In recent discussions with health and scientific research teams, the importance of Artificial Intelligence has gained increasing attention in the day-to-day work of professionals involved with projects and products. These are environments where quality is critical and potential failures can have consequences that go far beyond financial losses. Below, see how this transformation is taking place in practice:
For a long time, corporate operations were managed primarily by looking at the past. Today, the practical use of AI in project and quality management is changing this paradigm. Instead of updating schedules after delays have already occurred and auditing failures after the fact, leaders can now anticipate deviations before they affect results. Much of a manager’s work traditionally consisted of reacting to events as they happened.
Artificial Intelligence is changing this paradigm. For the first time, organizations are using their own data to anticipate risks, forecast scenarios, automate repetitive activities, and support strategic decisions before problems occur. More than a technological innovation, this represents a fundamental change in how companies are managed.
Imagine a project manager responsible for twenty initiatives at the same time. Without AI, the manager becomes aware of a delay when a weekly report arrives. With AI, the system identifies patterns indicating a high risk of delay several days in advance, recommends reallocating resources, and highlights which deliverables require immediate attention.
The Impact and Key Figures of AI in Project and Operations Management
- up to 40% less time spent on administrative tasks
- around 30% greater predictability in schedules and costs
- up to 60% lower risk of failures and rework
- up to 80% of project management activities supported by AI by 2030
- up to 35% greater operational efficiency in companies investing in automation
From Analyzing the Past to Data-Driven Predictive Decision-Making
Project and quality management systems generate thousands of pieces of information every day, including documents, schedules, indicators, audit records, nonconformities, action plans, and operational histories.
For many years, much of this valuable information remained stored in spreadsheets, reports, or isolated systems. Today, Artificial Intelligence is transforming this volume of information into actionable knowledge.
Machine Learning capabilities learn from previous projects to forecast timelines, costs, and risks. Generative Artificial Intelligence produces reports, summarizes documents, and answers queries in natural language. Intelligent Document Processing (IDP) automatically extracts information from contracts, forms, certificates, and PDFs, reducing manual effort and improving data reliability. At the same time, analytical models identify trends and anticipate deviations before they affect organizational performance.
The result is a significant shift: managers spend less time producing information and more time using it to make faster and more confident decisions.
The Biggest Challenge of AI in Companies Is Not Technology
Although Artificial Intelligence is at the center of corporate discussions, international experience shows that technology alone does not guarantee results.
Studies from organizations such as Gartner, McKinsey, RAND, and MIT indicate that a large share of AI initiatives fail to move beyond the pilot stage. Factors identified include a lack of financial return, discontinued projects, and challenges related to data quality.
The main cause, however, is not the technology itself. It is the lack of integration between AI models and real business processes. When Artificial Intelligence operates separately from day-to-day operations, it answers questions. When it is integrated into enterprise systems, it supports decisions. This distinction completely changes the value AI can generate for the business.
The Predictive Era: How AI Works in Project Management in Practice
In practice, AI supports the entire lifecycle of corporate initiatives. From the initial assessment of a new request, algorithms can classify priorities, estimate effort based on actual historical data, mitigate risks, and monitor delivery progress in real time.
Predictability of Schedules and Costs, and Risk Mitigation
Models trained on previous projects help reduce the traditional optimism bias found in estimates and improve schedule and cost predictability. During execution, algorithms identify patterns that typically precede delays, issue warnings about budget risks, and recommend actions before problems materialize.
Intelligent Portfolio Management and Team Allocation
Another significant transformation is taking place in portfolio management. Instead of analyzing projects individually, AI can balance teams according to available capacity, simulate allocation scenarios, prioritize investments based on strategic value, and provide natural-language answers to questions such as: “Which projects are at the highest risk this month?”
With AI implemented in project management, manual routines such as creating reports, dashboards, and status reports can be automated, freeing managers to focus strategically on resolving bottlenecks.
It is therefore no coincidence that Gartner projects that by 2030, around 80% of project management activities will be directly supported by Artificial Intelligence.
From Corrective Quality to Preventive Quality
In quality management, the transformation follows the same principle. Instead of taking action only after a nonconformity has been recorded, Artificial Intelligence makes it possible to anticipate deviations and reduce their recurrence.
Computer vision systems identify defects quickly and consistently. Intelligent Document Processing tools automatically organize certificates, forms, and technical documents while maintaining version control, traceability, and compliance.
When it comes to managing nonconformities, AI can classify occurrences, identify probable causes, correlate similar events, recommend corrective actions based on previous experience, and monitor their effectiveness over time. As a result, quality management can focus on addressing the root causes of problems rather than simply responding to their effects.
The same applies to audits, risk assessments, and continuous improvement programs, where predictive indicators enable organizations to act before deviations compromise processes, customers, or regulatory requirements.
Corporate Governance: Why AI Requires ISO 42001
As Artificial Intelligence becomes increasingly embedded in corporate decision-making, the need for governance also grows.
International standards such as ISO 42001 represent a new stage by establishing requirements for the responsible, transparent, and auditable use of AI systems, complementing established standards and regulations such as ISO 9001, IATF 16949, ISO 13485, ISO 17025, and FDA 21 CFR Part 11.
Beyond meeting regulatory requirements, organizations can build greater trust in how they use their own data.
Value-Driven Implementation: Integrating SoftExpert Suite AI into Day-to-Day Business Operations
The maturity of Artificial Intelligence does not depend solely on selecting the right technology platform. It depends on the ability to integrate processes, people, data, and governance.
This is precisely the approach adopted by SBMTEC Digital Transformation. Specializing in the implementation of enterprise solutions, the company supports organizations from process assessment through configuration, training, ongoing support, and the continuous evolution of their platforms. As a SoftExpert partner, SBMTEC implements integrated environments in which projects, quality, risks, documents, and indicators share the same data foundation, enhanced by Artificial Intelligence, hyperautomation, and predictive analytics capabilities.
Among the projects already implemented by SBMTEC, two significant cases stand out. At Comau, the result was a reduction of up to 60% in the risk of failures. At Pharmascience, the implemented technology enabled the company to achieve compliance with RDC 17 and obtain complete traceability of its products.
This integration makes it possible to deploy capabilities such as Copilot AI, Intelligent Document Processing (IDP), Data Lab, and intelligent automation solutions, transforming repetitive activities into digital, auditable, and scalable processes.
The results observed in implemented projects further reinforce this potential. Key outcomes include savings of up to 80% in the time required to analyze records, as well as significant compliance improvements in highly regulated industries such as pharmaceuticals. In addition, market studies indicate that organizations investing in automation can achieve productivity gains of up to 30% and improvements of up to 35% in operational efficiency.
Conclusion: The Transformation of AI in Project Management and Decision-Making
Artificial Intelligence does not replace managers’ experience or eliminate the importance of human expertise.
Its greatest impact lies in reducing operational effort, expanding teams’ analytical capabilities, and enabling faster, better-informed, and more predictable decisions.
The journey begins by identifying the processes where AI can generate the greatest value, progresses through rapidly implemented pilot projects, and gradually expands across the organization with governance and continuous improvement.
Companies that understand this shift will be better prepared to compete in an environment where speed, quality, and decision-making capabilities are becoming increasingly decisive factors.
In this context, embedding AI into project management and day-to-day operations is no longer merely a technological investment. It becomes a strategic pillar in which achieving tangible results depends on the right integration of tools, data, and governance. The future of management, therefore, will not be defined by who has the most data, but by who knows how to transform it into faster, smarter, and more reliable decisions.
