Has AI business automation made a difference in 2022?

By altilia on December 21, 2022

As 2022 is coming to an end, we want to take a step back and have a look at the state of the industry of AI for business automation.

McKinsey has just published their “State of Artificial Intelligence 2022” report, based on a survey of from 1,492 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures.

The report demonstrates the growth and importance of AI to organizations, with a set of companies seeing the highest financial returns from AI continuing to pull ahead of competitors.

The results show these leaders making larger investments in AI, engaging in increasingly advanced practices known to enable scale and faster AI development, and showing signs of faring better in the tight market for AI talent.

There were two key takeaways from this important report:

  1. AI adoption is increasing, and the market is growing.
  2. Emerging AI leaders are reaping the rewards.

AI adoption is increasing, and the market is growing

Global AI adoption is increasing: companies adopting AI in at least one business area increased by 2.5 times since 2017. The average number of AI capabilities used by organizations has increased from 1.9 in 2018 to 3.8 in 2022.

Investments in AI have also increased: % of companies whose digital budget went for more than 5% to AI increased from 40% in 2018 to 52% in 2022.

Most used embedded capabilities:

  • Robotic Process Automation 39%
  • Computer Vision 34%
  • Natural Language Processing 33%
  • Deep Learning 30%
  • Knowledge Graphs 25%
  • Reinforcement Learning 20%
  • Transformers 11%

Altilia is exceptionally well positioned, since its offer is strong for both widely adopted embedded capabilities (RPA, Computer Vision, NLP) as well as new emerging capabilities whose value is not yet fully understood by the market but will become critical in the coming years (Knowledge Graphs, Reinforced Learning, Transformers).

With companies starting to understand the cross-functional potential of AI automation application, this also puts Altilia in a good position as the Altilia Intelligent Automation platform is designed to let customers leverage the same infrastructure to implement multiple use cases. This helps to increase the ROI of the platform over time.

Emerging AI leaders are reaping the rewards

McKinsey recognizes a group of “AI high performers”, a group of companies that are being particularly effective with the implementation of AI projects. Companies whose EBIT have increased more than 20% from the use of AI (8% of total respondents).

These high performers are more likely than others to follow ‘frontier’ core practices that unlock value:

  • Adopting practices for AI development at scale (AI Ops).
  • Embracing modular AI architecture to rapidly accommodate new AI applications.
  • Introducing quality control procedures to provide high-quality data to feed AI algorithms.
  • Leverage emerging low-code and no-code programs to speed up the creation of AI applications.

These AI high performers are also more likely than other companies to report that they engage in practices to mitigate AI related risks, testing the validity of models and monitoring them over time for potential issues.

Altilia is again well positioned since Altilia Intelligent Automation is designed to let customers ‘outsource’ the complexity of AI Ops, a topic we will investigate further in a subsequent blog post.

Our platform has a modular architecture and it allows to simplify the deployment of multiple AI applications, and it’s also designed as a low-code/no-code platform, to simplify the creation of AI applications.

It is important to understand that Altilia Intelligent Automation combines the use of Large Language Models (LLMs) with no-code technology, and this constitutes a breakthrough.

LLM are already popular and publicly discussed, but those are general purpose technology that doesn’t perform very well for specific business applications.

By utilizing Altilia’s no-code approach to AI models training, these ‘general purpose’ technologies can be fine-tuned by enterprises, to turn them into a ‘domain-specific’ technology.

This will allow businesses of all sizes to experience the benefits of LLM technology in their specific business applications, allowing them to obtain returns on AI investments, comparable to the “high performers” mentioned by McKinsey.

By altilia on December 21, 2022

Explore more stories like this one

Altilia is recognized as Major Player in the 2023-2024 IDC MarketScape Worldwide Intelligent Document Processing Vendor Assessment

Altilia, as a leading innovator in the field of Intelligent Document Processing (IDP), is proud to announce it has been recognized as a Major Player in the IDC MarketScape: Worldwide Intelligent Document Processing Software 2023–2024 Vendor Assessment (doc # US49988723, November 2023). We believe this acknowledgment represents yet another milestone for Altilia, reaffirming its position as a leader in the ever-evolving landscape of Intelligent Document Processing technology. With a dedicated team of over 50 highly experienced AI professionals, including scientists, researchers, and software engineers, Altilia aims to democratize the use of AI to help enterprises automate document-intensive business processes. As we celebrate this recognition from the IDC MarketScape, Altilia will continue its efforts to shape the future of document processing, bringing cutting-edge solutions to the forefront of the IDP market, and offering organizations unparalleled efficiency, automation, and knowledge management capabilities. About IDC MarketScape: IDC MarketScape vendor assessment model is designed to provide an overview of the competitive fitness of ICT (information and communications technology) suppliers in a given market. The research methodology utilizes a rigorous scoring methodology based on both qualitative and quantitative criteria that results in a single graphical illustration of each vendor’s position within a given market. IDC MarketScape provides a clear framework in which the product and service offerings, capabilities and strategies, and current and future market success factors of IT and telecommunications vendors can be meaningfully compared. The framework also provides technology buyers with a 360-degree assessment of the strengths and weaknesses of current and prospective vendors.

Read more

How the technology behind Chat GPT can work for your organization

The explosion of interest and publicity in Artificial Intelligence in recent months has come from the advent of Large Language Models, specifically OpenAI’s ChatGPT, which set the record for the fastest-growing user base in January. Suddenly it seems like everyone is fascinated by the coming surge of AI with new applications, creating excitement and fear for the future. When Google’s so-called “Godfather of AI” Dr Geoffrey Hinton warned about “quite scary” dangers, it made headlines around the world. Behind the hype So, it is important to understand what is behind the hype and see how it works and what your organization can use to build future value. This blog is split into two: first we learn about Natural Language Processing, the branch of computer science concerned with giving machines the ability to understand text and spoken words in much the same way humans can. And then we will go deeper on Large Language Models (LLMs), which is what ChatGPT and others like Google’s Bard are using. NLP combines computational linguistics with statistical, machine learning, and deep learning models to enable computers to process human language in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment. NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real time. There’s a good chance you’ve interacted with NLP in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. But NLP also plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processes. There are two sub-fields of NLP: Natural Language Understanding (NLU) uses syntactic and semantic analysis of text and speech to determine the meaning of a sentence, similarly to how humans do it naturally. Altilia uses Large Language Models for this. Natural Language Generation (NLG) enables computers to write a human language text response based on data input. ChatGPT uses LLMs for NLG. Large Language Models (LLMs) LLMs are a relatively new approach where massive amounts of text are fed into the AI algorithm using unsupervised learning to create a “foundation” model, which can use transfer learning to continually learn new tasks. The key is using huge volumes of data. The training data for ChatGPT comes from a diverse set of text sources, including billions of web pages from the internet, a huge number of books from different genres, articles from news websites, magazines and academic journals and social media platforms such as Twitter, Reddit and Facebook to learn about informal language and the nuances of social interactions. The model is then able to predict the next word in a sentence and generate coherent text in a wide range of language tasks. Altilia does exactly the same, but uses this capability to provide enterprise tools for specific business use cases. Technology breakthrough Overall, NLP is the core technology to understand the content of documents. LLMs are a breakthrough in the field as they allow a shift from where an NLP model had to be trained in silos for a specific task to one where LLMs can leverage accumulated knowledge with transfer learning. In practice, this means we can apply a pre-trained LLM and fine-tune it with a relatively small dataset to allow the model to learn new customer-specific or use-case specific tasks. We are then able to scale up more effectively, it can be applied more easily for different use cases, leading to a higher ROI. For more information on how Altilia Intelligent Automation can support your organization to see radical improvements in accuracy and efficiency, schedule a demo here.

Read more

Leveraging GPT and Large Language Models to enhance Intelligent Document Processing

The rise of Artificial Intelligence has been the talk of the business world since the emergence of ChatGPT earlier this year. Now executives around the world find themselves in need of understanding the importance and power of Large Language Models in delivering potentially ground-breaking use cases that can bring greater efficiency and accuracy to mundane tasks. Natural Language Generation (NLG) enables computers to write a human language text response based on human generated prompts. What few understand is that there is still a deep flaw in the ChatGPT technology: up to 20-30% of all results have inaccuracies, according to Gartner. What Gartner have found is that ChatGPT is “susceptible to hallucinations and sometimes provides incorrect answers to prompts. It also reflects the deficiencies of its training corpus, which can lead to biased or inappropriate responses as well as algorithmic bias.” To better understand this, it’s key to consider how LLMs work: hundreds of billions of pieces of training data are fed into the model, enabling it to learn patterns, associations, and linguistic structures. This massive amount of data allows the model to capture a wide range of language patterns and generate responses based on its learned knowledge. However, as vast training data can be, the model can only generate responses as reliable as the information it has been exposed to. If it encounters a question or topic that falls outside the training data or knowledge cutoff, responses may be incomplete or inaccurate. For this reason, and to better understand how best to use LLMs in enterprise environments, Gartner outlined a set of AI Design Patterns and ranked them by difficulty of each implementation. We are delighted to share that Altilia Intelligent Automation already implements in its platform two of the most complex design patterns: LLM with Document Retrieval or Search This provides the potential to link LLMs with internal document databases, unlocking key insights from internal data with LLM capabilities This provides much more accurate and relevant information, reducing the potential for inaccuracies due to the ability to the use of retrieval. Fine-tuning LLM The LLM foundation model is fine-tuned using transfer learning with an enterprise’s own documents or particular training dataset, which updates the underlying LLM parameters. LLMs can then be customized to specific use cases, providing bespoke results and improved accuracy. So, while the business and technology world has been getting excited by the emergence of ChatGPT and LLMs, Altilia has already been providing tools to enterprises to leverage these generative AI models to their full potential. And by doing so, thanks to its model’s fine-tuning capabilities, we are able to overcome the main limitation of a system like OpenAI’s ChatGPT, which is the lack of accuracy of its answers. For more information on how Altilia Intelligent Automation can help your organization, schedule a free demo here.

Read more