We are firmly in the 4th Industrial Revolution. Data, business intelligence (BI), and artificial intelligence are transforming businesses at an unprecedented rate, but only those with the right fundamentals in place will be able to enjoy the competitive advantages these technologies can offer.
Michelle Moody, Managing Director of Technical Consulting for Data and AI at Protiviti, is here to answer six core questions around the future of data, AI, and BI. Before joining Robert Half’s sister company, Michelle was an Executive Partner for Cognitive Business Decision Support at IBM and spent almost a decade at Capgemini UK, reaching a position as Vice President of Insights & Data.
Michelle shares her insights on how mindsets, skillsets, and strategies will need to change, based on decades in the industry and real-world client experience.
1) How will AI-driven automation redefine the role of the analyst as routine reporting gives way to predictive and prescriptive insights?
The adoption of AI automation won’t herald a complete and total change in analytic fundamentals, according to Michelle. She believes the focus on maintaining quality data and using that to gain commercial insights will remain the same.
“You're capturing that data within the underlying platform and capturing it over time. You're looking at trends, using them to say, ‘This always happens. So, therefore, I'm going to do this to address it,’ and you automate that. And it's a use case that you continue to automate, which is less about AI and more about automation and good data,” she says.
“I think where it's going to go is this: you’ll start to see some of these LLMs (Large Language Models) mature to a point where the dashboarding is within the actual LLM,” she says. “Going into the future, you're going to find that reporting platforms will evolve to use generative AI rather than relying on canned BI reports. People will be more inclined to just use an AI bot than to go to a dashboard.”
2) Can conversational analytics genuinely democratise data, and what new dependencies on model accuracy and governance should be considered?
Conversational analytics have already pushed many businesses into an environment in which data is democratised. But Michelle suggests that what is required is governed democratisation, where the context of the data is understood, and insights are provided in line with the role of the individual.
From a governance standpoint, business functions will need to ensure they understand the datasets, context of the data, and the sensitivity of the data, so that the insights are fair, responsible, and ethical. Moreover, she believes that critical thinking skills regarding AI outputs and a foundational knowledge of data will be essential for gaining accurate, valuable insights.
“There's a huge part of the workforce that is now almost dependent on AI chatbots. The question is, are they critically thinking about what is coming out of the interface? Is it fit for purpose? Does it make sense? Is the data up to date? I think a lot of this is going to come down to people having better critical thinking skills to make sure that the insights contain the ‘right’ answers,” she says.
3) What competitive advantage will organisations gain from shifting BI from static dashboards to Generative BI — real-time, proactive intelligence?
There are advantages to real-time generative business intelligence, and it can be critical for certain businesses. In some cases, organisations could see a reduction in the cost of maintaining, hosting, or perhaps migrating traditional BI dashboards. Equally, it facilitates faster decision-making and a more proactive approach, especially when businesses can democratise data to teams with the requisite data literacy skills.
Generative BI will shift the strategic focus to higher-value skills, such as data governance, cybersecurity, critical thinking, and domain expertise, which can help frame prompts and questions for the most accurate and effective output.
4) How will generative AI reshape the design of BI tools, from automated narratives to scenario modelling?
Michelle predicts that, rather than AI changing BI tools, it could render them obsolete over time. She believes LLMs will mature to a point where users can request custom dashboard renderings on demand via generative BI.
“AI requires a very different skillset than developing traditional dashboards, which is basically mapping structured data fields to UI. A lot of those ‘front-end’ dashboard jobs will slowly fade away—I’m not saying it'll be overnight—but there'll be less reliance on them because the AI models are going to be much quicker, more up to date, and tailored to specific questions asked by the user. It's going to be a different way of looking at it,” she says. “You're going to be focused on adopting the new AI models that will render visuals and drill downs on the fly, developing new skills for prompting, skills for iteratively training the models as opposed to using BI tooling with canned reports.”
5) What new skills will business leaders and data teams need as interpretation, judgment, and domain expertise become more valuable?
As well as manual data preparation skills (“Because, at the end of the day, the data forms the foundation of the answers that you’re going to automate or get through AI”), Michelle advises that a deep understanding of data, data management, data governance, and cyber security are essential skills for businesses hoping to use AI to utilise data more effectively, safely, and responsibly.
“Companies that have invested in strong data governance skills will be in a much better position to adopt AI, but in a transparent, explainable way, because they will be managing that data effectively,” she says. “It should be classified; it should be labelled; it should be owned by the business and aligned to Data/AI Policies. The lineage for it should be there, and it should be consistent and clean. These skills will be valuable as AI adoption increases across the market.”
Michelle also believes that cybersecurity and data security skills will become vital. “You'd be hard-pressed to find software tooling that isn't adding AI into the applications that organisations are using. If malware is passed into an organisation via AI, for example, using Agentic AI, then in theory that virus could be passing information to different functions and potentially to other third parties, there's a risk there.”
AI literacy skills will also be key moving forward, especially if businesses plan to democratise data, introduce LLMs, and cut headcount for efficiencies. She says, “There is a different kind of skill that people will need to learn going forward—it doesn't matter what function. If you're going to use the AI bot, then it's about getting a reasonable base level in terms of data and AI literacy skills, for everyone in the organisation, C-suite down.”
6) How should organisations balance speed and automation with the need for transparency and responsible use of AI-generated insights?
Getting data governance in place for each department of the business, then embedding policies into the lifecycle management of that data—with regular training for all teams—is the best foundation for AI and automation adoption.
“Organisations that get those foundations in place are going to be the ones that can adopt a lot more quickly, a lot more thoughtfully, and with a lot more responsibility, having that transparency, that explainability around what the AI is there to do, where it's being used, what data it's being used against,” she says.
“It's a really interesting time. We’re moving forward in the 4th Industrial Revolution now. There are some exciting things that can happen because of AI and its efficiencies, new ways of working, and new roles,” she says. “There is the opportunity for organisations to be thoughtful about putting the right foundations in place so they can adopt it in the right way.”
You can connect with Michelle on LinkedIn or visit Protiviti to learn more about how she and her team support AI and automation roll-out. For more insights into future skillsets and mindsets in tech, read the latest Shaping the future of tech report now.
