Nowadays, it is not surprising to see companies using AI to get huge benefits. Even a 2022 report from Mckinsey states that AI adoption globally is 2.5x higher than in 2017.
This data represents how the future of businesses is going to change due to AI adoption. Similarly, a Mckinsey 2020’s report signalling that revenue production by AI adoption will be doubled between 2020 and 2024.
While looking at the competition behind AI adoption, a well architect AI implementation can be a game-changing event for any organisation and make them stand out from the competitors. However, a well architect AI implementation is challenging to get, and we at DSW | Data Science Wizards focused on creating AI-enabled ecosystems in different organisations through our tech-agnostic platform UnifyAI, so in this article, we are going to look at the challenges that any organisation can face during the journey of AI adoption and how UnifyAI can help organisations to help simply face and overcome those challenges.
Let’s dive in.
Challenges in AI Adoption
Suppose you are considering using AI to complete any of your operations or looking for a scalable way to implement AI in your organisation. In that case, it is important to become aware of the challenges you might need to cope with and how UnifyAI, as a holistic approach, can resolve them. That way, you can successfully get a seamless path to AI adoption.
Here are the most common challenges you will meet in making AI work for you.
Organisations don’t understand the need for AI projects
If an organisation is doing good business, then its team often becomes reluctant to adapt to noticeable changes and the adoption of technologies like artificial intelligence can be a challenging shift or task to perform.
With this challenge convincing investors to invest in AI is also a challenge if the returns are unclear because where AI adoption is concerned, you will only sometimes be clear about what you are trying to build. Such uncertainty always becomes so much tricky to manage.
In that case, you need not worry about the uncertainties involved in AI adoption because, as an AI consultant DSW has phenomenal expertise in helping organisations become AI-enabled. So we understand the potential benefits of implementing AI ecosystems, projects and use cases. Using our this expertise, organisations can understand the value of their data and the involvement of AI with it.
Company’s data is not appropriate
To get an effective AI agent, it is suggested to model it using a sufficient amount of well-optimized data. Simply, high-quality data can give a high-quality AI model. Unfortunately, sometimes older or inadequate data management systems cause difficulties in AI adoption.
For example, suppose in any organisation, any CRM tool is being utilised for collecting demographic data, purchase behaviour and interaction data of customers. In that case, the organisation may have data that can be used for intelligent modelling. But if the data management is not optimised, that can mean the organisation is not interested in AI adoption. With the wrong data management system, making a structured way becomes difficult.
These insufficient data management systems lead to confusing data lakes and silos, and considering this fact, we have designed our platform in such a way that it can easily gather and structure only important data from complex data systems and involve that data in the process of making data as a value for you.
Organisations lack the skill-set
Being equipped with high-quality data is not only a requirement to become AI-enabled but also needs the right skill sets, directions and components to make AI use cases work.
In the competition of AI adoption, often organisation struggle to get the right data and AI skill set that leaves them unable to become AI-enabled. Even where companies have a high degree of in-house expertise, a lack of structuring AI components can become a significant obstacle in AI adoption.
Using UnifyAI, you can eliminate many common problems like Insufficient processing, multiple data and model pipeline, and inefficient orchestration. It works as a solution for organisations that needs more major skill sets to complete MLOps.
Organisations struggle to find good vendors to work with
Somehow organisations understand that AI adoption is a way to perform better than before, and they believe that they don’t understand how to use their data and technology together to deliver higher business value.
As a result, companies try to get AI adoption done with vendors, and negative experience with vendors makes companies reluctant to dive into AI adoption. However, with experienced AI vendors results of the work can speak for themselves.
DSW has developed itself as a user of the finest state-of-the-art technologies to fulfil customers’ demand for higher business values using their data. We have constantly been developing cutting-edge solutions that can provide a seamless experience of creating AI environments throughout organisations. As a result, UnifyAI has come in front of us, which not only makes AI adoption easy but also allows us to scale newer or older AI use cases and projects.
Organisations are not able to find a good use case
Implementing AI just for the sake of it often doesn’t encourage company-wide adoption. When any organisation doesn’t have a strong AI use case, it will always be challenging to deliver high business value. Without a strong reason behind AI adoption, it only makes a difference towards a technological perspective. So it is a best practice to apply AI only when you know how it will be a serious advancement.
However, there are more often chances that a company’s data has the potential to grab high business values, but somehow they need help understanding it. Our expertise in the field comes as a solution for such an organisation that helps organisations understand the value of their data and gain benefits using AI.
Low explainability of AI Team
Often, it has been observed that the data and AI teams end up working with data silos, meaning that most AI projects are stuck and die in dealing with vast amounts of data. Even after experimentation, they face considerable problems in production.
As a result, they plan architectures of AI workflow, which only increases the complexity of making scalable AI projects, and the benefits from such projects need to be explained better to get the workforce for implementation.
An AI team can avoid this complexity by using platforms such as UnifyAI that give them a seamless experience of taking AI use cases into production with high efficiency and explainability.
Fear of overhaul legacy systems
Nowadays, it is astonishing to see any organisation still rely on its old infrastructure to make its IT operation work. In such an organisation, management chooses not to adopt technologies like AI because of fear of the costs behind adoption.
If the cost is a concern, then thinking of AI adoption as a costly program is a myth because there are open-source technologies that make AI adoption simple and cheap. However, doing so might need an operational framework on the premises.
UnifyAI empowers organisations with an efficient operational framework in which all cutting-edge technologies are optimised and structured to give an easy and throughout experience from experiment to production to any organisation.
One sure thing is that there are huge benefits from AI adoption.
The complexity of AI Program Integration
In most cases, it has been seen that an AI team has made an optimised program that can potentially give huge benefits. Still, the integration of these programs needs a huge amount of engineering, and this engineering becomes an obstacle for companies.
More engineering effort for integration meant the solution never saw the light of day. This all happens because lack of skill sets for taking AI use cases from experimentation to production.
One of the most critical features of UnifyAI is that it is engineered to deliver all simple or complex AI projects into production without requiring a high level of model engineering. This feature not only avoids the significant complexities in taking AI into production but also gives an environment using which one can scale AI.
AI Governance
In one of our articles, we learned about AI/ML model governance, and many AI projects face problems of implementation in real life. For example, to operationalise a cloud-based banking platform in Poland, the organisation needs to build data centres only in Poland.
Often to build an AI use case, organisations need a massive amount of data, whether the data is sensitive or not, but it needs to keep in an adequately secure environment. In the failure of that organisation could face a considerable fine.
Such rules and regulations become obstacles to AI adoption because governing bodies often halt solutions in their track. Therefore, as we keep track of such an important step behind A implementation, we also help organisations to understand and get work done while following every piece of information, rules and regulations.
No challenges are greater than the results
Although there are many challenges in AI adoption, organisations should be confident in the way of AI adoption. It has always been said that becoming aware of the pitfalls is an essential first step.
After knowing all the obstacles an organisation might face, it can become more focused on finding strategic designs that can increase its chances of success. Looking at the potential benefits of AI adoption, there is no challenge that is too great to overcome.
About DSW
Data Science Wizards (DSW) aim to democratise the power of AI and Data Science to empower customers with insight discovery and informed decision-making.
We works towards nurturing the AI ecosystem with data-driven, open-source technologies and AI solutions and platforms to benefit businesses, customers, communities, and stakeholders.
Through our industry-agnostic flagship platform, UnifyAI, we are working towards creating a holistic approach to data engineering and AI to enable companies to accelerate growth, enhance operational efficiency and help their business leverage AI capabilities end-to-end.
Our niche expertise and positioning at the confluence of AI, data science, and open-source technologies help us to empower customers with seamless and informed decision-making capabilities.
DSW’s key purpose is to empower enterprises and communities with ease of using AI, making AI accessible for everyone to solve problems through data insights.
Connect us at contact@datasciencewizards.ai and visit us at www.datasciencewizards.ai