{"id":14992,"date":"2023-04-18T00:00:00","date_gmt":"2023-04-17T23:00:00","guid":{"rendered":"https:\/\/members.dataiq.global\/articles\/ai-for-bi-the-inevitable-and-exciting-future-for-data\/"},"modified":"2023-04-18T00:00:00","modified_gmt":"2023-04-17T23:00:00","slug":"ai-for-bi-the-inevitable-and-exciting-future-for-data","status":"publish","type":"article","link":"https:\/\/www.dataiq.global\/devstage\/articles\/ai-for-bi-the-inevitable-and-exciting-future-for-data\/","title":{"rendered":"AI for BI \u2013 The inevitable and exciting future for data"},"content":{"rendered":"<p><strong>AI and BI\u00a0<\/strong><\/p>\n<p>Recently, the public focus has been on high-profile AI cases such as <a href=\"\/articles\/chatgpt--do-androids-write-of-electric-sheep-or-just-what-i-tell-them\" target=\"_blank\" rel=\"noreferrer noopener\">ChatGPT<\/a>, but the reality is that AI has been utilised by businesses for years and continues an upward trajectory of development. One of the main areas that AI has been used is to improve business intelligence (BI).\u00a0\u00a0<\/p>\n<p>\u00a0\u00a0<\/p>\n<p>As the amount of data sourced keeps increasing, it has become harder for traditional methods to keep pace with developing useful insights and utilising the data sets \u2013 the adoption of AI has allowed BI to obtain far more valuable insights from the data available. Such insights improve effectiveness from operations to markets, increase the likelihood of success for new products and, arguably most importantly, they help drive data-led decisions for business leaders.\u00a0\u00a0<\/p>\n<p>\u00a0\u00a0\u00a0<\/p>\n<p>Furthermore, the integration of machine learning capabilities within BI platforms means there are countless opportunities for businesses to create their own forecasts and predictions, as well as automating business processes.\u00a0\u00a0<\/p>\n<p>\u00a0\u00a0<\/p>\n<p><strong>Predictive analysis\u00a0<\/strong><\/p>\n<p>Hands up if you have a subscription to a streaming platform or a music platform? Congratulations, you have most likely been recommended new shows to watch and bands to enjoy thanks to AI predictive analysis. It is a tool that can be adapted to any sector \u2013 banking, B2B, B2C, media \u2013 and it is able to cross the line between customer experience, development of new products and predicting trends, making it invaluable for businesses.\u00a0<\/p>\n<p>\u00a0\u00a0<\/p>\n<p>\u201cDespite the common perception that AI will replace humans, I believe there is great power in taking a human-centred approach to AI,\u201d said Sato. \u201cBy considering where it can augment human capacity or improve our productivity to free us up to perform higher level tasks, it will unlock a lot more value in the short- and mid-term. Many everyday products are enhanced by AI, and generative AI tools like ChatGPT and Midjourney are showing how quickly they can be adopted by humans and deliver value straight away.\u201d\u00a0<\/p>\n<p>\u00a0\u00a0<\/p>\n<p><strong>Challenges of AI in BI\u00a0<\/strong><\/p>\n<p>As with all new developments, there are problems that need to be understood and addressed. When it comes to utilising AI in business, some of these issues include:<\/p>\n<ol>\n<li>Algorithm bias<\/li>\n<li>Data scarcity<\/li>\n<li>Computing limitations<\/li>\n<li>Data compliance and security<\/li>\n<\/ol>\n<p>\u201cIt is important to remember that the excitement about the AI\u2019s potential, even when built with the best intentions, can still cause unintended consequences in the world,\u201d said Sato. \u201cResponsible technology strategies are becoming an important topic for business leaders, and we are seeing an increasing interest to adopt them.\u201d\u00a0<\/p>\n<p>\u00a0<\/p>\n<p>Rebecca Parsons, chief technology officer, Thoughtworks, added, \u201cNot only are <a href=\"https:\/\/www.thoughtworks.com\/en-gb\/insights\/reports\/the-state-of-responsible-technology-report\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">today\u2019s executives<\/a> starting to grasp the urgent need for the responsible use of technology, but they are seeing the solid, enterprise-enhancing reasons for doing so.\u201d\u00a0<\/p>\n<p>With all this in mind, there is no reason that businesses eager to improve their BI capabilities through the adoption of new AI tools cannot do so in a smooth, cost-effective manner. It will require some planning and a deep understanding of the challenges that can crop up regarding AI in BI, but the future looks set to include a much more prominent role for AI and, usually, the first ones to embrace the change are those that prosper.\u00a0\u00a0<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The use of artificial intelligence for businesses has escalated dramatically in recent years and there is good reason for this trend. However, there are some caveats to bringing in a new AI tool without fully understanding its capabilities.<\/p>\n","protected":false},"author":19,"featured_media":14993,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","_searchwp_excluded":"","footnotes":""},"categories":[129,398],"tags":[217,169,218,741,177,236,220,91,221],"pillar":[],"class_list":["post-14992","article","type-article","status-publish","format-standard","has-post-thumbnail","hentry","category-editorial","category-public","tag-ai","tag-analytics-and-insight","tag-artificial-intelligence","tag-bi","tag-business-intelligence","tag-investment","tag-tech","tag-technology","tag-tools"],"acf":[],"publishpress_future_action":{"enabled":false,"date":"2026-06-16 21:14:11","action":"change-status","newStatus":"draft","terms":[],"taxonomy":"category","extraData":[]},"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/article\/14992","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/article"}],"about":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/types\/article"}],"author":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/users\/19"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/comments?post=14992"}],"version-history":[{"count":0,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/article\/14992\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media\/14993"}],"wp:attachment":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media?parent=14992"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/categories?post=14992"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/tags?post=14992"},{"taxonomy":"pillar","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/pillar?post=14992"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}