The AI race has been on for a while. Yet, its influence is still here, with many companies, despite their size and domain, looking to leverage this technology to gain a competitive edge. Chances are you are one of those who want to incorporate AI into business processes – whether for automated workflows or enhanced decision-making. Is that so? If your company is struggling to stay competitive without the power of AI, keep reading.
As the key person leading transformational change in your company, it makes sense for you to seek or develop AI solutions for business. According to a recent Gartner survey, 79% of corporate strategists see artificial intelligence (AI) as critical to their success over the next two years, with Generative AI now being the most frequently deployed AI solution in organizations. However, the journey to AI adoption comes with its challenges, requiring expertise from data scientists, machine learning teams, and developers.
As the technical lead and co-founder of Uptech, I’ll share our team’s experience in implementing artificial intelligence in various apps. Over the last years, we have built several apps incorporating powerful AI capabilities, including Dyvo.ai, Angler,ai, Hamlet, and an AI-powered virtual assistant for Plai.
In this article, I’d like to share some insights and knowledge my team and I gained while working on those projects. I’ll explain how to implement AI in business, what steps are required, and what the potential benefits and pitfalls are, and I will share tips that will help you make your AI journey smoother.
What the Numbers Say About AI Implementation in Business
AI implementation in business settings is about a company-wide effort to pinpoint roles and tasks where it can bring the most value. Although the technology is relatively new, the power it brings is evident. We already see the unique ability of AI products to change the way businesses operate, make decisions, and interact with customers.
Let’s take a look at some statistics that may persuade you to integrate AI.
Deloitte's survey shows that companies well-versed in digital technology see a 4.3% return on investment from their AI projects just over a year after they start.
PwC highlights that the best AI leaders start their projects with a strong base. With a comprehensive approach to developing and integrating AI into business, they focus on three key areas: the transformation of business practices, legacy systems modernization, and making better decisions.
A Forbes Advisor survey reveals that 64% of businesses believe artificial intelligence will boost their productivity, showing strong confidence in AI's ability to change the way companies operate. Additionally, the survey indicates that the same percentage of business leaders think AI can improve customer relationships the most.
According to an IBM report, one in four companies are adopting AI due to labor or skills shortages, using it to improve operations and fill gaps in their workforce.
But that’s not all. As the key focus area within this tech domain, generative AI deserves special attention. That’s why I have selected more statistics below.
Shortlist surveyed 500 employers, and 33% agreed that ChatGPT would boost productivity by up to 74%. On that note, the tech industry might witness the most considerable changes, with 43% of millennials worrying about losing their jobs to AI.
Job loss worries aside, businesses continue to explore generative AI opportunities and adopt generative AI solutions at a rapid pace and expand their investments in it. According to a PitchBook report, venture capitalists injected $4.5 billion worth of investments into generative AI deals in 2022. The figure far dwarfs the $408 million invested in 2018. Likewise, Goldman Sachs is optimistic about the economic implications of generative AI, forecasting a global GDP growth of $7 trillion.
7 Steps to Incorporate AI into Business
Integrating AI into business can greatly benefit you, but where do you start? And, more importantly, how do you proceed then?
Here’s a step-by-step guide to introducing AI capabilities in your company:
- Define your requirements and goals
- Evaluate your AI readiness
- Choose the right AI tech stack
- Prepare training data
- Train and fine-tune the model
- Develop and integrate the AI solution
- Monitor and refine the model
I will guide you through each step of the process in more detail now.
Step 1. Define your requirements and goals
There are quite a few ways for your business to use AI – from customer support automation to improved predictive analytics to detecting fraudulent activities in transactions. As each case is different, there will be different AI technology stacks, different approaches, and, therefore, resources needed. As such, you must identify areas of your business where AI proves most impactful early on.
For example, if your business is heavily marketing-oriented, you may need an AI solution that is based on Large Language Models and uses NLP (natural language processing) to write ad copies or any other texts for you.
On the other hand, if your business operations rely on structured data (e.g., Excel spreadsheets) and you want to build a predictive model, you probably won’t need a neural network for that. Such models are commonly built with simpler machine-learning algorithms, like decision trees or linear regression.
A completely different case would be creating an AI image analysis tool for the healthcare industry. Images belong to the unstructured data category, and they require a different kind of AI model – mainly convolutional neural networks (CNNs) and the variations of this model. Besides, the training process here requires a lot of imaging data that must be preprocessed as it contains personal information, which adds to the complexity, durability, and costs of the project. Not to mention, there is a necessity for software compliance with strict regulations like HIPAA and GDPR.
So, as you can see, the goals and requirements, based on how well they are defined, can either make or break your AI implementation initiative.
Step 2. Evaluate your AI readiness
Another important step is to assess your organization's ability to adopt AI technologies and use them to achieve business goals.
Remember the statistics I provided earlier? Businesses that are digitally mature and AI-ready can expect a higher percentage of ROI in the first year after the start of the project.
So, how do you assess the tools and resources needed to carry out your AI plan?
AI development expertise
The first thing to do is to check if you have AI specialists and experts who understand AI implementation from both a technical and business perspective. If not, do you have a budget to outsource to AI development companies or buy and set up a software-as-a-service (SaaS) solution? Even with the latter, you'll likely need to hire AI professionals to tailor the software to your needs.
Costs of development and maintenance
You might want to develop custom AI software or choose an available SaaS solution, each with its pros and cons, like varying AI implementation times and customization limits.
The total cost of owning AI systems, whether custom or SaaS, includes fees for the vendor, ongoing maintenance, and expenses related to setting up and managing a cloud infrastructure. This should be taken into consideration as early as possible.
Following our previous examples, the type and complexity of the AI project will also influence the costs. Building a predictive model that uses classic ML techniques will be cheaper than a healthcare AI image analysis tool.
Data quality and availability
AI algorithms depend heavily on the quality of data they receive. Have you heard the phrase, “Garbage in, garbage out.”? If you feed your AI model poor-quality data, you will get respective results.
Up to 90% of the data stored in your company's systems may be unstructured – like images, videos, and PDFs – which is challenging to use without significant preprocessing. Even structured data stored in tables must undergo organizing and preparing jobs for training algorithms properly.
Computing and storage resources
Services from cloud providers like Microsoft Azure, Amazon Web Services, and Google Cloud are essential for training, deploying, and managing AI models. Your data will also be stored in the cloud – whether in data warehouses, data lakes, or a combination known as data lakehouses. Proper configuration of your cloud setup is critical to avoid costs that outweigh the benefits.
Employee training
Even with skilled AI developers, you will still need to train your staff on the new technology. This ensures they can effectively use AI now and adapt as you move towards broader AI adoption within the company.
Step 3. Choose the right AI tech stack
Artificial intelligence is a broad term that covers a wide array of technologies. Implementing AI in business means knowing exactly what technology would work best in your case. I would like to cover the 4 most significant AI subfields, namely machine learning, deep learning, NLP, and computer vision, with respective models and technologies involved in each.
Machine learning
Machine learning is the subset of AI that allows computers to learn from historical data and make decisions and predictions without much human intervention. Common machine learning methods include decision trees, which are great for making straightforward predictions and classifications based on past data.
Common algorithms include:
- Decision Trees
- Support Vector Machines (SVM)
- k-nearest Neighbors (k-NN)
- Linear Regression
Frameworks and libraries: Scikit-learn framework (for beginners); TensorFlow and PyTorch (for more complex models that require deep learning techniques).
Languages: Python, R (for statistical analysis).
Application examples:
- Fraud detection systems. Banks use machine learning models, such as decision trees and support vector machines, to detect unusual patterns and prevent fraudulent transactions.
- Customer sentiment analysis tools. Retail and e-commerce businesses apply machine learning to social media and review data to determine customer sentiment for more targeted marketing strategies.
- Demand forecasting software. Companies in supply chain management may utilize machine learning algorithms like linear regression to predict future product demand based on historical sales data.
Deep learning
Deep learning is a subset of machine learning that uses human-brain-inspired neural networks to recognize patterns and make connections in large data sets. Used for complex problem-solving tasks, deep learning requires more data than machine learning to be effective.
Common models include:
- Convolutional Neural Networks (CNNs) for image tasks;
- Recurrent Neural Networks (RNNs) for sequential data like time series or text;
- Transformers for handling various tasks like natural language understanding and generation.
Frameworks and libraries: TensorFlow and PyTorch.
Python dominates due to the extensive support for deep learning libraries.
Application examples:
- Speech recognition systems. Deep learning models like RNNs are used to develop virtual assistants and mobile voice-to-text applications that convert speech into text and vice versa.
- Text generation software. Transformers are now widely used to create AI software that can generate text for a wide range of purposes – whether these are articles for a company’s blog or text-based customer assistants.
- Medical diagnosis tools. Deep learning frameworks support the analysis of medical imagery, which allows doctors to diagnose diseases from scans with higher accuracy.
Natural language processing
Natural language processing (NLP) is the aspect of AI that allows computers to understand and process human language.
Common models include:
- BERT (Bidirectional Encoder Representations from Transformers) for understanding the context of words in text;
- GPT (Generative Pre-trained Transformer) for generating human-like text;
- LSTM (Long Short-Term Memory) networks for tasks that require learning from sequences of data.
Frameworks and libraries: Hugging Face’s Transformers library, NLTK, and SpaCy are popular choices.
Languages: Mainly Python.
Application examples:
- Chatbots and virtual assistants. With the help of models like GPT and BERT, businesses can deploy advanced chatbots that can understand and respond to human queries with a high degree of relevance and accuracy.
- Translation services. NLP is extensively used in real-time language translation tools, making cross-language communications possible.
- Content recommendation systems. Various platforms, including live streaming services, use NLP to analyze user preferences and textual descriptions to recommend personalized content to users.
Computer vision
Computer vision helps computers interpret and understand visual information from the world, such as images and videos. It’s commonly used for image classification, object detection, and image generation.
Common models include:
- YOLO (You Only Look Once) for real-time object detection;
- ResNet (Residual Networks) for deep image classification;
- GANs (Generative Adversarial Networks) for generating new images.
Frameworks and libraries: OpenCV (for real-time computer vision applications), TensorFlow, and PyTorch.
Languages: Python, C++ (used in performance-critical applications).
Each technique and each model has its respective strengths and disadvantages, as well as the skills and teams required. So, picking the right one is crucial.
Application examples:
- Advanced analytics systems. Computer vision technologies, such as YOLO, analyze video from store cameras to track customer movements and interactions, which enables better layout planning.
- Quality control systems. In manufacturing, computer vision models like CNNs may be used to inspect products on assembly lines for defects for higher standards of quality.
- Facial recognition security software. GANs and other neural networks are employed in security systems to enhance identity verification processes, which are crucial for access control and law enforcement.
Step 4. Prepare training data
Most AI implementations in business require a lot of data for training purposes. Besides quantity, training data quality is also a must. Where do you get it?
There are 2 possible options:
- You gather your own data from diverse sources, such as your CRM system or any other data-accumulating tool used within your business.
- You opt for external data sources such as public datasets on Kaggle and similar resources under the condition that those datasets fit your product goals.
Once you have enough data in place, you’ll need to do some preprocessing jobs so that the AI model provides relevant results.
For this, you perform:
- Cleaning – delete corrupted information, fill in the missing values, etc.
- Data labeling – assigning each piece of data a label or tag to show the model the expected answer it should provide (e.g., in medical imaging, radiologists annotate X-ray scans by marking organs and any anomalies, such as tumors, to teach the AI model to recognize and interpret similar images on its own.)
- Standardization – data points are converted to one common format (e.g., your datasets may include “Texas” and “TX” values; it’s important to use only one variant)
Of course, these are just a few things you do to ensure the data fits for training the model.
Be ready for the data collection and preparation phase, which may take up to 80 percent of the whole AI implementation process.
Step 5. Train and fine-tune the model
The next step when you integrate AI into the business will be training the model with the prepared datasets. The model would learn in self-supervised mode and adjust its hyperparameters accordingly. The process goes on iteratively until the results converge at the optimal point. It’s important to realize that training an AI model, especially if we talk about deep learning neural networks, is a resource-intensive process. You will require powerful computers as well as enough storage space to train the models. That's why many companies avoid training a new model from scratch and choose to fine-tune the existing one.
For example, you can take one of the available large language models that have already been trained on enormous volumes of text data (e.g., OpenAI GPT-3 was trained on about 45TB of text data from different datasets) and use transfer learning technique to fine-tune its parameters for your business needs.
Another example is that in the healthcare industry, you might fine-tune a pre-trained Convolutional Neural Network (CNN) originally designed for general image recognition to diagnose skin cancer from dermoscopic images. During this process, you adapt the final layers of the network to focus on specific types of skin lesions and adjust the learning rate to refine detection accuracy.
Remember that mistakes could happen in training, or you might need a different training algorithm to produce a better-fitting result. This will incur additional costs and time as you retrain the model. Also, there are times when you’ll need to fine-tune the model to align its performance to your business goals further.
Step 6. Develop and integrate the AI solution
Once trained or fine-tuned, your next step is integrating the AI model into your business workflow. This involves developing a new application or adding new AI features to an existing one. Here, the “start small” rule works best.
It's advisable to initially develop a minimal viable product (MVP) or a proof of concept (PoC) to test the feasibility of the AI solution among a select group of users. This step allows you to gauge how the AI performs in practical settings and make necessary adjustments before full-scale implementation.
However, transitioning from a prototype to a fully operational AI system can be challenging. According to Gartner, only 53% of AI projects progress from prototypes to production. A common stumbling block is the inability to replicate the success of PoCs in controlled environments when AI systems are deployed in real-world scenarios, where they must handle data from multiple sources and improve various processes.
To address this challenge, ensure that your AI application is robust enough to handle diverse data inputs and capable of adapting to complex operational workflows. Regular reviews and iterations based on real-world feedback will help you refine the AI system.
Also, a reasonable timeline for completing an AI PoC should not exceed three months. If the expected results are not achieved within this timeframe, you might want to terminate the trial and explore other potential use cases.
At Uptech, we can deliver a PoC within the 3-months timeline so that you get a quick and efficient way to test and refine your AI solutions before committing to a full-scale roll-out.
Step 7. Monitor and refine the model
AI models might suffer from performance issues when deployed. For example, your AI solution fails to respond accurately to new data despite showing ideal results during training. Or it may demonstrate bias or hallucination. Such issues call for further fine-tuning to adjust its weights and biases even if the AI implementation in business has already happened.
To solve them, it's important to establish a system for regularly assessing the model's performance against real-world data. Continuous monitoring will allow you to track the model’s accuracy, fairness, and reliability and identify any deviation from expected behavior.
If issues like decreased accuracy or unintended biases are detected, you should retrain the model using updated datasets or adjust its algorithmic parameters. Sometimes, introducing additional data that the model hasn't seen before can help it learn and adapt better.
Another important aspect here is to keep your AI system transparent and explainable. This means being able to trace how decisions are made within the model. Transparency implementation helps identify the causes of errors or biases and builds trust among users.
Also, make sure you have feedback loops that allow end-users to report anomalies or errors in AI predictions. These insights can be invaluable for ongoing refinement processes, helping to enhance the model continually as it interacts with new data and different scenarios.
Given all of the above mentioned, you may think that AI is a magical solution. Well, it’s not. It can't automatically adjust to fit into your business operations or solve every problem on its own. The real strength of AI depends on the people using it – those who take the time to understand what it can do, see where it can be applied, and carefully integrate it to address specific issues and reach clear objectives.
When you incorporate AI into your business, you set a clear plan for how to use it, train your team to handle AI tools properly, and constantly check how it's affecting your business operations.
Why Your Business Needs AI Implementation
As a co-founder of Uptech, I know that many businesses face cost pressure, productivity concerns, and an influx of data in today’s digital environment. However, I also know that AI solutions offer timely benefits to help companies navigate challenges in competitive markets. I share several examples below.
Increased process efficiency
AI solutions, especially those relying on deep learning models, are capable of processing large numbers of information in real-time. More importantly, they can be trained with business-specific datasets that allow them to replace humans in specific tasks. Having smart AI assistants in place helps businesses refocus their workforce on tasks that require creative human input.
For example, marketers can use AI tools to outline SEO strategies instead of creating them from scratch. Healthcare providers can utilize conversational AI solutions to improve patient care and communication.
Automated business processes
Many companies still rely on a manual workforce to coordinate business operations. This affects their agility and ability to respond promptly to changing market dynamics. Already, AI providers have repurposed AI models into automation-capable solutions, such as InstructGPT, to perform follow-ups of the initial prompt. But you can build your own AI helper that will be completely tailored to your business and automate those processes you suffer from the most.
For example, you can ask the AI software to “build a real estate business,” and it will respond with a strategy, design a website, or write scripts for phone calls to prospective clients.
Improved decision-making
Businesses sit on top of voluminous data, which might prove helpful in supporting decision-making if it is correctly harnessed. Conventional business intelligence software cannot ingest and analyze textual data in ways that, say, deep learning models can. That’s why many organizations opt for AI to detect specific patterns from the data they have (e.g., sales data) to make predictions and make more informed decisions that serve as a supporting foundation for the company’s next move.
Personalized experience
AI allows businesses to shift away from the one-size-fits-all customer funnel into one that caters to individual preferences. This way, customers enjoy a highly personalized experience that encourages them to stay loyal to the brand and increase spending. For example, Amazon uses AI to suggest relevant products based on the customer's past transactions, browsing behaviors, and current activities.
Practical AI Applications in Business
So, what can you get with AI technologies? How does it affect your business operations? In this section, I explain the ways AI is transforming different job roles.
Content creation
Content creation involves writing marketing pitches, blog articles, and other textual copies, which might take hours. With the right AI technology in place, marketers can generate the initial copy in seconds and make further edits. For example, by specifying simple instructions, the tool built on the base of a large language model like GPT-3 or BERT can create copies for specific cases.
Design creation
AI is also disrupting the graphic design space. It powers tools that help graphic designers brainstorm fresh ideas and deliver publish-ready photos. Diffusion models and transformers have made it possible to create special tools (e.g., Midjourney and DALL-E 3) that allow anyone to create professional renderings. At Uptech, we have our own AI solution, Dyvo, that enables marketers to generate studio-quality product photos for e-commerce stores.
Video creation
AI has yet to impact video creation in the way it did in image or text creation. There is still a need for substantial editing before the generated videos are fit for production. Works are, however, in the pipeline to elevate the roles of AI in this space. For example, Runway has revealed a powerful AI system capable of generating new realistic videos from text, images, and existing videos.
AI assistants
Having an AI chatbot that works on your data is helpful for many businesses. For example, our project, Plai, is a digital solution that helps HR managers review employee performance. We added a new AI feature that allows managers to create employee development plans with feedback from the generative model, taking into account the results of the review. Managers can also create and refine their goals (OKRs) with AI and suggest if your department's goals are not aligned with the company strategy.
By the way, you can read our guide, which explains how to build an AI assistant in more detail.
Operations automation
Whether in logistics, sales, or inventory management, employees will benefit from the automation capabilities that AI brings. For example, production managers can use AI software to optimize resource allocation, scheduling, and inventory movement to improve manufacturing efficiency.
Customer support
Customer support teams are tasked to provide prompt resolutions, and they’ll benefit from AI-powered agents. When trained with specific products or services, AI models can interact with customers like human personnel do. So, you can use these chatbots to filter and respond to common queries and escalate complex ones to your support team.
Challenges of AI Implementation into Business and Their Solutions
Even with your best efforts, integrating AI into business is fraught with challenges. These are the most significant hurdles and ways to tackle them.
Quality of training data
Training data quality directly influences the model’s output. If you provide questionable datasets, the model’s performance will be impacted.
Solution. Investing in high-quality, diverse datasets always pays off. Also, implement robust data cleaning processes to ensure the data you feed into your model is accurate and representative.
High cost of training models
Some companies underestimate the resources to train a foundational model. This is, of course, if you build your own AI model from scratch. OpenAI spent around $4.6 million to train the GPT model, which it later fine-tuned to produce ChatGPT.
Solution. Instead of training a model from scratch, it’s more cost-effective to fine-tune pre-trained ones for your business needs.
AI errors and inconsistencies
As powerful as they are, AI makes mistakes. You will still find irregularities, such as bias and hallucinations, on models you’ve trained extensively.
Solution. Continuously monitor the model's performance and refine it by retraining with updated datasets or tweaking the algorithms. Implement checks for fairness and unbiased outcomes regularly.
Limited impact on business processes
Not all business processes will notice significant impacts from AI. What’s more, there are sectors and operations that can’t use AI to its full potential due to strict security regulations or other reasons.
Solution: You can integrate AI into the workflow that provides the most significant operational and financial returns. Conduct pilot tests to identify these impactful areas before full-scale implementation.
Data security risks
Building AI solutions involves storing, moving, and processing large volumes of sensitive data, which poses security risks.
Solution. To prevent data risks, use advanced encryption methods and other data protection measures to secure data during storage, processing, and transit. Establish strict data governance policies to safeguard information further.
How Uptech Can Help to Implement AI in Your Business
At Uptech, we not only blog about AI but also build solutions for our clients. Our team taps into years of experience in app development and knowledge of emerging AI technologies to provide all kinds of AI solutions for various business applications.
- We’ve built Dyvo.ai, an AI-powered app allowing users to create unique avatars from selfies. In this project, we trained and fine-tuned the AI model on the cloud to produce unique photos. From here, we expanded Dyvo.ai for business use cases by enabling the app to generate mesmerizing brand-aligned product photos with AI.
- Our team augmented Plai, a digital HR solution, with generative AI. This allows managers to summarize performance reviews and provide feedback to employees automatically.
- Meet Hamlet, an AI-powered text summarizer built with the tech-davenci-003 model. It allows users to create text summaries from PDF files.
- And there’s Angler.ai, which lets marketers align their campaigns to relevant target audiences and social network platforms with AI algorithms.
We took a user-centric approach in every app we built to ensure product-market fit. While our team strives to bring state-of-the-art technologies to businesses, we’re equally concerned about solving real-world problems. As such, we test your idea by preparing a PoC within 3 months before designing an AI solution that your customers find useful.
Listen to our client’s experience with our AI development team.
"Uptech is a great partner for software and web development projects. I was impressed with the talent level for each of the roles, including design, front-end, back-end."
Indy Sheorey, Executive, Angler AI.
Last but not least, yes, AI can and will transform the business landscape in ways never seen before. It offers improved efficiency, automation, and personalization when appropriately integrated. On paper, incorporating AI capabilities does not sound like a big deal. In reality, it’s a lot of work, and not every company is ready for this sort of journey. But we are here to help you figure that out
Talk to our team to learn how you can benefit from implementing AI into your business.
FAQs
How can I measure the ROI of AI implementation?
To measure the ROI of AI, you should divide the net profit (the difference between cost savings and revenue gains from AI solutions) by the total investment made in AI technologies and resources.
What IT infrastructure is required for AI?
AI requires robust computing power, storage solutions, and often specialized hardware like GPUs. Cloud-based solutions are popular for scalable infrastructure needs. However, the requirements may differ based on the type and complexity of the AI project.
An ML predictive model requires fewer resources, such as:
- Infrastructure. Standard servers with moderate CPUs and basic SSD storage.
- Software. Python with Scikit-learn and smaller databases like PostgreSQL.
- Cloud. Scalable services like AWS EC2 for manageable growth.
An advanced computer vision tool will need more resources, namely
- Infrastructure. High-performance GPUs and advanced servers with high RAM.
- Software. TensorFlow or PyTorch for deep learning, OpenCV for image processing.
- Cloud. GPU-enabled instances, like AWS EC2 G4, are suitable for heavy image processing.
What kind of talent do I need for AI implementation?
For AI implementation, you need data scientists, machine learning engineers, and AI specialists who can design, train, and deploy AI models. Instead of hiring all these specialists, you can contact us, and we will build a team for your project.
How can I upskill my existing employees for AI?
You can provide access to online courses, workshops, and seminars focused on AI and machine learning skills.
What AI tools and platforms are available for businesses?
There are numerous AI tools and platforms available – from machine learning libraries like TensorFlow and PyTorch to comprehensive platforms like IBM Watson and Google Cloud AI.
How do I choose the right AI tools for my needs?
Choose AI tools based on your specific business needs, the complexity of tasks, ease of use, and integration capabilities with your existing systems.
Can I use pre-built AI models for my business?
Yes, pre-built AI models can be used for your business, especially for common tasks like image recognition or customer sentiment analysis, and can be fine-tuned for specific needs.
What are the pros and cons of using AI as a service (AIaaS)?
AIaaS offers cost efficiency and ease of use without needing in-house expertise, but it may limit your control over the data and the specificity of the AI solutions.
How much does AI implementation cost?
The cost of AI implementation can vary significantly depending on the project's complexity. For simple projects, costs start around $10,000, while more advanced projects may exceed $200,000. Consulting fees and software costs vary, with consultant rates ranging from $200 to $350 per hour and third-party AI software costs up to $40,000 annually.
If you wonder how to integrate generative AI, a basic integration of a pre-built generative AI model can cost from $10,000 to $50,000. Integrating a custom generative AI solution could range from $50,000 to $500,000 or more, depending on the complexity.
How long does it take to incorporate AI into business processes?
Incorporating AI into business processes typically takes 3 to 12+ months, depending on the complexity of the AI solution and the business requirements involved.