AI has developed from a buzzword into a disruptive force, influencing how organisations predict, plan, and make critical decisions. As the adoption and integration of AI tools speed up, forward-thinking leaders need to embed AI into everyday workflows.
On this episode of the Reshaping Finance podcast, Didem Un Ates, CEO and founder of Lotus AI, shared insights into the real-world impact of AI-driven forecasting and the cultural and organisational shifts required for genuine impact.
Un Ates' perspective offers a road map for finance professionals who want to lead in a landscape where delaying the AI journey is not an option. Critical success factors for AI adoption include responsible AI talent transformation, change management, empowering employees to redefine their roles with AI, and strong executive sponsorship, along with clear success metrics.
The evolution of forecasting: Going from Excel to intelligent CRMs
Just as Excel once revolutionised finance in the 1980s, AI is now ‘a bit like that’, Un Ates said.
‘You can’t say “I’m not going to use AI” in this age because it would be like trying to do your modelling or forecasting without Excel … or trying to do business in today’s world without using the internet,’ Un Ates explained.
The jump from spreadsheets to advanced planning tools, and now to intelligent customer relationship management (CRM) tools, marks a new era. According to Un Ates, ‘there’s a huge difference between clients who are on intelligent CRMs versus legacy ones’, with some organisations seeing forecasting accuracy improve by 20% to 50% and errors reduced by half.
Where traditional forecasting relied on gut feel, linear models, and manual updates, AI-powered tools integrate structured and unstructured data, handle thousands of variables, and enable continuous real-time recalibration.
The human factor remains crucial
Bringing everyone in the organisation on board when it comes to AI transformation is essential, and ‘continuous training and open communication about the opportunity cost of inaction’, are critical steps according to Un Ates. ‘Statistically, today, the people who resist AI training, and who do not embrace AI as an opportunity, are unfortunately and typically, the first ones who are asked to leave,’ she said.
‘Talent transformation is the number one critical success factor, giving people the opportunity to proactively define their new, augmented jobs with AI,’ according to Un Ates. She also stressed that AI will not replace teams or humans but rather repetitive tasks. ‘The real opportunity is to add bigger, better value with human ingenuity,’ she said.
Prioritising use cases and defining success
Prioritising AI use cases in finance requires a strategic and clear-eyed approach. ‘We always start with understanding what’s already going on … We conduct an AI maturity assessment,’ she said.
An assessment leads to clear understanding of the team’s current practices, available data, training needs, and mindset. Once the assessment is done, the road map for AI-driven forecasting is built.
Un Ates outlined a framework for prioritising AI use cases, starting with maturity assessments, opportunity scans, and workshops, then ranking initiatives based on impact and feasibility to avoid wasted investment of time and resources.
Equally important is defining what success looks like from the beginning of the process. ‘We are not doing AI in forecasting for the sake of AI. It’s about what decisions, what processes do we want to improve, which insights and actions we want to make better.’
Clarity from the start prevents wasted investments in new tools that don’t deliver.
Breaking down misconceptions and implementing guardrails
Common misconceptions among finance leaders include believing AI requires perfect data or viewing AI as either an oracle or useless. Un Ates emphasised the importance of having a holistic view of AI transformation, embracing continuous learning and focusing on value creation.
As adoption scales, responsible AI becomes critical. ‘The risks of this technology require us to be very careful,’ she said. Proactive governance and guardrails are non-negotiables.
‘Regardless of where your country or region is on AI regulation, you’d better put the guardrails in place. Otherwise, just like being unprepared for cybersecurity, it will come back and hurt you,’ Un Ates said.
Adaptability and responsible leadership are skills for the future
For finance professionals, Un Ates identified adaptability and learning agility as top priorities. ‘Those who learn and embrace AI are actually going to not only protect their jobs, but they’re probably going to progress faster and be promoted’, adding that ‘data storytelling, human judgment, leadership … and responsible AI, the governance and compliance, is mission critical for success’.
Un Ates’s approach is both pragmatic and optimistic. Start with honest assessments, involve the whole organisation, define success metrics, and invest in both technology and people. ‘Everybody in the organisation needs to know, this is it for the long term. It’s not just a finance function using AI for forecasting.’
As AI becomes foundational infrastructure, the leaders who blend technical skills with empathy will shape a successful future for their organisations.
Listen to the full episode of the Reshaping Finance podcast to dive deeper into AI-driven forecasting and hear the full conversation.
Additional resources:
The Next Frontier of Finance: Taking AI from Theory to Implementation
Essential AI resources for practical use
Re-defining finance for a sustainable world