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.

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) is a technology that simulates humans. So, AI systems do what people do: they learn, see, write, solve problems, and make predictions.

But today, all AI is narrow AI. It performs a small number of tasks extremely well, but when it comes to new tasks it’s absolutely clueless. A chess model called Stockfish can beat any grandmaster, but it can’t order a cup of coffee.

The models become wider over time. When ChatGPT was released, it only supported text input – you couldn’t speak with it. With GPT-4, you could upload images. Now, GPT-4o can talk to you with a silky voice that sounds just like Scarlett Johannson. It can interpret your conversations in real time or tutor your kids in math.

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. You still need someone to write prompts or verify the results.

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 out of 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.

1. First, you need a random hypothesis: to analyze attendance equal perfect test scores?
2. 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.
3. Adjust. The model checks how accurate the initial predictions DaVinci the model is inaccurate, the model adjusts its parameters through a process called backpropagation.
4. 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 student grade history or time spent on finishing homework.

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.

Read our machine learning guide to go deeper into AI and machine learning

Your 2024 guide to machine learning. Finally, get it

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.

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.

Learn more about generative AI in our guide. Find out how world’s best educators use AI in real life

Generative AI in education

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.

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 seem basic or foolish. Students can ask anything without judgment or peer pressure.

Take a look at how GPT-4o helps solve a math problem.

Learn how to improve corporate and e-learning education by introducing the right type of AI.

AI guide in elearning

Healthcare

Patient engagement. AI chatbots like Ada are pretty good at patient recommendations. They integrate with smartwatches and other wearable devices to analyze your health in-depth.

But let’s keep in mind that such chatbots do not diagnose differences. They are not responsible for your treatment, unlike real doctors. But they are a great tool, even for the doctor use.

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

That’s why the first surgical robots in the US were approved long ago, in the year 2000. Surgical systems like the DaVinci absorbs natural body movements, saving thousands of lives. Yet they leave control to surgeons, who still make all the important decisions.

Smart health monitoring. AI-powered wearable devices and sensors continuously track vital signs like heart rate, blood pressure, and glucose levels. This way, they alert users and healthcare providers to any abnormalities for early intervention.

Diagnosis and treatment. AI algorithms analyze medical images, like X-rays and MRIs. Sometimes a doctor doesn’t see a problem, but AI can find even the smallest patterns that indicate cancer.

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, but others are more skeptical.

Still, the recent announcements by DeepMind and AlphaFold pave the way for better understanding of disease processes, predicting protein structures, and developing more targeted therapeutics.

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.

Read our guide to learn how healthcare companies improve their workflow, cut costs, and save lives

How AI can drive innovation in healthcare

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 identify paint points.

This helps businesses to better understand customer needs and offer tailored recommendations. So not only do customers get happier, but also you 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 old-school way. But in all 3 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? Much 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.

Learn how to implement AI in business, step by step. From understanding your requirements to tracking AI performance

7 steps of AI development in business

Note, that just like companies, individual consultants typically juggle several projects at once. When you hire a company, you can get an expert that works on your project full-time.

Consultants or developers? Always make sure that the AI company doesn’t just consult, but also delivers.

AI is a trendy and full of mystery. That’s the perfect combination for easy money. If your AI vendor can consult, but can’t build the actual model, they can be selling you snake oil.

Does industry experience matter? That depends on your project.

Sometimes, industry knowledge is even more important than AI experience. When you are building a simple model with a tool like Google AutoML, it’s more important to understand your industry regulations.

When there’s little specific regulation, industry experience is not as important. Data scientists can talk to your in-house experts and make the best of the data even without much industry exposure.

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:

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.

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.

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.

Build the model. Develop a prototype of the AI model. Train the model using the prepared data, and fine-tune hyperparameters.

Test and evaluate. Feed the model with real-world data and see if the predictions are correct.

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 your are happy with the results. But remember your starting goal and don’t drag the iteration process into infinity. After all, it’s only PoC

Gather feedback. Discuss the model with the developers and your stakeholders. See if it makes sense to implement full scale model. If so, then what are the potthe ential challenges and next steps?

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 others 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 prioritise 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 (though, not always) 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.

We’ve talked enough about us. Read about other data science companies in our list

Top 10 data science consulting companies

AI regulation is hard. But we cover current state of it in our guide. Learn everything you need to know in 2024

Breakdown of AI compliance in 2024

AI & ML conferences

AI conferences are the best way to talk to world class AI experts. Let’s find out which conferences are worth a visit.

World summit AI. That’s the world’s largest AI summit established in 2017. Speakers include experts from Apple, Facebook, NASA, IBM and many more. If you want to learn about cutting edge, go to World summit ÀI

Super AI. Another great conference in Singapore. This one includes 5000 visitors from 1000 companies, most are C-level employees. That’s a perfect place to get an AI deal.

Annual MENA Conversational AI Summit. This is the biggest summit focused in conversational Ai. Here, you can learn everything about chatbots, virtual assistants, contact center automation, voice technology, and conversation design.

Find out more about AI & ML conferences. AI conferences take place around the world, all year long. Find out the most interesting ones in our guide

Top 10 AI/ML Conferences You Must Visit in 2024

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 together – just like 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.

Small language models instead of LLM

A groundbreaking computer from 1945 took up an entire room and operated at 100kHz. An iPhone 15 fits in your palm and operates at 3.5GHz – that’s almost 35000 times more powerful.

The same thing is happening with AI. For example, GPT-4 runs 100 trillion parameters (100 000 000 000 000). But soon, small language models can take over. They will only need a few billion parameters – enough to run on your phone.

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 can the new standard.

AI is the future

Learn more about current AI and ML trends in our article. What’s next?

Now you’re ready for AI consulting. You should know all the basics, so there should be no problem talking to the AI consultants.

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.