Accelerate and realize the full potential of AI

You’re in the right place.

AI has already proven it can revolutionize every organization (and we do mean *every* organization). But AI also poses real challenges to data privacy and security, so ensuring the responsible use of AI is of the utmost importance.

To help launch your own AI journey, our experts have answered some common questions below. And if you don’t find what you’re looking for – just reach out!

Frequently asked questions

Attending the demo

What should I expect from the demo?

We will guide you through the GRACE Platform’s features using practical real-world examples tailored to your organization’s specific industry and structure. If customization is necessary, we’ll explore your options for AI deployment, development, operation, and governance.

Who in my organization should attend the demo?

AI projects are fundamentally business endeavors. Thus, initial stakeholders are often from business development roles, including C-suite executives. IT usually becomes involved at a subsequent phase, especially when aligning with infrastructure is pertinent.

What industries do you work with? Could you list any common AI applications in my industry?

We collaborate with a diverse range of industries. To get insights into AI applications specific to each industry, please explore our use cases below:

If your industry isn’t listed or you’re seeking more tailored solutions, just share your objectives with us, and we’ll pinpoint relevant use cases tailored to your organization’s needs.

What information should I provide for the demo?

For a tailored demo experience, it would be helpful to share details about your organization’s size, industry, and familiarity with AI. This information assists us in showcasing AI solutions that are most relevant and beneficial to your organization.

Getting started

What resources do I need to start an AI project?

From your side, an AI project requires:

  • data
  • domain expertise
  • a budget
  • management support

On our side, 2021.AI brings AI ​​expertise. Depending on the project’s complexity, you may need AI engineers, data scientists, data analysts, machine learning engineers, or other specialists—all of which we provide through our consulting services.

You will also need infrastructure on which to run the AI. This could be your own cloud, but we can also deploy and develop infrastructure on-premise.

How much data is required to start an AI project? What kind?

The amount and type of data you need to begin an AI project will vary depending on the project, the AI technique, and the problem. There’s no one-size-fits-all answer. The key is to have enough data for the model to generalize from the given patterns and, later, to make accurate predictions when fed new data.

It’s okay if you don’t have an exact idea of your own data needs. Once you’ve shared with us your goals and data capabilities, we’ll schedule a follow-up meeting with a data scientist to provide a data estimate.

Can we extract data from our old system?

We have experience with all types of databases and systems, including legacy systems, and we are happy to discuss this during the demo as well.

Learning the process

How long will it take to implement AI in our organization?

It can take between 1 and 3 months to set up the AI, depending on the project’s complexity.

What are the steps involved in developing an AI project?

An AI project typically involves the following steps:

  • Agreement on scope
  • Data gathering
  • Data preprocessing
  • Data analysis and feature engineering
  • Model selection and training
  • Model evaluation
  • Hyperparameter tuning
  • Testing
  • Deployment
  • Ongoing monitoring and maintenance

We will involve you in the planning and execution of these steps.

Avoiding risks

What risks are involved in an AI project? How can these risks be mitigated?

AI projects, like any technology project, involve certain risks. Identifying and mitigating these risks is key. Common risks and mitigation strategies include:

  1. Data Issues: Data may suffer from issues such as poor data quality, lack of relevant data, privacy concerns, or biases in the data.
    • These risks can be mitigated through rigorous data cleaning, anonymization techniques, diversity in data, and strong data governance policies.
  2. Model Performance: AI models might underperform due to overfitting, underfitting, or not being appropriate for the task at hand.
    • These risks can be mitigated by following best practices in model selection, training, validation, and testing. Ongoing performance monitoring is essential.
  3. Implementation: Technical challenges may arise when integrating the AI ​​solution into existing systems or processes.
    • These risks can be mitigated through careful planning, good project management, and close collaboration between the AI ​​team and the rest of the organization.

At 2021.AI, we have a perfect track record for implementing AI projects on time and within budget.

Maintaining the project

What kind of maintenance is necessary after the AI system is implemented?

Post-implementation, an AI system requires ongoing maintenance to ensure continued performance and value. The GRACE Platform streamlines this process by automating tasks that would otherwise be handled by DevOps and MLOps teams.

For example, GRACE automates data pipelines and model retraining, making it much easier to maintain the AI ​​system. GRACE also automates tasks related to data ingestion, data cleaning, model training, and model deployment. These features ensure optimal system performance over time, with reduced need for manual intervention.

In other words, you don’t need a large, expensive team to maintain your AI system – GRACE has got it covered. This allows you to focus on leveraging AI for your strategic objectives, rather than getting bogged down in the daily details of system management.

What kind of team or expertise should we have in-house? What can be outsourced?

AI projects require multiple areas of expertise, but the specifics depend on your organization’s goals, resources, and the complexity of the project. Typically, an in-house team might need skills in data analysis, business analysis, and project management.

Outsourcing allows your organization to focus on strategic activities while ensuring effective AI model management. Outsourcing is most effective for jobs requiring technical expertise, including tasks related to data science, machine learning, and AI-specific software development.

Measuring results

What ROI can we expect from implementing AI?

The return on investment (ROI) from implementing AI will vary depending on the nature of the project. While it’s difficult to provide a general estimate, it’s important to consider both tangible and intangible benefits. After the initial analysis phase, our team will be better suited to calculate a ROI to support the business case.

How do we measure the success of an AI project?

Measuring the success of an AI project depends on the project’s objectives and use cases. Any evaluation of success must consider technical metrics, business outcomes, and ethical considerations.

Critical metrics include:

  • Technical metrics (e.g. model performance and accuracy)
  • Business metrics (e.g. revenues, savings, time spent on certain tasks, customer satisfaction scores)
  • Adoption and usage
  • ROI
  • Scalability and maintainability

Our team will provide the necessary tools and support to make sure your project is a success!

Calculating costs

Do integrations cost extra?

For licensing costs, the answer depends on the nature of the integration.

For consulting costs, it depends on whether your IT department can work with APIs. If so, 2021.AI does not charge an additional fee for integrations. (Please note that normal IT support fees still apply.)

How much does one AI project cost?

Pricing depends on the nature and complexity of the project. An AI project costs €50,000 on average. We can provide a more precise estimate after the demo when we will have a better idea of your organization’s needs and goals.