As AI adoption rises across industries and business use cases, one of the most important implementations remains business intelligence. I’m as enthusiastic as the next person about the benefits that AI can bring to business intelligence, having seen first-hand how it shortens time to insights and makes decision-making more accessible and accurate.
Injecting AI into BI can unlock many capabilities, from expanding the number of data sources to speeding up data preprocessing and enabling non-data science experts to query and explore data. But as exciting as these promises are, it’s also important to stay grounded in reality.
In conversations with people across the enterprise ecosystem, I’ve heard some less-substantiated viewpoints. There are persistent myths about the impact that AI can have on business intelligence, including that it will soon be possible to fully automate business strategy, data quality obstacles will evaporate, and we won’t need human data science experts. Likewise, the viewpoint that any AI answer engine can be used to connect to any database is still going strong, even though I’m still doing my best to refute it.
In this article, I take a close and honest look at four primary myths around AI-powered decision intelligence, and explain what is and what is not realistic.
Myth no. 1: Business strategy will be automated
The biggest myths about AI always centre around its power. There’s a belief that once LLMs become powerful enough, fast enough, or are trained on enough enterprise data, they’ll be able to formulate and execute new strategies on a fully automated basis.
The idea is that AI decision-making will be good enough not just to rival human-led strategising, but to push it out of the picture entirely in favor of an AI-version of a Magic 8 Ball.
As someone who works with AI all the time, I can tell you that nothing could be further from the truth. If you know what you want to achieve, where you are going, and how to get there, AI will help you get there faster and more efficiently. But if you leave it to AI to steer the truck, you’ll end up in a dead end.
AI can suggest possibilities, calculate risks, model contingencies, and give you all you need to make a good strategic decision, but it should not be the one to do the deciding. AI models are getting better and better at incorporating business logic and accessing proprietary resources to inform their answers, but they will always lack the nuanced, big-picture understanding that human leadership has.
Indeed, business strategy isn’t just a matter of formulae and inputs. Relationships, lived experience, and human interactions all play important roles. Only people can bring the creativity, context, and sheer intuition that are needed for successful strategic decision-making.
Myth no. 2: All that’s needed is to plug in AI
Too many people still think that they can just plug an LLM into their data environment and hit “go.” They don’t realise the work needed for the AI engine to understand its context and know how to work within the existing analytics systems.
Many definitions live outside of databases, in metric layers or just tribal knowledge, so unless data management and governance is part of the picture, plug-and-play AI can’t reach them.
It’s never enough to simply spread AI on top of the underlying data architecture. You need to teach it the meaning behind your data, including definitions, business rules, and semantics, so that it can deliver the answers that executives need.
Otherwise, your AI is liable to make up wrong answers that seem extremely convincing. Connecting AI with data without investing in the complex backend work of data intake and prep workflows, decision-making guardrails, permissions, approved metrics and log sources is a recipe for disaster. You’ll end up with AI that is expensive, slow, unreliable, and inaccurate.
Myth no. 3: AI can handle any data, no matter the quality
Believing that AI can cope with any data in any state can have serious consequences. AI can make analytics more accessible, but it can’t replace clean systems, consistent metrics, and operating discipline.
In fact, one small mistake in a single training dataset can be amplified when the information is copied across the system.
Without clear ownership, priorities, and definitions, AI just accelerates confusion. If you feed in data that is poorly-defined, inconsistent, or erratic, you’ll get outputs that are unreliable and inaccurate. For example, if various departments at your company use different definitions of what a given KPI means, AI will generate multiple plausible versions and there’ll be no way to determine which one is correct.
Access control, compliance, lineage, auditability, and risk management remain core enterprise requirements. You need to implement robust rules around data sources, management, ownership, and governance, otherwise even the best AI engine will fail to deliver.
Myth no. 4: Data engineers and analysts will become obsolete
Finally, the myth that everyone likes to tell about AI: it’s coming for your job. AI solutions are becoming more LOB-friendly, with NLP-powered interfaces that support natural language queries and smart systems that automatically suggest the best visualizations. But they can’t replace a data science team.
The cliche that “AI won’t replace you; the worker who uses AI will replace you” is true. Data teams will no longer need to carry out repetitive tasks like manually cleaning data, surfacing patterns via coded queries, and detecting anomalies. But they are still needed for deeper-value tasks like data-driven story telling, explaining data insights and empowering growing teams of citizen analysts.
People inherently trust people over machines, so stakeholders will still look to data science experts to authenticate and validate the AI-generated guidance they receive. Additionally, complex data input processes mean that only data specialists know how to collect and prepare the fresh signals that AI platforms need.
Don’t fall into the AI myth trap
Integrating AI into business intelligence processes can bring a whole slew of benefits for every enterprise in every industry. But it’s crucial to be realistic about its potential. By being aware of these myths, executives can create effective AI-powered analytics systems that deliver the fast, reliable insights they need for swift and solid decision-making.