Strategies To Keep In Mind Before Adopting Artificial Intelligence.
By Anita Srinivasan
19th June, 2023
Artificial Intelligence and Machine Learning have become revolutionizing strategies that businesses are incorporating into their operations. By leveraging data science consulting services, organizations are striving to tread ahead of their competitors.
In this blog, we will discuss how processes like document processing, market predictions, and CRM (Customer Relationship Management) services can be accelerated using AI and ML tools. We will also discuss bottlenecks and hurdles like biased algorithms and the need for extensive data preparation, which organizations must overcome when using AI-powered tools.
Let us look at some statistical information that has been made available in recent years on data science implementation:
A survey by Gartner Inc.(1) recorded that 37% of companies have implemented AI in their operations. The adoption rate of the technology has tripled in the last year.
Investment in AI by leading businesses continues to skyrocket by 91.5%.
Gartner Inc.(2) predicts that by 2027, Chatbots and Virtual Customer Assistants will become the primary customer service channel for businesses.
Deloitte(3) reports that 82% of companies that have adopted AI and cognitive technologies early on have noticed a substantial increase in ROI from their production-level projects.
Accenture(4) reports state that AI can increase profitability by an average of 38% across 16 industries by 2035.
A report by McKinsey and Company(5) a data analytics company, states that there has been an increase in AI adoption rate by 25% in businesses that operate in the healthcare, pharmaceuticals, finance, and retail sectors.
According to a PWC study(6), 72% of business leaders believe that AI will be advantageous for their enterprises in the future, with 67% believing that AI and data science consulting services will open new doors of opportunities for them.
AI Strategy Components For Businesses:
In addition to tools and expertise, organizations must strategize how to incorporate AI and ML tools to derive maximum benefits. In this blog, we will look at three key strategy components that businesses must consider:
Before investing in AI and ML technology, organisations must establish a case study to convince stakeholders of an AI investment. Businesses must research case studies or hire data analytics companies concerning AI adoption and how their operations can benefit by using these technologies.
For utilizing organizational resources, leadership support is crucial. Leaders must communicate with teams, emphasizing the need for AI in their operations and how an effective strategy can contribute to the overall growth of their organization.
All organisational teams, departments, and leaders must collaborate and be willing to expend time and effort for the success of an AI strategy. Teams and departments must prioritize tasks and brainstorm ideas for an effective implementation process.
The Starbucks company is one such real-world examples of a business that has successfully utilised Machine Learning algorithms in operations such as digital marketing, procurement processes for improving sales and other functions of the business.
Hurdles businesses must overcome while utilizing Artificial Intelligence and Machine Learning
Although AI and ML can make your day-to-day business operations easy to manage and conduct, utilizing these technologies come with their own set of challenges.
One of the biggest hurdles that businesses must overcome, is the need for extensive data preparation. For these technologies to operate effectively, businesses are required to have clean, relevant, and diverse datasets to train these algorithms to analyze the data. It can be a time-consuming and expensive process for businesses that have substantial and complex datasets.
These technologies also pose a threat of presenting biased algorithms. For algorithms to deliver unbiased results, businesses must ensure that their data sets accurately represent their target audience and are unbiased. This is particularly crucial for finance and healthcare sectors as mismatched data can result in grave consequences for individuals and society.
Strategies such as investing in data quality and governance, using diverse datasets to train algorithms and training employees for new roles can be beneficial in addressing these challenges. Using low code platforms such as Microsoft Power Platforms that provide SaaS offerings can make the journey of adopting AI technology seamless for businesses.
Tools that businesses can use for implementing Artificial Intelligence and Machine Learning
Tensor Flow: Tensor Flow is an open-source software library developed by Google for building and training machine learning models. It is extensively used by developers and researchers for several applications from image recognition to natural language processing.
Amazon Web Services (AWS): AWS provides businesses with various AI and ML tools such as Amazon SageMaker for building ML models, Amazon Recognition for image and video analysis and Amazon Comprehend for natural language processing.
Microsoft Azure: Microsoft Azures comes with an extensive set of AI and ML tools such as Azure Machine Learning for building ML models, Azure Cognitive Services for natural language processing and Azure Data bricks for data engineering and ML.
Google Cloud AI Platform: Google Cloud AI Platform comes with a range of tools and services that help in building and deploying ML models that include Tensor Flow, Keras and AutoML.
While undertaking the process of selecting an AI and ML tool or platform, business must consider whether these tools are specific to their needs and requirements and if they have the proper expertise to effectively implement these technologies.
Businesses must also ascertain the level of complication a problem presents. If it is a simple text analysis, a basic data analysis or an image analysis, businesses can opt for low code platforms like Microsoft Power Platform or other intelligent process automation platforms like UIPath or H2O.
What’s next for Artificial Intelligence and Machine Learning in Business .
The evolution of AI and ML technologies provides businesses with new avenues to explore when it comes to their usage in their operations. AI and ML can be used for supply chain optimization and automation, for improving fraud detection and enabling more personalized and targeted marketing campaigns.
Businesses are required to be cautious and mindful of the ethical and social implications that these technologies present. Especially in the areas of privacy, bias, and job displacement. Businesses need to consider investment in transparency, accountability, reskilling and upskilling their work force for newer roles while adopting data science technologies.
The technologies of AI and ML have the potential to transform how businesses will operate and compete in this digital age. By effective utilization of these technologies, businesses can stay ahead of the curve in their respective industries.
For more understanding about AI and ML strategies, check out our informational video on “Top 3 AI ideas you can use for your business.”