How do I get started with AI in my organization? 4 stages of implementing artificial intelligence
Krystian Bergmann
Artificial intelligence (AI) has ceased to be a futuristic vision and has become a viable tool that is revolutionizing business. Many companies see the potential of AI to optimize processes, increase efficiency and gain competitive advantage. However, implementing AI in an organization can seem complicated and overwhelming. It is important to approach the implementation process in an orderly and efficient manner.
AI implementation is a process that can be divided into four key stages.
Step 1: Identify needs and challenges in the organization
If you feel that AI in your organization is essential - then you already know your need. You want automation, acceleration of work, and of course - cost savings. However, in order to structure these needs and identify the most pressing challenges in your organization, it is important to have a specialist guide your organization through the process. It is worthwhile to use what we call a "zero workshop", which will be a good reflection of the team for further action.
Workshop zero AI-Primer delivers:
- Understanding the impact of AI on business.
- Market overview - how are your competitors working with AI?
- Identifying which implementations are becoming the market standard?
- Identification of AI implementation opportunities.
- A strategic approach to implementation.
- Improve the competence of the team.
- Examples of Quick Wins and ways to identify them
Stage 2: AI Product Design Sprint
During the AI PRIMER, you gain a solution best suited to your organization's current needs and further deepen. Once you know the most pressing challenges in your organization and have Quick Win examples, you can move on to further intensive work and the next stage, the AI Product Design Sprint.
AI PDS is a process that allows in-depth analysis of selected ideas.
We use workshop exercises to analyze the potential of projects. We focus on understanding the user's perspective, identify functional requirements, and finally focus on matching technology to business requirements.
What do you gain in stage 2, i.e. during the design sprint?
- Defining the Business Challenge: We work with you to specify what problems in your company can be solved with data.
- Determine the role of the user or customer in the process
- Searching for Optimal Solutions: We analyze existing models , to find the ones that best fit your needs.
- General Data Management Guidelines: We provide key recommendations for effectively managing your data.
- Establish Monitoring and Evaluation Methods: We develop a plan to monitor and verify that the implemented solution is delivering the expected results.
Main sections of the workshop:
- Problem - What do we want to solve? What question are we asking?
- Users and stakeholders - Who will benefit from the model? Who makes the decisions?
- Business value - What impact is the solution expected to have on the business?
- Data - What kind of data do we have? What is the quality of the data?
- ML engineering - What kind of model do we need? What will its architecture look like?
- Success metrics - How will we measure the effectiveness of the model?
- Development stages - What does the implementation plan look like? What are the milestones?
- Risks and constraints - What can go wrong? What are the legal, ethical or technological constraints?
- User/customer path - how will the user use the product or service?
Why is this stage important?
Above all, we strive to create a consistent vision of the project for the entire team - from data analysts to executives. This ensures that everyone has a clear understanding of the purpose and scope of the work. Next, it is crucial for us to precisely match the machine learning model to real business needs, which guarantees its practical application and effectiveness. It is also important to proactively identify potential gaps and risks at an early stage, even before the programming work begins, thus avoiding costly mistakes and delays in the future. After the AI Product Design Sprint stage, the organization has concrete ideas for AI implementation that are tailored to its needs and capabilities.
#caseStudy
NewGlobe, looking to speed up the creation of teacher guides for expansion, has partnered with Netguru. Using GenAI, the time required to create a single guide was reduced from 4 hours to just 45 seconds. This significantly accelerated its entry into new markets and enabled it to adapt content to local curricula more quickly. As a result, NewGlobe gained the ability to quickly scale and personalize educational materials, resulting in higher operational efficiency and customer satisfaction (NPS 9). The implemented GenAI-based solution has strengthened NewGlobe's position as an innovative leader in EdTech.
Stage 3: AI PoC Pilot
After the phase of exploring the possibilities that artificial intelligence brings, and after carefully defining the business problem and pre-selecting potential solutions, comes a key moment in the AI implementation process - the Proof of Concept (PoC) pilot stage.
Before deciding on a full-scale deployment, which involves a significant investment in time, money and resources, conducting a carefully planned pilot becomes an indispensable step in minimizing risk and verifying the real value of the chosen AI solution in a specific operational environment.
The AI PoC pilot is nothing more than a controlled test environment in which the selected artificial intelligence solution is implemented on a limited scale, under real business conditions, but without full involvement of all the organization's systems and processes. Its main purpose is the practical verification of the assumptions behind the selection of a given AI technology. It allows assessing how the algorithm handles real data, how it integrates with existing infrastructure, how users respond to it, and whether it generates the expected business results.
The effectiveness of a POC is measured by metrics that assess whether an AI solution has the potential for further development and implementation in a given business context. Key KPIs at this stage focus on technological feasibility, process fit and data quality.
Examples of KPIs monitored during POC include:
- Accuracy of algorithm performance: Does the model achieve a minimum acceptable performance (e.g., >80% accuracy) to justify further work? This helps determine the potential of the solution with a limited data set and under controlled conditions.
- Compatibility of the solution with the business process: How well can the technology be embedded in an existing process? KPIs may include the number of process modifications needed, integration time, or level of manual intervention.
- Technical stability and performance: Response time, resource consumption, ease of deployment in a test environment. The POC is to verify that the solution runs smoothly and reliably on a small scale.
- Data quality: Verification that the available data is sufficient to train and operate the model. KPIs may include indicators of data completeness, consistency and relevance.
- User acceptance (initial): Although limited in scope, it is worth assessing whether end users see value in the solution - for example, through qualitative feedback or basic usability testing.
Stage 4: AI MVP - full implementation
After successful pilot testing, we are confidently proceeding with the implementation of MVP - Minimum Viable Product, the first fully functional production version of our AI-based solution. This milestone is characterized by deep integration of AI into the operational foundations of our company. We are seamlessly integrating the new technology with our key systems and tools, such as CRM (Customer Relationship Management) for smarter customer relationship management, ERP (Enterprise Resource Planning) to optimize business processes, CMS (Content Management System) to support content personalization, as well as with our mobile applications, ensuring the availability of intelligent features across all platforms.
In parallel with the technical integration, a key element of this stage is the comprehensive training of our team. We ensure that every employee, regardless of department, is fully prepared to use the new AI tool effectively and to correctly interpret the results and analysis it generates. We invest in training programs that not only teach how to use the interface, but also explain the logic behind the algorithms, building understanding and confidence in the new technology.
To ensure full transparency and facilitate data-driven decision-making, we implement advanced dashboards that visualize AI-generated key performance indicators (KPIs). These intuitive dashboards provide real-time, clear and understandable information about the effectiveness of the deployed solution. In addition, we configure an alert system that automatically notifies you of significant events and deviations, allowing you to react quickly and manage proactively.
MVP implementation is not the end of the process, but an important milestone. We continuously monitor the effectiveness of the implemented solution, analyzing its impact on key business metrics and gathering user feedback. Based on this data, we are actively planning a roadmap for the further development of our AI system, identifying areas for optimization and new opportunities to apply artificial intelligence to our organization.
The strategic implementation of AI brings tangible benefits to our company. Automating repetitive tasks significantly saves time and resources, allowing our team to focus on more strategic initiatives. Using AI to optimize processes and personalize offerings builds a sustainable competitive advantage, offering our customers faster, better and more tailored solutions. What's more, the deep personalization of offerings directly translates into increased revenue and increased customer loyalty. Ultimately, making decisions based on reliable data and analytics provided by AI eliminates guesswork and significantly increases the accuracy of our business choices.
Want to identify your organization's challenges and see how AI can help you spread your wings. See more about the AI PRIMER workshop: https://www.concordiadesign.pl/ai-primer/
