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.
