AI Discovery  & Exploration

Do you want to start your AI projects
with a test?

INTRO

How to test AI ideas in action?

Before going “all in” with AI, you want to start small.
The best way to do this is to choose one business case in your organisation that can help you prove whether projected benefits may occur.
Your AI Partner will help you pick AI solutions and tools.
The landscape of AI platforms is changing weekly. We haven’t seen such growth before. For this reason, you need a technical partner who will assist you in picking the right tools for the job. It is not about picking ChatGPT but the whole ecosystem of tools and specialised systems for the job.
The best way to validate your AI idea is to test it in action.
Using a sample of your internal data, you can see if you picked the right business challenge to solve it on a small scale. This way, you improve your chance of getting a green light and budget for a full solution.

THE PROCESS

Let’s start with a Proof of Concept

Once we define AI business cases during the AI Assessment & Strategy phase we choose THE STRONGEST USE CASE and builid a Proof of Concept for it during the Discovery & Exploration phase. If it is successful, we will move forward to develop MVP and a fully scalable solution.

LET’S TALK ABOUT A PROOF OF CONCEPT

Case study

MANUFACTURING

Industry 4.0 Production Optimisation with AI

We created an AI and Machine Learning platform that optimises production processes, speeds up machines, and assists operator teams. We proved that the production process can be supported in real-time while taking into account external parameters such as temperature, type of raw materials used, and humidity.

Result:

Prediction of machine settings was 75%
Costs of future infrastructure maintenance have been reduced to the minimum
We proved that VertexAI solution enables faster model training and optimisation for an on-premise infrastructure

ACTION PLAN

AI Discovery & Exploration

Let’s find out how you can benefit from AI – optimising costs, improving efficency, maximise customer experience…

1. AI Use Case Deep-Dive

Describing business context of the one use cases - problems, goals, metrics, main functionalities, benefits for the end user and the company

2. AI Technology Exploration

Exploring available technologies, solutions and tools to facilitate the business requirements of the use case

3. AI Prototyping & Validation

Setting up a prototype to validate the use case with the explored technologies based on sample data and API structure towards KPIs and business metrics

4. AI Solution Overview

Refining the use case, selecting appropriate technologies with data requirements specification, and outlining implementation approaches based on deep-dive analysis, technology exploration, and prototyping

END RESULT

6 scenarios after building a Proof of Concept

Successful Proof of Concept with Clear Business Value

The AI solution demonstrates significant improvements in efficiency, accuracy, or customer satisfaction.
Scenario: The company decides to proceed with full-scale implementation, integrating the AI solution into their existing systems and processes.

Partial Success with Identified Improvements

The AI solution shows promise but requires further refinement to meet all business requirements.
Scenario: The company decides to iterate on the proof of concept, addressing identified weaknesses and optimizing the model.

Technical Success but Lack of Business Fit

The AI solution performs well technically but does not align with the company's strategic goals or provide sufficient ROI. The company explores other AI opportunities that better fit their business needs and may repurpose some of the developed technology.

High Potential but Requires Significant Investment

The AI solution shows high potential but requires substantial investment in infrastructure, data acquisition, or talent to be viable. A detailed roadmap is created to outline the steps and resources needed for full implementation.

Discovery of New Business Opportunities

During the proof of concept, new applications or business opportunities for AI are discovered. Additional pilot projects may be initiated to validate new use cases, leading to potential new revenue streams or operational efficiencies.

Failure Due to Data or Technical Challenges

The AI solution fails to achieve the desired performance due to technical limitations or data issues. The company decides to invest in further research and development to overcome the technical challenges and in improving data collection, storage, and management systems to support AI initiatives.