{"id":1875,"date":"2024-11-26T03:00:50","date_gmt":"2024-11-26T03:00:50","guid":{"rendered":"https:\/\/solutionsreview.com\/thought-leaders\/?p=1875"},"modified":"2024-11-26T20:00:02","modified_gmt":"2024-11-26T20:00:02","slug":"how-to-spot-an-ai-imposter-part-3","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/thought-leaders\/how-to-spot-an-ai-imposter-part-3\/","title":{"rendered":"How to Spot an AI Imposter – Part 3"},"content":{"rendered":"
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The History of AI Enabled Business Intelligence.<\/strong><\/em><\/p>\n

The use of generative AI in business intelligence is no different than all other content creation use cases just mentioned. Remember, generative AI, with its ability to use AI to create things, has mostly just highlighted the advances that have already been made over the last decade. Therefore, to understand the use of generative AI in business intelligence, we have to understand the history of AI enabled business intelligence.<\/p>\n

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Diagram 1 – AI enabled business intelligence capabilities in 2019<\/em><\/p>\n

By 2019, every capability listed in\u00a0Diagram 1<\/em>\u00a0was already functional in several business intelligence companies. At that time, I predicted that the business intelligence companies who were investing heavily in AI would be the winners in the next phase of BI innovation. I also completed some quantitative research to determine which of these capabilities were most important to users of business intelligence. Using the results of the research, I created a data-driven ranking and applied it to all of the vendors who were already investing heavily in AI. There were only 7 companies with significant AI enablement in their platforms.<\/p>\n

If you look at the full set of capabilities listed here, it is very obviously the precursor for a business intelligence platform copilot. Assisted insight guided the user to new insight within the current user context. Automated insight provided insight that appeared the user opened the application. Next best question and next best insight provided the user options for moving forward in their discovery journey. Almost everyone was doing visual recommendations. Users could write natural language queries, and when insight was returned it included a natural language explanation of the results. When users began to type something into the business intelligence search engine, suggestions were automatically filled in based on semantics or previous searches. Image and voice recognition were already in full swing, some within the platform and some through partners.<\/p>\n

This set of capabilities should look familiar, since this is what many of the new and legacy BI vendors are promoting as innovation because they tacked generative AI onto their platforms in the last year. However, the truth is that none of this is new. All of these capabilities were already in place back in 2019.<\/p>\n

A Realistic View of AI Enabled Business Intelligence Today<\/strong><\/h4>\n

So how does what happened in 2019 impact what’s happening here in 2024? When you understand the history of AI enablement in business intelligence, the current vendor landscape falls neatly into 4 categories (See Diagram 2 below).<\/p>\n

First, there are the add-ons. These are the vendors that already have enterprise business intelligence capabilities. However, they didn\u2019t invest early in AI. Now, in the last couple of years, they tacked on generative AI.\u00a0As a result, they have limited AI enablement and limited automation.<\/p>\n

Second, there are the newbies. The newbies got excited about the potential for AI and BI in the last couple of years, so they launched new products. Most of them have flashy new AI capabilities, but they lack the necessary capabilities for the modern enterprise.<\/p>\n

Third, there are the laggards. The laggards will need to greatly accelerate their AI innovation and investment in the next couple of years. If they don\u2019t invest now, they will either be gone or continue to lag behind. These are companies that are late to market in terms of implementing any kind of AI enablement in their BI platforms.<\/p>\n

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Diagram 2 \u2013 Four categories of AI-enabled business intelligence platforms<\/em><\/p>\n

Fourth, the leaders are the companies who were already AI-enabling their platforms five and ten years ago. When generative AI hit, they already had a core AI engine functioning in their platforms. As a result, generative AI is already completely embedded in their platform, not just tacked on. They are already AI enabling and automating their entire enterprise-grade platforms. They have copilots that exist throughout the entire BI life cycle.<\/p>\n

For a complete analysis of what really matters for AI enabled BI, read the Ferraro Consulting POV paper,\u00a0Hype Alert: Not All AI Enabled BI Makes the Grade<\/em><\/strong><\/a>.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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The History of AI Enabled Business Intelligence. The use of generative AI in business intelligence is no different than all other content creation use cases just mentioned. Remember, generative AI, with its ability to use AI to create things, has mostly just highlighted the advances that have already been made over the last decade. Therefore, […]<\/p>\n","protected":false},"author":1170,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[96],"tags":[],"acf":[],"yoast_head":"\nHow to Spot an AI Imposter - Part 3<\/title>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to Spot an AI Imposter - Part 3\" \/>\n<meta property=\"og:description\" content=\"The History of AI Enabled Business Intelligence. The use of generative AI in business intelligence is no different than all other content creation use cases just mentioned. 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