Read this guide before hiring an AI consulting company

This article will get you ready for AI consulting. You’ll know all the basics, and will be on the same page with the consultants.

Everything you need to know before hiring an AI consulting company – in one guide.

Part 1: What does AI consist of?

We are in the middle of the AI boom. AI has been gaining traction for years, but today even my grandmother heard about it.

Still, AI seems mysterious because it’s full of buzzwords: machine learning, deep learning, data science, data engineering, etc. Let’s break them down one by one.

Artificial intelligence (AI)

Artificial intelligence (AI) refers to systems that use statistical models and large datasets to perform tasks that normally require human intelligence like generating text, analyzing images, writing code, or making predictions.

Today’s AI is still narrow. It’s optimized for specific tasks and objective functions, but it doesn’t truly “understand” or transfer knowledge across domains the way humans do. A model can ace benchmarks or outperform experts in one area, yet struggle outside its training distribution.

Modern foundation models like GPT-5, GPT-4o, Claude 3, Gemini Ultra, and LLaMA 3 are multimodal.

They handle text, images, audio, and video, support tool use and API calls, and assist with reasoning, coding, and content generation. Creative tools like Midjourney and Runway extend this into media synthesis.

Still, none of the current models have consciousness, self-awareness, or cognition. Today, AI is a tool that doesn’t fully replace experts at their jobs.

But we’re already seeing AI move beyond single responses toward multi-step workflows. For example:

• An AI agent can read incoming emails, draft replies, and schedule meetings automatically.
• It can analyze sales data, generate a report, and suggest next-quarter actions.
• It can review code, propose fixes, and run tests before a human even looks at it.

That said, autonomy isn’t the same as accountability. These systems still operate within defined constraints and require human oversight, especially in high-stakes areas like healthcare, education, or finance.

Confused by AI jargon? Basics of AI, data science, machine learning, deep learning, and generative AI – explained as simply as counting on your fingers. Read the article.

Machine learning (ML)

Machine learning (ML) is a subset of AI that relies on adaptive learning, with no explicit programming. So ML engineers don’t pre-program answers for every single input. The model figures out responses on its own.

By contrast, non-ML AI models are rigid and rule-based. Email filters are usually non-ML. If the email has keywords like ‘prize’ or ‘free’, then it’s automatically marked as spam.

Most modern AI systems apply at least some elements of machine learning. That’s why the terms are often used interchangeably. But you can still find non-ML AI models in fields like robotics, speech, or image recognition.

Deep learning (DL)

Deep learning (DL) is a subset of ML that uses neural networks. A neural network works just like our brains. Our brains are made from neurons, and every neuron is linked with thousands of other neurons. AI neural networks work the same way:

To train the model, developers upload real-world input and output data. The AI will try to find connections between input and output. They are called the hidden layers. At first, the hidden layers are random and can be incorrect. However, after each step of training, the connections become more accurate.

Let’s say you want to create a neural network that predicts exam grades.

• First, you need a random hypothesis: to analyze whether attendance equals perfect test scores?
• Test the hypothesis. You train the model on real-world data, but only on student grades and attendance scores. Once the model is trained, you feed slip-up attendance scores of some students and let it predict their grades.
• Adjust. The model checks how accurate the initial predictions of DaVinci are, it adjusts its parameters through a process called backpropagation.
• Iterate. The adjusted model runs the tests again. This process can take hundreds and even thousands of iterations until the developers are satisfied with the accuracy.

The more diverse your input data, the more accurate the results. To estimate exam scores, you may need to collect more than just attendance. It could be a student’s grade history or the time spent on finishing homework.

Wondering how machine learning differs from deep learning? Our detailed explanation breaks it all down for you. Learn more.

Data science (DS)

Data science (DS) is at the crossroads of AI and statistics. Its goal is to extract knowledge from data.

Not all AI is data science, but there’s a huge overlap. When it’s used to gain insights, it’s data science. So, all predictive analytics and recommendation systems are data science. But much of AI isn’t data science: generative AI, speech recognition, natural language processing (NLP), robotics, etc.

The neural network example that predicts grades is also data science.

Data engineering

Data engineering is about the infrastructure for data science. Data engineers build systems that collect, store, and process data. When the data pipeline works smoothly, data scientists extract insights from it.

So while data scientists focus on data insights, data engineers create pipelines to make it all happen.

There are plenty of different algorithms, but we’ll focus on the major types.

Supervised learning models

Supervised learning models learn by example. To train the model, developers feed it with extensive input and output data. As the AI identifies patterns, it can predict outcomes for new data.

There are two main types of supervised models:

• Regression models find connections between variables. You can visualize such connections in a chart with X and Y axes, as in the picture above. If a coffee shop sells a cappuccino for $5, you can calculate the price of 10 cups. That’s called linear regression.
• Classification models sort items into categories. Imagine a full fruit bowl. If I ask you to pick all the bananas out, you’ll instinctively look at the shape, color, and so on. To people, classification is simple and automatic. We don’t need to have seen a billion bananas to tell them apart. But computers need a ton of input, and that’s why we still have captchas (although bots have become better at it).

Supervised models are great when you have lots of labeled data. They are perfect for content recommendation systems, automated grading, and language learning tools.

Unsupervised learning models

Unsupervised learning doesn’t have human guidance. Developers feed AI with large datasets and let it find patterns on its own.

Clustering is a popular type of unsupervised learning. It’s similar to classification, except the data is unlabeled.

Let’s say I ask you to separate some gemstones by type. Even if you have never seen a gemstone, I bet you can tell rubies and emeralds apart. All because they look completely different: one’s reddish, the other is greenish.

Use unsupervised learning when you need to identify trends, organize content, or cluster students.

Semi-supervised learning

Semi-supervised learning is like having a smart assistant with partial instructions. It combines labeled and unlabeled data to learn. For instance, if you label a few pictures of apples and bananas and leave the rest unlabeled, the algorithm uses the labeled data to classify the rest.

This method works well when labeling data is time-consuming or expensive, but you still want accurate results.

Reinforcement learning models

Reinforcement learning models are trained with feedback.

The algorithm interacts with the environment and receives feedback – either rewards or penalties. The AI learns to maximize actions for long-term rewards.

Back to the gemstone example. Imagine that you need to pick all rubies out of 10 stones, but you don’t know what it looks like. If you pick the right gem, you can keep it. If you make a wrong choice, you’ll get hit with a stick. That’s reinforcement learning.

Use reinforcement learning models when you have a dynamic environment. With such models, you can simulate physics experiments, create adaptive learning paths, or power your interactive learning games.

AI can be applied to virtually any industry, transforming workflows and decision-making processes. Most often, when we talk about AI today, we mean generative AI – the most popular and impactful technology in the field. It complements traditional AI by creating content like text, images, or even music.

Generative AI vs. discriminative AI

If you want to use AI, you need to know the difference between generative and discriminative AI.

The basics are pretty simple: generative AI generates new data, while discriminative models classify data sets into different groups.

So, a generative AI will make a picture of a banana. A discriminative AI can tell bananas apart from apples.

Here’s how to tell if you have generative or discriminative AI. It is Gen AI when the output is content – like text, audio, image, video, or any combination of those. It’s not generative if the output is mathematical: a probability, a number, or a classification (apples or bananas?).

To see how generative AI is making an impact in personalized learning, streamlined content creation, and other areas, check out the detailed material.

AI chatbot

Chatbots generate responses, so they have to be generative – right? Well, no. Chatbots rely on both generative and discriminative models.

In fact, the first chatbots were not even AI-based. They just searched for keywords in your prompt and outputted a pre-written response. That’s also how search engines work at their core.

Most chatbots today are either discriminative or generative. They use machine learning algorithms to find out your intent and will respond based on context. Others say you want to order pizza with a chatbot and type “I want some pizza today”

• A non-AI chatbot will see the “pizza keyword” and list their pizza toppings: cheese, meat, or pepperoni. But if you tell the chatbot that you don’t like their pizza and want something else – the chatbot will still list pizza options. All because it doesn’t understand context, it just sees the “pizza” prioritized and assumes that you want pizza.
• A discriminative AI chatbot will understand the context. With it, your prompt can be more abstract: “I want a pizza for 4 people with some tropical toppings”. And it will suggest an extra large pizza with pineapple.
• A gen AI chatbot feels more natural. The output is not just a list of suggestions. A modern generative AI chatbot can hold up a conversation, so it’ll feel like talking to a very pizza-smart person, not just a bot.

Education

Optimize administrative tasks. Administrative work is a huge part of education, but in most schools, it’s not optimized. Here’s where AI can help:

• Scheduling;
• Classroom and locker layouts;
• Staff allocation;
• Enrollment;
• Routing guidance.

Now let’s take a look at the most widely cited advantages of AI in education, as noted by both administrators and faculty.

Automatic student grading

Checking assignments is a huge part of teachers’ work. And since there is a teacher shortage in the US, it’s good to relieve them from this burden.

AI can free up educators for more interesting tasks. Sure, some assessments will still require teachers’ attention. But AI can find these situations, too.

Now, AI has a downside – students use it for exams and homework. Thankfully, there are AI content detectors. Of course, these are AI-based too.

Course analytics

Most curriculum publishers don’t have a full picture of student engagement. Without AI, publishers rely on very basic tools: they can check grades or ask students if they’ve enjoyed the course.

AI takes this to the next level. With good analytics, EdTech companies understand exactly which parts of the course to improve. For example, they’ll see when the students pause or skip videos.

For a certification preparation platform, a custom AI coach analyzes user interaction data to identify engagement patterns and knowledge gaps.

The system allows for:

• Tracking content engagement at a micro-interaction level
• Identifying drop-off points and skipped sections
• Detecting areas where learners struggle
• Generating data-driven recommendations for course improvements

AI-driven analytics transforms curriculum optimization from reactive adjustments into evidence-based iteration.

AI assistants

AI makes private tutoring accessible to any world-class dentist who can have a tutor available 24/7, at a fraction of the cost.

But it’s an unexpected advantage, too. With AI assistants, no student will hold back their questions because it may seem basic or foolish. Students can ask anything without judgment or peer pressure.

Discover how GPT-5o redefines deep research with app integrations, live progress tracking, on-the-fly follow-ups, and immersive full-screen reports.

Content personalization

AI doesn’t just personalize. It hyper-personalizes. That means adapting content, timing, channels, and even tones of communication based on individual behavior and preferences. Whether it’s product recommendations or training paths, AI helps deliver the right message to the right person.

For example, an AI-driven highlights generator can automatically identify and extract the most relevant content segments for each user. Instead of presenting identical materials to everyone, the system tailors what users see based on engagement data and contextual signals.

The solution is capable of:

• Detecting high-value moments within long-form content
• Generating personalized highlights automatically
• Adapting content delivery based on user behavior
• Increasing relevance without manual curation

This approach ensures that users receive content aligned with their interests and learning needs – automatically and at scale.

We also explored what adaptive learning really means and how it works in our dedicated article.

AI is entering every corner of education – and corporate learning is one of the fastest-growing areas.

A recent 2026 AI in Learning & Development report found that ~87% of L&D teams are already using AI, and only about 2% have no plans to adopt it, with many moving beyond pilots into defined workflows.

The majority of L&D teams now use generative AI for key tasks such as:

• Creating and updating training content: AI generates and refines learning materials at scale, helping organizations replace lengthy manual content creation cycles with rapid, adaptive course development.
• Providing on-demand assistance to employees: AI tools embedded in workflow systems deliver real-time, personalized support that reinforces learning in the flow of work.
• Mapping skill gaps and recommending personalized learning paths: AI analyzes performance data to pinpoint development needs and suggest tailored upskilling plans.

Why is AI catching on so quickly in corporate training? Because business environments evolve faster than traditional training formats can adapt.

Static materials and periodic workshops rarely reflect real-time performance data or emerging skill demands. AI enables continuous learning, contextual support, and measurable development strategies.

Here are three practical applications supported by real implementation experience:

1. Data-driven learning insights and adaptive content planning

AI helps organizations move from intuition-based training decisions to measurable, data-backed planning.

In a project for a state education agency, we built staff insights dashboards that consolidated performance and operational data into a unified analytics environment. Instead of relying on fragmented reports, leadership gained structured visibility into workforce performance.

The solution enabled:

• Centralized performance and engagement tracking
• Identification of skill and performance gaps
• Trend analysis across teams and roles
• Data-backed prioritization of training initiatives

This approach allows learning programs to evolve based on actual performance signals, not assumptions.

2. AI co-pilots for real-time performance support

AI can embed guidance directly into daily workflows, reducing the need for separate training sessions.

For a legal services organization, we implemented an AI co-pilot integrated into internal systems to support professionals in real time. Rather than replacing expertise, the assistant augmented it by providing contextual, on-demand support.

The system was capable of:

• Retrieving relevant documents and precedents
• Generating structured summaries and drafts
• Providing contextual recommendations within case workflows
• Reducing time spent on repetitive knowledge retrieval

This model demonstrates how AI shifts training from event-based sessions to continuous, in-the-flow learning.

3. AI-powered skills mapping and personalized upskilling

AI enables structured, scalable workforce development aligned with business strategy.

In a talent development initiative, we developed an AI-driven system that analyzed employee competencies, role requirements, and performance data to generate tailored learning pathways.

The solution supported:

• Automated skill gap identification
• Personalized training recommendations
• Career path alignment with business needs
• Data-backed internal mobility planning

By linking skills data with strategic workforce goals, organizations can move from reactive reskilling to proactive capability building.

Companies are learning that training doesn’t have to be reactive. With AI, it becomes smarter, faster, and more aligned with business goals.

Explore the full article to learn:

• What businesses think about AI in L&D;
• What’s holding some companies back;
• What 5 trends are shaping effective and scalable corporate training.

Healthcare

AI adoption is accelerating across clinical and operational domains. According to industry data, 22% of healthcare organizations have implemented domain-specific AI tools in 2025, a 7x increase over 2024 and a 10x increase over 2023. Health systems (large organizations that operate multiple hospitals and clinics under unified management) account for a significant share of these implementations due to their scale and available resources.

The global AI in healthcare market was valued at approximately $36.7 billion in 2025 and is projected to expand to over $500 billion by 2033.

Let’s briefly overview the most widely used applications.

Patient engagement

AI-powered chatbots such as Ada and similar virtual assistants help patients evaluate symptoms and decide whether to seek care. Many integrate with wearables to analyze:

• Activity levels
• Heart rate trends
• Sleep patterns
• Other health indicators

These tools do not diagnose or prescribe treatment. Instead, they support triage, improve health literacy, reduce unnecessary visits, and provide structured pre-consultation data for clinicians.

Surgeries

People are not as stable as robots. We breathe, and we have a heart rate. Sometimes these facts alone lead to a disaster. In surgery, one slip-up can cost a life.

Robotic systems like da Vinci enhance surgical precision by:

• Filtering out natural hand tremors
• Translating movements into micro-actions
• Providing high-definition 3D visualization
• Enabling minimally invasive techniques

Surgeons remain fully in control, but robotic assistance improves stability and reduces complication risks.

Smart health monitoring

AI-powered wearables and connected sensors continuously track vital signs such as heart rate, blood pressure, oxygen saturation, and glucose levels.

Algorithms analyze trends over time and detect anomalies early. A proactive intervention allows for shifting care from reactive treatment to preventive monitoring.

Diagnosis and treatment

AI models assist in analyzing medical images, including X-rays, CT scans, MRIs, and pathology slides. Trained on large datasets, they can detect subtle patterns that may be difficult to identify visually.

In oncology, radiology, and dermatology, AI supports faster case prioritization and more consistent diagnostic accuracy, with clinicians making final decisions.

Administrative applications

AI automates administrative tasks. This minimizes paperwork and secures patient data. Here are some things that AI automates:

• • Appointment scheduling;
  • Electronic health record (EHR) management;
  • Insurance claim processing.

Drug discovery

Some say that we are on the brink of a major breakthrough in molecule design.

In 2025, the investigational drug Rentosertib became one of the first AI-generated small-molecule candidates to reach mid-stage clinical trials, a milestone that underscores AI’s emerging capability in end-to-end design.

However, some other opinions are more skeptical since AI did not yet overcome fundamental translational challenges such as predicting human outcomes reliably.

Recent advancements from Google DeepMind, including the continued expansion of the AlphaFold system and its integration into large-scale biological research platforms, are accelerating structural biology and drug discovery.

The newer generation models go beyond static protein structures, modeling complex interactions between proteins, nucleic acids, and small molecules. They help improve target validation and early-stage therapeutic design.

AI tools on multimodal datasets could enhance disease understanding and clustering, as well as patient population clustering.

This will be revolutionary for multiple standards of care, with particular impact in the cancer, neurological, and rare disease spaces.

AI is steadily becoming part of everyday veterinary practice, helping clinics automate routine tasks, support diagnostics, and deliver more proactive, data-driven care for animals.

Here are the key areas where AI is making an impact:

Imaging & administrative efficiency

AI speeds up X-ray and ultrasound analysis, spotting abnormalities with fewer errors. It also automates scheduling, billing, and client communication, freeing up staff for patient care.

Learn how EHR systems boost administrative efficiency.

Voice-to-text for faster records

AI transcribes vets’ spoken notes into digital records, cutting paperwork time and improving accuracy even during busy shifts.

Smarter diagnosis & early detection

AI cross-references symptoms, lab results, and past cases to support vets in detecting diseases sooner. It’s a reliable second opinion, not a replacement.

Business

Improve customer experience

A chatbot is probably the first thing that comes to mind when thinking of customer experience. And that’s true, they allow 24/7 access to support which can be vital.

But there’s also behavior analysis. AI can analyze customer interactions across many channels. You can predict churn early on and offer clients personalized offers. You can also identify customer feedback and pain points.

This helps businesses to understand customer needs better and offer tailored recommendations. So not only do customers get happier, but you also get to improve your sales.

Solve uncodable tasks

There are some tasks that traditional programming won’t handle, either because it’s too expensive or simply not possible.

The most obvious examples are natural language processing or image recognition. Both are applicable for AI only.

Other tasks are possible even with regular programming, but it wouldn’t be optimal. Think of dynamic pricing, fraud detection, or inventory management. All of them can be solved the old-school way. But in all cases, you get better results with AI, and the development can even end up cheaper.

Automate tedious work

Who likes to fill out Excel spreadsheets? Many of our daily tasks are plain and boring. AI can take care of that, letting you focus on challenging work instead. It’s a win-win for both the business and the employee because some things are better left for the computer.

This process is called RPA or Robotic Process Automation. RPA automates repetitive tasks traditionally done by people. Robots or bots mimic humans in digital systems to handle monotonous tasks like data entry, form processing, and report generation.

Today, many businesses are going a step further by combining RPA with AI – not just to automate tasks, but to analyze data, make decisions, and adapt processes as conditions change. See 7 ways how companies are applying these technologies in real business environments in this guide.

We’ve already seen how AI is reshaping industries. But its impact doesn’t stop there. Beyond grand strategies and digital transformation plans, AI is steadily changing how teams work every day.
Let’s explore how AI tools and techniques are helping professionals solve real tasks, automate repetitive work, and focus on what matters.

AI in software development

AI isn’t writing full applications (yet), but it’s becoming a reliable sidekick for developers. It helps speed up code generation, troubleshoot bugs, and even explain unfamiliar codebases.

ChatGPT is among the most widely adopted tools in this space. It assists both junior and senior developers alike with tasks ranging from writing snippets and documentation to automating test cases.

Is ChatGPT becoming the developer’s new best friend? Read the special piece to learn more.

Alongside ChatGPT, tools like DeepSeek are entering the spotlight. DeepSeek made waves for its promising results and surprisingly strong performance – especially considering its recent release. We broke down what makes it different, how it performs, and what to watch out for.

Discover more about DeepSeek and its potential.

AI in cybersecurity

AI is helping security teams detect and respond to threats faster than ever. It scans large volumes of logs, flags anomalies, and even predicts potential vulnerabilities based on behavior patterns.
From phishing detection to real-time monitoring, it’s become a vital tool for overburdened cybersecurity teams. We explored the specific use cases and tools that make it effective – and where human oversight is still needed.

Read our full breakdown here.

AI and AIOps

Modern IT environments are too complex to manage manually. This is where AIOps (Artificial Intelligence for IT Operations) is becoming more popular. It helps teams prevent incidents, reduce downtime, and improve system reliability by analyzing metrics, logs, and traces in real time.
The technology is already used in large-scale operations to cut through noise and highlight what really needs attention.

Curious how it works and when it makes sense to adopt? Here’s our explainer.

AI for document processing

Another powerful (and often overlooked) application of AI is in document automation. AI can extract key information from invoices, contracts, and forms, saving hours of manual work – especially in industries like healthcare, logistics, and finance.

It doesn’t just scan PDFs – it understands content, classifies documents, and fills data into the right systems. This improves accuracy and helps employees focus on value-added tasks.

How to choose the AI consulting company

Company or person? We recommend going for an AI company.

Appeal to the company or hire an AI consultant? We recommend going for an AI company.

An artificial intelligence consultant might be great at training AI models, but terrible at fitting the model into your business needs. You may get a technically sound AI that doesn’t bring any actual value. That’s why even the most experienced AI developers can be poor consultants. With a company, you hire both technical and business people at once.

Also, when you hire a company, you get the collective experience of the entire organization, where people have collaborated on different types of projects. No matter how much experience a single consultant will have, a good AI company will always have more.

How to develop an AI PoC

A PoC, or Proof of Concept, is a demonstration to show that a concept or idea is feasible. It’s a way to test whether a proposed solution will work in real-world conditions before investing more time and resources into full development.

You develop a PoC the same way you build an AI model. Here’s how:

1. Define the objective. Find out exactly how AI can help your company. To do that, clearly state the problem you want to solve and identify the key metrics for success.
2. Gather and prepare data. Most likely, your company already has a ton of data. It’s time to organize it. Clean, preprocess, and annotate the data to improve its quality.
3. Select your AI approach. Choose the right algorithms for the problem. Is it machine learning, deep learning, natural language processing or something else? Decide on the tools and frameworks. It could be TensorFlow, PyTorch, Scikit-learn, etc.
4. Build the model. Develop a prototype of the AI model. Train the model using the prepared data, and fine-tune hyperparameters.
5. Test and evaluate. Feed the model with real-world data and see if the predictions are correct.
6. Iterate and improve. The first iterations are far from accurate. Refine the model based on feedback and your test results. Train the model and test it until you’re happy with the results. But remember your starting goal and don’t drag the iteration process into infinity. After all, it’s only PoC.
7. Gather feedback. Discuss the model with the developers and your stakeholders. See if it makes sense to implement a full-scale model. If so, then what are the potential challenges and next steps?
8. Plan for scaling. Finally, get a plan for full-scale implementation. Consider your resources, infrastructure, and any additional data that’s required for scaling up.

Top AI and data science companies

Let me be clear: we are an AI and data science development company. A very good one, actually.
But many other companies are great at working with AI, too. It’s not about finding the best AI company. It’s about finding the best AI company for you.

Here are the main criteria to look out for:

• Location. Check if the vendor matches your working hours. Even if you hire an offshore company, they typically still provide some overlapping hours for communication.
• Price. Pricing is important, but prioritize the value that your AI vendor brings. You get what you pay for. Cheap companies have fewer experts, less experience, and they focus on smaller projects. Sometimes (not always, though), cheaper companies have a poor working culture: there’s a higher chance of missed deadlines or just less overall value.
• Type of data science services. Data science is vast. From a small business sales forecast to e-commerce customer behavior analysis. Small companies usually focus on smaller projects.

That’s enough about us – explore our curated list of the top 10 AI consulting companies to see who else is shaping the field.

AI compliance

There isn’t a single global law for AI yet, but rules are emerging that businesses must pay attention to.

The European Union’s Artificial Intelligence Act passed in 2024 and will be phased in through 2025-2027. It groups AI systems into risk levels (from permitted to high-risk) and sets requirements accordingly. Companies that don’t comply can face fines up to €35 million or 7 % of global turnover, whichever is higher.

Data privacy is also key. The General Data Protection Regulation (GDPR) still applies to AI systems that process personal information. Organizations have to determine whether they are data controllers or data processors, which affects legal responsibilities.

In the US, there’s no federal AI law yet. Some bills have been proposed, but most AI-related requirements come from state or sector rules, such as New York’s Responsible AI Safety and Education Act.

As a result, companies building or using AI need to track both local and international laws, especially around safety and personal data.

AI regulation is hard. But we cover the current state of it in our guide – learn everything you need to know.

The future of AI

Scientific discovery

Our society faces many challenges, and AI can help solve them.

• To tackle climate change, AI will create sustainable agriculture. Agrotech AI models use satellite images to analyze soil, weather, and crop conditions. They analyze all the data to increase yields and reduce resource use.
• In astrophysics, AI analyzes telescope images to find new exoplanets or gravitational waves.
• In neuroscience, researchers from Google and Harvard have just created a very detailed brain map. Though it’s not a map of the entire brain yet, with this knowledge scientists can already create advanced brain-machine interfaces
• In physics, AI is used to research nuclear fusion. There’s hope that we’ll be able to fuse atoms – just like the Sun does. If that happens, we’ll have a virtually endless source of energy.

And this list goes on. We might be getting into a new renaissance thanks to AI – check out all the recent statistics in a dedicated material.

Small language models instead of LLM

One of the first general-purpose computers, ENIAC (1945), filled an entire room and ran at roughly 100 kHz. A modern smartphone chip runs at around 3-3.5 GHz – tens of thousands of times faster in clock speed alone.

A similar pattern appears in AI. While earlier large models were estimated in the hundreds of billions of parameters, today compact language models with 1-7 billion parameters can handle reasoning, summarization, and coding tasks locally — and are optimized to run directly on consumer devices.

Today, we still have giant supercomputers, but they are not for daily use. Supercomputers design molecules, research nuclear fusion, and power LLMs. Soon, we will use smaller models for simple tasks and leave LLM for the most advanced research.

Multimodal AI

We’ve already discussed that all current AI is narrow. Any specific model can only perform so many tasks. A model that can perform complicated customer behavior analysis can’t have a small talk with your customer. And vice versa: an AI chatbot can chatter with your clients, but can’t analyze their behavior.

But AI gets multimodal. GPT 4o combines audio, text, and vision. It can talk, generate images and 3D models, and much more. Soon, multimodal AI might become the new standard.

Now you’re ready to hire a dedicated AI consultant. You should know all the basics, so there should be no problem talking the same tech-savvy language.

Bonus. Looking for a consulting company? Here we are. We consult companies on getting the right solution, and we build AI models from scratch. Reach out for a free consultation.