Technology
The trouble with putting AI to work – and how to do it
Published by Jessica Weisman-Pitts
Posted on October 12, 2022
5 min readLast updated: February 3, 2026
Global Banking & Finance Review®
Company
Published by Jessica Weisman-Pitts
Posted on October 12, 2022
5 min readLast updated: February 3, 2026
By Collin Mechler, Director, Practice Areas, Domo, Inc
Integrating artificial intelligence (AI) into any business comes with challenges. Any IT leader who has tried to integrate advanced technologies has most likely encountered numerous obstacles during the process of introducing AI, machine learning (ML), the internet of things (IoT) or data science into a functioning business. It is tough to know where to begin, let alone, how to make it all work.
According to Gartner, just 8% of businesses have introduced an AI model into production. This fact demonstrates the trials and tribulations which come with the introduction process. However, breaking into the top 8% isn’t impossible. What’s more, because AI and ML have the potential to transform business, it’s worth persevering, learning from your own – and others’ mistakes – and making the effort to integrate it.
Breaking the barriers to entry
The process of integrating AI technology into businesses can be overwhelming, however, it is more achievable now than ever before. All you need to do is understand the process and what you want to achieve,then select the appropriate tools and methodologies to help you through the process.
Choosing the right platform that addresses all the challenges of integrating AI and ML models into your organisation is integral in breaking those barriers. Data platforms which use AI, offer solutions which are designed to assist organisations to achieve business intelligence. Furthermore, they take into account the inefficiencies associated with the process of integrating AI which include: defining the problem, gathering data, developing the model, visualising the results, and deploying to production.
This is achieved by fostering model management, democratising data exploration, embedding raw code into the data platform to align with current modelling processes, and automation testing and prototyping models.
How AI impacts businesses and data
Integrating AI into everyday business has a huge impact, especially when introduced thoroughly. It is important to analyse your current business needs and establish strategy-based goals in which introducing AI technologies would prove to be most beneficial. Once AI or ML processes are integrated into a business model, this strategy and the subsequent goals must be continually revised in order to ensure that you understand how AI is benefitting your business.
It is important to understand that even if everyone is aware of the business problem, there is no guarantee that they are all impacted or are reacting in the same way. Business initiatives are rarely the same across every department, and it is important to consider the smaller, individual systems and tools in place which make overall processes difficult. AI provides businesses with the opportunity to target and automate difficult processes. The presence of AI ultimately relinquishes employees time and allows for maximum productivity efficiency levels. .
Many businesses integrate AI technology into their enterprise models to improve various elements across the organisation – from operational costs, efficiency, growing revenue and making data-driven decisions. Deploying an AI model within your business provides a streamlined structure where you can manage your data more efficiently and ultimately make better and quicker decisions with it.
The adoption of AI can be particularly helpful in allowing organisations to understand their data better, faster and more coherently. In increasingly digital times, it can be difficult to keep track of big data and analytics. This is an issue which can span over various aspects of a business including customer insights and IT efficiencies. AI and ML
models can analyse data in real time, identify patterns as well as anomalies and communicate this in ways which are easy to understand. Ultimately, implementing AI and ML technologies have an overall positive impact on businesses and the types of data influencing everyday life and decisions.
How modern BI factors in
Modern BI is an important component that ties in with applying AI models within your company. The complexity and variety of available solutions places additional pressure on IT leaders to put the right kind of data at the fingertips of consumers, as the demand for faster decisions increases.
For businesses, the main challenges of implementing an AI or ML model are: ● The unique steps of introduction depending on the needs of a business ● The alterations and business-wide change to internal systems
Modern BI aids in overcoming these challenges by democratising data, putting real-time information into the hands of employees, facilitating innovation and allowing better decision-making processes Ultimately, achieving modern business intelligence allows organisations to solve complex problems more efficiently. With the help of AI and BI, you can streamline your business by accessing 100% of your data across your whole business.
Author Bio:
A specialist in large-scale digital transformations, Collin runs Domo’s Practice Areas, a group of deep industry and technology experts charged with the goal of providing stronger impact and value to Domo’s clientele. Collin is himself an expert in supply chain, retail, and manufacturing, specialising in the productionization of advanced tech (AI/ML, IoT, and data science).
Prior to working for Domo, Collin ran one of two major business units at Element AI (now the AI division of ServiceNow), an AI enterprise tech think tank specialising in novel applications of deep learning and machine learning. Collin’s background also includes extended stints at Blue Yonder (nee JDA) and Accenture.
Artificial intelligence (AI) refers to the simulation of human intelligence in machines programmed to think and learn like humans. It encompasses various technologies, including machine learning and natural language processing.
Machine learning is a subset of AI that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention. It is widely used in data analysis and predictive modeling.
Business intelligence (BI) refers to technologies and strategies used by organizations to analyze business data. BI helps in making informed decisions by providing actionable insights from data analysis.
Data science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
The internet of things (IoT) refers to the network of physical devices connected to the internet, enabling them to collect and exchange data. IoT is used in various applications, including smart homes and industrial automation.
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