The study’s goal was to interpret AI’s role in corporate budgeting, examining how well such technology performs when making financial decisions. Ultimately, it’s an investigation into whether AI’s financial decisions align with a company’s long-term strategies and how its decisions compare to human management.

The researchers, Kristof Stouthuysen, Professor of Management Accounting and Digital Finance at Vlerick Business School, and PhD researcher, Emma Willems, studied tactical and strategic budgeting approaches.

Tactical budgeting is about quick, responsive decisions, referring to short-term, data-driven financial decisions. These are aimed at improving immediate performance, like making adjustments to spending based on market trends.

Strategic budgeting typically involves a more comprehensive approach that focuses on future planning, aligning various resources with a business’s vision.

According to the research, AI is superior when performing tactical budgeting processes like cost management and resource allocation. However, the need for human insight remains important to ensure accurate and strategic financial planning over the long term.

The controlled experiment was achieved by running a management simulation where experienced managers were asked to allocate budgets for a hypothetical automotive parts company. Stouthuysen and Willems then compared these human-made decisions to those produced by an AI algorithm using the same financial data.

The results concluded that AI was superior in optimising budgets when a company’s strategic financial planning was clearly defined. However, AI struggled to make budgeting decisions when key performance indicators (KPIs) did not align with the company’s financial goals.

Stouthuysen and Willems work on the study emphasised the importance of a collaboration between humans and AI. “As AI continues to evolve, companies that use its strengths in tactical budgeting while maintaining human oversight in strategic planning will gain a competitive edge. The key is knowing where AI should lead and where human intuition remains indispensable.”

According to the study, AI can theoretically take over from humans when it comes to tactical budgeting, providing more precise and efficient outcomes. Stouthuysen and Willems believe companies need to define their strategic priorities clearly and implement AI for tactical budget-making decisions to maximise financial performances and achieve sustainable growth.

The findings challenge the widespread misconception that AI can completely substitute the need for humans in budgeting. Instead, this research emphasises the importance of taking a balanced approach, utilising both AI and humans, assigning tasks to silicon or human processes according to their proven abilities.

(Image source: “Payday” by 401(K) 2013 is licensed under CC BY-SA 2.0.)

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In an industry like pharmaceutical, and its often complex supply chains, companies have to be aware of the mass of changing rules and regulations emanating from different legislatures at local and federal levels. It’s no surprise, therefore, that it’s in regulated supply chain compliance that AI can be hugely beneficial. Given that AIs excel at reading and parsing documentation and images, service providers like Lighthouse AI use the technology in its different forms to comb through existing and new documentation that governs the industry.

The company’s latest suite, Lighthouse AI for Review uses the variations on machine learning of predictive and generative AI, image recognition and OCR, plus linguistic modelling, to handle use cases in large volume, time-sensitive settings.

Predictive AI is used for classification of documents and generative AI helps with the review process for better, more defensible, downstream results. The company claims that the linguistic modelling element of the suite refines the platform’s accuracy to levels normally “beyond AI’s capabilities.”

eDiscovery – the broad term

Lighthouse AI is currently six years old, and has analysed billions of documents since 2019, but predictive AI remains important to the software, despite – it might be said – generative AI grabbing most of the headlines in the last 18 months. Fernando Delgado, Director of AI and Analytics at Lighthouse, said, “While much attention has been rightly paid to the impact of GenAI recently, the power and relevancy of predictive AI cannot be overlooked. They do different things, and there is often real value in combining them to handle different elements in the same workflow.”

Given that the blanket term ‘the pharmaceutical industry’ includes concerns as disparate as medical technology, drug research, and production, right through to dispensing stores, the compliance requirements for an individual company in the sector can be wildly varied. “Rather than a one-size-fits-all approach, we’ve been able to shape the technology to fit our unique needs – turning our ideas into real, impactful solutions,” says Christian Mahoney, Counsel at Cleary Gottlieb Steen & Hamilton.

Lighthouse AI for Review includes use cases including AI for Responsive Review, AI for Privilege Review, AI for Privilege Analysis, and AI for PII/PHI/PCI Identification. The Lighthouse AI claims that its users see an up to 40% reduction in the volume of classification and summary documents with the AI for Responsive Review feature, with less training required by the LLM before it begins to create ROI.
AI Privilege for Review is also “60% more accurate than keyword-based models,” Lighthouse AI says.

AI’s acuity with visual data is handled by AI for Image Analysis uses GenAI to analyse images and, for example, produce text descriptions of media, presenting results using the interface users interact with for other tasks.

Lighthouse’s AI for PII/PHI/PCI Identification automates the mapping of relationships between entities, and can reduce the need for manual reviews. “The new offerings are highly differentiated and designed to provide the most impact for the volume, velocity, and complexity of eDiscovery,” said Lighthouse CEO, Ron Markezich.

(Image source: “Basel – Roche Building 1” by corno.fulgur75 is licensed under CC BY 2.0.)

See also: Hugging Face calls for open-source focus in the AI Action Plan

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According to the platform’s published strategy, the company will focus on three key principles – AI Blocks, Product Power-ups, and a Digital Workforce. Its aims are to “accelerate its vision to democratise the power of software.” By making its software more accessible and usable for its users, monday.com hopes to address the challenges encountered by businesses and work forces in teams of all sizes, streamline workflows and increase productivity.

The latest AI strategies are designed to help small and medium-sized businesses (SMBs) and mid-market companies grow and adapt quickly without the need to hire more staff. According to monday.com, AI will boost process times that are typically slowed by the scale of larger enterprises and Fortune 500 companies.

In line with monday.com’s stated goal to “democratise access to AI,” the platform’s approach is to be user-friendly, allowing any users regardless of technical knowledge to create, customise, and use AI tools in their workflows.

Daniel Lereya, Chief Product and Technology Officer at monday.com spoke about the company’s approach to making software accessible to everyone. “By embedding intelligence into the products our customers already know, use, and love, AI will accelerate our mission to democratise the power of software,” he said.

“With a majority of our customers in non-tech industries, they’re looking to us to lead them through the AI transformation. Our productisation of AI ensures that intelligence serves our customers and not the other way around.”

Of the three principles ‘AI Blocks’ are customisable AI tools that can be added to existing workflows, regardless of technical knowledge. The AI Blocks feature capabilities like “Categorise” and “Extract,’ letting users analyse data, and recognise patterns in a few clicks.

The second principle, ‘Product Power-ups,’ refers to the integration of AI features directly into monday.com’s existing suite of products. This is designed to help the company’s customers address challenges in areas like CRM data automation, resource management, predictive risk management, and real-time service ticket resolution. The features could help teams make quicker, more informed decisions, streamline workflows and increase efficiency.

Finally, ‘Digital Workforce’ is a collection of AI agents that operate to support users and customers. Digital Workforce is can handle specific tasks that are automatable, including project risk analysis, the identification of ongoing customer service issues, and helping move delayed sales deals forward.

monday.com is planning the launch of ‘monday Expert’ in March, its first AI agent that’s designed to support the onboarding of new users, and perform some tasks on behalf of users.

The company claims its AI capabilities have had a notable effect, with reports of monday.com users performing around 10 million AI-driven actions in 2024. The numbers of AI use instances nearly tripled each quarter of 2024, indicating a rapid rise in the use of AI in the CRM platform.

(Image: “Monday – Back to work!” by tinto is licensed under CC BY 2.0.)

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The application of preemptive observability in pre-production may be more important as AI code generators become more common , the company claims. For instance, a 2023 Stanford University study revealed that developers using AI coding assistants were more likely to introduce bugs to their code. Despite this, major companies like Google are increasing their reliance on AI-generated code, with over 25% of the company’s new code being AI-created.

Nir Shafrir, CEO and Co-founder of Digma, commented on the growing resources that are being dedicated to ensuring systems perform well, saying, “We’re seeing a lot of effort invested in assuring optimal system performance, but many issues are still being discovered in complex code bases late in production.”

“Beyond this, scaling has often remained a rough estimation in organisations anticipating growth, and many are hitting barriers in technology growth that arise precisely during periods of significant organisational expansion. It means that engineering teams may spend between 20-40% of their time addressing issues discovered late in production environments, with some organisations spending up to 50% of engineering resources on fixing production problems.”

Preemptive observability is expected to become a key factor helping companies gain competitive advantage. It has several potential benefits for AI-generated code, including speed increases and improvements to the reliability of human-written code. According to Digma, preemptive observability helps ensure manually written code is more trustworthy, and reduces risk in the final product.

As well as tackling bugs introduced by AI code generation, Digma’s preemptive observability analysis engine has been designed to combat common, long-established issues companies may have experienced with human-made code, which may result in service level agreement (SLA) violations and performance issues. For high transactional establishments, like retail, fintech, and e-commerce, this technology could become valuable.

Digma’s algorithm has been designed to use pattern matching and anomaly detection techniques to analyse data and find specific behaviours or issues. It is capable of predicting what an application’s response times and resource usage should be, identifying possible issues before they can cause any noticeable damage. Digma specifically detects the part of the code that is causing an issue by analysing tracing data.

Preemptive observability analysis prevents problems rather than dealing with the aftermath of the issues. Teams can monitor holistically, and address potential issues in areas that are frequently ignored once in production.

Roni Dover, CTO and Co-founder of Digma, highlighted what differentiates Digma’s preemptive observability analysis engine from others: “By understanding runtime behaviour and suggesting fixes for performance issues, scaling problems, and team conflicts, we’re helping enterprises prevent problems and reduce risks proactively rather than putting out fires in production.”

Application performance monitoring (APM) tools are used to identify service issues, monitor production statuses, and highlight SLA errors. APMs are practical for sending alerts when services fail or slow during production. But unlike preemptive observability, APMs are limited in non-production settings, and can’t provide analysis of problems’ sources.

By identifying performance and scaling issues early on in the production process, even when data volumes are low, preemptive observability helps prevent major problems and reduce cloud costs.

Digma recently completed a successful $6 million seed funding round, indicating a growing confidence in the technology.

Image source: “Till Bechtolsheimer’s – Alfa Romeo Giulia Sprint GT No.40 – 2013 Donington Historic Festival” by Motorsport in Pictures is licensed under CC BY-NC-SA 2.0.

See also: Microsoft and OpenAI probe alleged data theft by DeepSeek

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