Gartner mentions Altilia in its 1st market guide on Intelligent Document Processing

Antonio Colasante

By altilia on April 22, 2022

Antonio Colasante is the chief growth officer at Altilia, with more than 15 years’ experience as a sales director, including as Sales Director Financial Services Analytics South Europe for Oracle. He developed his career within leading consulting companies, such as Accenture, Everis, Deloitte, and other major financial institutions in Italy and abroad after graduating with a degree in Economics. He is a voice of authority on risk management, and has been a guest speaker at seminars for ABI and Borsa Italiana.

By altilia on April 22, 2022

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Taking on the challenge of managing complex Machine Learning Operations

As we explored in our previous blog, demand for AI-based solutions is growing year on year as organizations adopt and experiment with artificial intelligence to drive competitiveness and efficiency in a tough business environment. We examined the key talent and skills gap challenge that companies, particularly small and medium enterprises, are facing as the explosion of AI projects outstrips the supply of qualified data experts required. Now we want to go deep on another significant issue that has been identified by business leaders and consultants as a blocker to growth. And also show how Altilia can offset this challenge. Managing Machine Learning (ML) Operations When companies consider introducing new business process automation solutions, they often underestimate the complexity of managing the lifecycle of an AI project. ML Ops are defined as a set of practices that combine the implementation of Machine Learning, DevOps, and Data Engineering models, with the goal of developing, releasing, monitoring, and the scaling into production high quality ML systems. This is done by leveraging organizational, cultural, and technological aspects that support the governance and automation of the ML model lifecycle. Properly managing ML Ops is a major challenge even for large and structured companies. In fact, according to a recent survey by the Artificial Intelligence Observatory of the Polytechnic University of Milan, out of a sample of 80 large companies operating in Italy that have already started AI projects, 71% say they have not yet introduced ML Ops as a structured practice. Among the stated reasons for this delay stands out the lack of time they devote to AI initiatives compared to traditional business activities (43%) and the lack of internal expertise to manage ML Ops (33%), an issue which brings us back to the challenge discussed in our last blog. Deloitte identified this ML Ops issue in their State of AI report: “Despite evidence that establishing clear processes and redefining roles to deliver quality AI results in improved outcomes, there has been little growth in the market in terms of adopting such practices, according to survey respondents. In both the fourth and fifth editions, just one-third of respondents reported that their companies are always following MP Ops, redesigning workflows and documenting AI model life cycles.” The ML Ops cycle in its complexity could be broken down in three distinctive parts: The Business process analysis/understanding part refers to the mapping of internal processes, systems, and any other key element to understand the "problem" to be solved: such as defining requirements, objectives, key outcomes, and formulating use case hypotheses for ML applications. The Data & Model Preparation part concerns the collection and refinement of data to train the ML model; the selection of the ML model with the best fit to solve the problem; the testing of different algorithms, features and hyperparameters; keeping track of all experiments, and maximizing code reusability. Continuous Operations & Improvement refers to the continuous update of the solution with each product release; the continuous training of the algorithm, with the storage and processing of new data to update the model; the continuous monitoring of the model to keep track of changes in performance and accuracy. How can Altilia help to overcome the ML Ops challenge? Altilia’s approach is offering a comprehensive platform to manage the whole AI implementation lifecycle with an end-to-end approach. The platform is built overtime and improved upon experience by Altilia, to hide the complexity of managing ML Ops, and to simplify the training and the optimization of AI models over time. This eliminates the need to dedicate an internal team (with limited experience) just to maintain an AI implementation project, to monitor its performance over time and to manage its infrastructure. Additionally, the automation challenges faced by businesses are rarely completely unique. Using a platform approach, Altilia can replicate pre-tested use case specific solutions and adapt them to perfectly fit the needs of the customer. This greatly simplifies the process analysis, reducing the uncertainty when defining implementation goals, a leading to a greater confidence about the expected outcomes. Altilia simplifies data and model preparation by giving access to library of generalized pre-built AI models, including Large Language Models (LLMs) that can be trained and adapted, according to the customer’s specific requirements. The training of models is simplified, thanks to our document annotation interface, that allows users to easily generate examples for the system to process. Lastly, Altilia facilitates the monitoring and fine-tuning of AI models to increase their accuracy over time and prevent data drift. Contact Altilia here to learn how our unique AI technology platform can help your organization overcome these challenges.

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How Altilia can help you bridge the AI skills gap

The demand for AI-based solutions in the enterprise is ever increasing as more and more companies view artificial intelligence as a solution to automate core business process, driving operational excellence and accelerating digital transformation. Top-level research from the Artificial Intelligence Observatory of the Polytechnic University of Milan shows that, from a sample of 200 large organizations operating in Italy, 59% have already started executive projects in the AI field, 77% of which are running more than one project in parallel. According to a recent survey by PwC in US, on a sample of 1000 large organizations, 72% have already integrated or are looking to integrate AI technologies into their operations. Full-scale deployment Deloitte’s significant State of AI study found that 79% of business leaders surveyed reported full-scale deployment for three or more types of AI applications – up from 62% in the previous year. And 76% of those leaders expect AI investments to increase in the next year. But not everything in the AI garden is rosy, particularly for small and medium sized organisations. There are many challenges affecting the effective use of artificial intelligence which can hinder the ability to tap into its potential and lead to a growing gap between those companies that have the capability and those who do not. Over two blogs, we will focus on the two big challenges facing organisations planning to use AI – and how Altilia can make the difference. The AI skills shortage The complexity of managing Machine Learning Operations The AI skills shortage McKinsey’s State of AI 2022 Survey called out the difficulties of hiring AI-related roles, with 78% of respondents citing difficulty with hiring AI data scientists, 72% struggling to hire data architects, 70% ML engineers and 69% data engineers. Perhaps surprisingly, they state that finding AI hires is perceived as more difficult than three years ago, with 57% saying that the hiring of data engineers is more difficult, 52% for data architects and 48% of ML engineers. Clearly, demand for top AI talent is outstripping supply. Among the limiting factors, the skills problem emerges as one of the most relevant. In addition to programming and software development skills, the planning and life-cycle management of an AI project, requires highly specialized skills that are still scarcely available in the labour market. These technical skills include: Skills needed to identify and prepare datasets that will be used by AI algorithms. Mathematical and statistical skills that enable the solving of complex problems related to AI applications. Skills needed to implement Machine Learning and Deep learning models (e.g., Supervised & Unsupervised Learning techniques). Skills related to data storage infrastructure, cloud services, and integration of AI systems with other enterprise software and applications. In addition, since AI automation projects are ultimately aimed at boosting operational business processes, it is crucial to create a match between AI skills and all the skills involving the application of specialized domain-specific knowledge, methodologies, and operational techniques for the operation of specific areas, such as Supply Chain, Marketing and Sales. For this reason, a core challenge is to bridge the gap between technical AI skills and domain-specific competences within companies and bringing core AI skills to figures not traditionally related to engineering and software development. How can Altilia help to overcome the skills gap challenge? Altilia prioritizes AI democratization as the core of its approach: for this reason, the Atlilia Intelligent Automation platform is designed to maximize intelligent process automation capabilities while minimizing the complexity of managing AI algorithms for the end user. Thanks to the use of pre-built algorithms, based on transformer models, users can leverage AI capabilities even without the in-depth mathematical and statistical knowledge related to AI applications. Thanks to the use of a no-code visual interface for document annotation and review, users can autonomously prepare the dataset that will be used by AI algorithms, even without the technical skills required to implement machine learning or deep learning models. Altilia’s IPaaS cloud infrastructure, equipped with pre-built software connectors, greatly simplifies software interoperability, and removes the complexity of managing computing resources and data storage. In addition, the Altilia Intelligent Automation platform is designed for to reduce the learning curve for business-domain experts to understand and apply AI algorithms for business automation. In fact, the Altilia Labels module for document annotation, allows business-line users to intuitively transfer their knowledge into algorithms, to perfectly fine-tune the solution according to real business and process needs. The Altilia Reviews module provides a tool to review and validate the results produced by the algorithms, this is a crucial feature to guarantee AI explainability and to allow business-line users to effectively govern the process. Contact us here to discuss how Altilia can support your organization’s drive for business optimization through AI. And read our next blog on the challenge of managing ML Ops.

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Has AI business automation made a difference in 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: AI adoption is increasing, and the market is growing. 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.

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