contact in seconds, engages in a dialog, and passes the "warm" lead to the manager.
HR / Recruiting
Recruiters spend most of their time on the same type of communication instead of evaluation. AI takes over the routine: conducting the initial dialog, responding to candidates, and collecting basic information.
Service / Support
The speed of response directly affects the customer experience. AI responds instantly to typical typical requests and stabilizes the service without expanding the team, reducing the workload.
Internal processes
Processes work as long as the team is small, and then chaos begins. AI structures knowledge, standardizes processes, and removes manual work from typical tasks.
Casey
The most common cases from the heads of departments
- Some appeals were lost
- Responses came with a delay
- Leads were “cooled” outside of working hours
- AI closes the initial contact, responds immediately, and conducts a dialog until it is transferred to the manager.
- +25-30% more processed leads
- Faster response, fewer losses
- Stable flow of leads
- Recruiters spent hours asking the same questions
- The candidates passed the stages in different ways
- Some of them dropped out before the interview
- AI conducts an initial dialog, answers common questions, and collects basic information.
- -50% time for initial contact
- The team focused on evaluating
- The process has become more stable
- Employees kept asking the same thing
- The knowledge base was not used
- Onboarding depended on colleagues
- AI answers internal questions and pulls information from the knowledge base.
- -40% internal requests to the team
- Faster onboarding
- Less distraction for the team
- Documents were generated manually
- Approvals were delayed
- The statuses were getting lost
- AI has automated the generation of documents and made the approval process transparent.
- -30-50% processing time
- Faster document processing
- Control at every stage
Process
Our methodology of work
You show the process. We look at where time, quality, speed, and control are being lost, and whether there is really a point of effect for AI.
Preliminary assessment / audit
We determine where in the process it makes sense to look for an effect and where AI is really justified.
Discovery
We analyze roles, scenarios, bottlenecks, interaction logic, requirements, and solution architecture.
Implementation
Development, integration, configuration, launch, and training of the team.
Support and development
Adjustment, adaptation to real-life scenarios, and development after launch.
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