Introduction
According to the 2023 WEKA report, 69% of companies have AI projects running, and 28% have fully integrated AI into their operations. The focus now shifts from just using AI to making it a key player in business growth.
This guide offers practical tips for companies looking to improve operations, enhance customer service, and make smarter decisions with AI. Learn how to use AI not just for the sake of it, but to gain a competitive edge and lead in your industry.
The Roadmap to AI Integration
Businesses are rapidly adopting Artificial Intelligence (AI) and Machine Learning (ML) not only to reduce costs but also to drive revenue growth. This shift sees AI/ML as crucial for developing innovative profit systems, refining sales strategies, and enhancing offerings.
If you are thinking, about what to start with, look no further — this guide answers all your questions.
Identify Objectives
Adopting AI requires clear objectives. For instance: Reducing customer service response times by 25%.
Here are some other metrics you can target: cutting operational costs through automation, increasing sales with targeted strategies, improving product quality, or making your supply chain more efficient.
Remember to set SMART goals and clear KPIs to be able to measure your success.
Assess Your Current Setup
Even though Artificial Intelligence (AI) and Machine Learning (ML) have a lot of promise for changing businesses, about 80% of AI projects don’t make it past the experimental stage. This issue mainly comes from the complex needs of creating and using these models, like needing a lot of computing power and good data. However, even small businesses can overcome these challenges.
- Computational Power: Leveraging cloud services like AWS, Google Cloud, or Microsoft Azure can provide the necessary computational resources without the need for significant infrastructure investment.
- Data Quality and Accessibility: Using public datasets, data marketplaces, and AI tools for data preparation can overcome the hurdles of acquiring and processing high-quality data.
- Expertise: Partnering with specialized AI service providers, such as SciForce, offers access to expert skills and experience, facilitating customized AI solution development.
- Integration and Maintenance: Choosing AI solutions that seamlessly integrate with existing systems and ensuring ongoing updates and maintenance are crucial for sustaining AI initiatives.
Gather a Cross-Functional Team
Assemble a team with diverse skills, including members from the relevant business unit, IT, and the ones with specific AI skills you need. This ensures the project benefits from different perspectives. You can also turn to a service provider with relevant expertise.
Choose Your AI Tools and Technologies
Select the AI tools that best match your objectives and current setup. There are many options available, so choose those that align with your specific needs. Here we share a list of tools that our AI\ML specialists use when developing projects for our clients.
- Databases
Qdrant is a vector database optimized for AI and ML applications, offering high performance, flexibility, and secure hosting options. Its user-friendly API, fast and accurate search capabilities, advanced filtering, support for rich data types, and scalability make it ideal for a wide range of uses, including semantic search engines and AI assistants. Qdrant stands out for allowing the storage of additional information alongside vectors, enhancing data analysis and utility.
2. Machine Learning
MLFLOW is a flexible MLOps platform supporting a wide range of research beyond machine learning. It simplifies project tracking, visualization, and model management, making it easier to organize and analyze experiments without the hassle of numerous .csv files.
3. Speech Processing Frameworks
Icefall is a toolkit developed by speech research expert Daniel Povey, designed for advanced speech processing and featuring models like Zipformer for real-time voice recognition. This toolkit facilitates immediate and precise speech understanding.
4. Deep learning
Whisper by OpenAI is an advanced model designed for recognizing and transcribing speech in various languages. It’s highly effective for applications needing precise transcription across different accents and situations.
More ML, Speech Processing, and Deep Learning frameworks are in the full article on our website.
5. Data Science
The DoWhy library is a tool for data science focused on finding cause-and-effect relationships in data. It lets you see these connections visually going beyond simple correlations, measure how factors in the data affect each other, and find out which features cause unexpected changes. It also allows for what-if analyses, helping predict the outcome of changes in the data.
Monitor and Adjust
In managing AI initiatives, it’s critical to regularly assess their impact and adapt as needed. Define key performance indicators (KPIs) relevant to each project, such as process efficiency or customer engagement metrics. Employ analytics tools for ongoing monitoring, ensuring continuous alignment with business goals.
Read on to learn how to assess AI performance within your business.
Making AI Work for Your Business
AI transforms business operations by enhancing efficiency and intelligence. It upgrades product quality, personalizes services, and streamlines inventory with predictive analytics.
Computer Vision
Computer Vision (CV) empowers computers to interpret and understand visual data, allowing them to make informed decisions and take actions based on what they “see”.
- Manufacturing: Fast and accurate defect identification
- Retail: Analyzing customer behavior for tailored marketing and optimized store layouts.
- Inventory Management: Real-time stock monitoring, automating restocking, and maintaining optimal inventory levels.
Case: EyeAI — Space Optimization & Queue Management System
Leveraging Computer Vision, we created EyeAI — SciForce custom video analytics product for space optimization and queue management. It doesn’t require purchasing additional hardware or complex integrations — you can immediately use it even with one camera in your space.
- Customer Movement Tracking: Our system observes how shoppers move and what they buy, allowing us to personalize offers, improving their shopping journey.
- Store Layout Optimization: We use insights to arrange stores more intuitively, placing popular items along common paths to encourage purchases.
- Traffic Monitoring: By tracking shopper numbers and behavior, we adjust staffing and marketing to better match customer flow.
- Checkout Efficiency: We analyze line lengths and times, adjusting staff to reduce waits and streamline checkout.
- Identifying Traffic Zones: We pinpoint high and low-traffic areas to optimize product placement and store design, enhancing the overall shopping experience.
Targeted for HoReCa, retail, public security, and healthcare sectors, it analyzes customer behavior and movements and gives insights into space optimization for better security and customer service.
Natural Language Processing
NLP enables computers to understand and respond to human language, automating tasks to enhance accuracy, reduce costs, and support business growth and customer engagement.
- Customer Service: Uses chatbots to provide instant, accurate responses.
- Sentiment Analysis: Analyzes feedback across platforms to understand customer opinions.
- Automated Document Processing: Streamlines the management of text data, from emails to contracts.
Case: Recommendation and Classification System for Online Learning Platform
We improved a top European online learning platform using advanced AI to make the user experience even better. Knowing that personalized recommendations are key (like how 80% of Netflix and 60% of YouTube views come from them), our client wanted a powerful system to recommend and categorize courses for each user’s tastes.
The goal was to make users more engaged and loyal to the platform. We needed to enhance how users experience the platform and introduce a new feature that automatically sorts new courses based on what users like.
We approached this project with several steps:
- Gathering Data: First, we set up a system to collect and organize the data we needed.
- Building a Recommendation System: We created a system that suggests courses to users based on their preferences, using techniques that understand natural language and content similarities.
- Creating a Classification System: We developed a way to continually classify new courses so they could be recommended accurately.
- Integrating Systems: We smoothly added these new systems into the platform, making sure users get personalized course suggestions.
The platform now automatically personalized content for each user, making learning more tailored and engaging. Engagement went up by 18%, and the value users get from the platform increased by 13%,
Conclusion
Using AI and ML helps reduce costs, grow revenue, and make better products. It’s all about setting big goals, using the right tech, managing resources wisely, and having a great team. It’s not just about staying ahead — it’s about being a leader in innovation.
Learn how SciForce turns AI ideas into real business wins on our website. For more insights, check out the full version of the article.
