Integrating advanced technology such as AI/MI, IoT or data science can be a challenging lift for organizations; in many cases, it is difficult to know where to begin, let alone know how to make it all work. In fact, only 8% of businesses have put an AI model into production (according to recent studies from Gartner and McKinsey & Company).
However, increasing that 8% isn’t impossible. It just takes a little bit of awareness and defining the problem(s), learning from your own – and others’ – mistakes, and application of appropriate solutions and tools. And because AI/ML has demonstrated such strong value when wielded well, I think it’s worth the effort to do it right to make it work for your organization.
Breaking the barriers to entry
While there are challenges to commercialising AI/ML, you can overcome them. Ultimately, it boils down to how well you can understand and then apply the appropriate tools and methodology.
Picking the right platform that addresses all the challenges of integrating AL/ML into your business is key in breaking those barriers. Data platforms for business intelligence have solutions that are designed to assist organisations in achieving overall modern business intelligence and placing an efficient AI model. Furthermore, it takes into account the inefficiencies associated with defining the problem, gathering data, developing the model, visualising the results, and deploying to production through the following capabilities:
- Facilitating model management
- Democratising data exploration and profiling
- Embedding raw code into the data platform to align with current modelling processes
- Automating testing and prototyping models
How AI impacts businesses
Perhaps the most important piece to the AI/ML process is knowing what you want to achieve. This begins with defining the business problem and continual revision of how that objective is being reached. Even if everyone is aware of the issue, that doesn’t mean they’re all reacting the same way. Business initiatives aren’t always aligned with those of the IT or analytics team. You may also have individual systems and tools in place which make the overall process difficult.
Many businesses integrate AI technology into their enterprise models to improve various aspects across the organisation – from operational costs, efficiency, growing revenue and making data-driven decisions. Deploying an AI model into your business provides a consolidated and streamlined mechanism that you can manage your data more efficiently and ultimately make better decisions with it. The adoption of AI can be particularly helpful when dealing with big data and analytics in areas like customer insights and IT efficiencies. AL/ML models can analyse data in real time, identifying patterns and anomalies to make decision-making much faster.
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How modern BI factors in
Modern BI is an important aspect that ties in with deploying AI into your company. With the demand for faster decisions rapidly growing, the complexity of available solutions puts additional pressure on IT leaders to put the right kind of data at the fingertips of consumers. The main challenges of implementing an AI/ML model are:
- The difference in internal systems
- The steps in the process not always being linear
- Consideration of all teams across the business to ensure they are all aligned.
Modern BI helps overcome these challenges by democratising the data, putting real-time data into the hands of your employees, fostering innovation, better decision-making and solving complex problems more efficiently.
With the help of AI and BI, you can get access to 100% of your data across 100% of your business.
By Collin Mechler, Director, Practice Areas
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.
Collin has degrees in engineering, with a Masters in Materials Science & Engineering from MIT.
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