You’ll have to spend time at least on creating start-up guides helping new onboards. Again, the more platforms we want to cover, the higher the development budget. If we choose a cloud strategy — when all data syncs to a secure cloud-based AI engine — our spending will become more manageable. However, if AI analysis happens on the device without syncing to the cloud, such data de-identification might be unnecessary. As long as all other HIPAA-related precautions are in place, there’s no need to anonymize PHI.

The solution conducts linguistic analysis of video recordings to detect possible changes in the communication style that could shed light on patients’ well-being and help physicians come up with better treatment plans. Akkio makes AI development and deployment effortless, so you can focus on augmenting decision-making across a wide range of use-cases, instead of needing to build slow and expensive in-house solutions. There are many tools available that make implementing an AI solution much easier.

How Much Does Artificial Intelligence Cost in 2023? AI Development Cost

The cybersecurity tools you implement can make a difference in the financial future of your business. According to the 2023 IBM Cost of a Data Breach report, organizations using security AI and automation incurred fewer data breach costs compared to businesses not using AI-based cybersecurity tools. The right AI software should allow easy deployment due to its flexible architecture. Using this software, you should be able to uncover the power of data in your business with advanced predictive modeling applications and to make use of data flow graphs for building the data models. While most companies see the potential of AI-powered solutions for business, only 23% have incorporated the technology in a meaningful way, according to BCG.

ai implementation cost

Considering the variables involved in the development process, most companies often hesitate to move forward with their AI idea. In the United States, the average base salary for a data scientist is over $102,000 (according to Indeed). And a software developer can expect to earn a salary of $110,140 (according to US News). After all, skills like data science, natural language processing, computer vision, and deep learning are in high-demand, with a limited supply of talent. In the world of traditional IT, once a complex system is built and deployed, things calm down.

This will be the first of a two part series on data ontology and its implementation in a knowledge graph.

Developers expect the neural network to reach and accuracy of an expert not in a lifetime but in some viable time. Depending on the Machine Learning project it can be an hour, a week or a month, machine learning implementation in business but not a dozen years for sure. Companies can minimize this risk by obtaining help from outside consultants, and AI vendors that have expertise in specific company verticals and business areas.

Its best examples are Netflix’s recommendations, email spam filters, and smart assistants. We’re not there yet, but it’s going to arrive a lot sooner than you think. In the meantime, consider a service provider or AI software that will deliver what you are looking for. A POC test takes time and therefore money, with market leaders charging up to $1M for just the discovery aspect.


Improving computer power, connectivity, and algorithms have made it affordable to all organizations in the last decade. In healthcare, custom AI solutions can ensure that specific market problems are addressed, and firms only pay for what they need instead of expensive off-the-shelf products that are not fit for purpose. This article discusses how much AI costs in healthcare and why companies can benefit from a bespoke solution. Before COVID-19, the value of health artificial intelligence (AI) was predicted to reach $6.6 billion in 2021. Suffice to say, the pandemic has only served to accelerate the industry’s development, with the critical need for rapid solutions. AI has become a pivotal tool for creating predictive models that track the virus and understand the likelihood of spreading.

If you’re thinking about incorporating AI into your business but don’t know where to start, look no further than Akkio. In recent years, Artificial Intelligence has become one of the hottest topics in the business world. Everyone wants to get in on the action but very few know where to start or how to implement AI without burning a hole in their pocket.

Key factors to consider before investing in AI

Companies are actively exploring, experimenting and deploying AI-infused solutions in their business processes. It takes a lot of effort and processing to make data valuable for an AI deployment. For starters, any AI application will need training data, so that it has a starting point from which it can learn – if this isn’t in place from the beginning, it can be time-consuming and expensive to catch up. This data also needs to be managed, processed and engineered, meaning that a data transformation programme is often required, whether that data is static and dynamic. It’s also important to remember that these costs are continuous throughout an AI deployment. On the other hand, you may get away with unstructured or semi-structured input.

  • It’s vital for any business exploring AI to have a full understanding of the market before they start, and be sure that their investment will be relevant long-term.
  • Technology giants have paid billions to acquire AI start-ups and absorb their capabilities.
  • Finally, even though AI solutions simplify manual routines, people need time to fully grasp the new working method.
  • A global-scale extrapolation estimates the AI adopters’ profit at $1 trillion by 2030, which makes up 10% of the current profit pool.
  • AI is meant to bring cost reductions, productivity gains and in some cases even pave the way for new products and revenue channels.
  • The client has to be ready to kick-off AI implementation with a Discovery stage or a PoC.

Furthermore, large pre-trained models like Inceptionv3 or ResNet and transfer learning techniques also help reduce costs by allowing developers to fine-tune existing models rather than training them from scratch. Stakeholders with nefarious goals can strategically supply malicious input to AI models, compromising their output in potentially dangerous ways. It is critical to anticipate and simulate such attacks and keep a system robust against adversaries.

Factors Affecting the Cost of Artificial Intelligence

You may have seen this tech in action in AI tools like content generators and Paraphrase tools. Both require different development processes and resources and therefore have different costs. While the potential of generative artificial intelligence (AI) may seem limitless, the computing power it requires could present limitations. One estimate places the cost of a ChatGPT query at 1,000 times that of the same question asked of a normal Google search. In the initial development stages, as companies such as OpenAI seek to generate public interest, that may be an acceptable cost, even with 100 million active users added in a single month. Without identified business cases, most top managers do not support the transition to new business models through machine learning implementation.

ai implementation cost

But a higher precision rate will inevitably incur higher costs of artificial intelligence. There are several concepts included where AI is the umbrella term such as Machine Learning, Natural Language Processing, and Robotics are its sub-branches. Based on applicability, these concepts can be used, so the cost to build an Artificial Intelligence-based project is also accordingly. So, here are some best five use cases of Artificial Intelligence that showcase ample growth. It is okay that the cost of building Artificial Intelligence can be a roadblock for businesses such as newly grown brands or start-ups, but things can be handled easily.


AI-based security tools filter out non-threatening activities, which reduces false positive readings. Cybersecurity professionals can then devote their time and resources to mitigating actual threats, which decreases time to detection as well as recovery. Congress has advanced legislation to appropriate $52 billion in funding for the CHIPS for America Act, which aims to increase semiconductor manufacturing and supply chain resilience in the United States.