The current state of technology enables the development of question-answering systems capable of conducting fluent, multi-turn conversations (chatbots). Frameworks like LangChain facilitate the creation of such systems by grounding responses in a custom knowledge base, typically stored in a vector database. Large Language Models (LLMs) then generate responses based on relevant documents retrieved from the knowledge base. While grounding responses in a knowledge base can enhance accuracy, hallucinations and factual errors may still occur.

 

These issues can be mitigated through human oversight and validation, or by focusing on efficient retrieval of relevant sources and omitting generative responses. Dedicated platforms like Microsoft Azure accelerate the development and deployment of conversational chatbots, although they often need to be complemented with open-source libraries and tools.

 

The much hyped Agentic AI promises full autonomous AI agents that are able to achieve goals using advanced reasoning and dynamically discovering and using tools like APIs, code execution, information retrieval and browser use.

 

Frameworks like LangGraph allow to build custom agents by orchestrating these components in a flexible manner. However, reliability is a core limitation of AI agents as they have difficulties with complex planning and decision making. As they struggle beyond a certain scope, it is advised to keep AI agents narrowly focused on specialized tasks. An example of such a task is converting a natural language search request into a structured database query and executing it. In most cases, human oversight and validation remain necessary to ensure reliability.

 

Vendors are actively advancing the field, though some engage in “agent washing,” overstating the agentic capabilities of their offerings. Nonetheless, the technology is on a trajectory that points toward steadily increasing autonomy and reliability. Most experts anticipate that meaningful maturity for practical, reliable use may emerge within the next 3–5 years, placing it in the medium‑term rather than the immediate short‑term horizon.

AI applied to project management has reached a maturity level where it delivers tangible benefits across the project lifecycle. It should no longer be seen as experimental, but as a set of practical assistants that can already be integrated into day-to-day work.

• Meeting assistance: Tools such as Sembly AI (1) automatically record and transcribe multilingual meetings (Dutch/French/English), generate clear summaries and identify action items. This ensures that decisions are accurately captured and follow-ups are not lost, even when several languages are spoken interchangeably. In practice, project managers spend less time on note-taking and more time on facilitating decisions.
  (1) Sembly does offer an on-premise solution, but only from 1.000 users upwards. For sensitive business discussions, we expect MS Copilot to eventually support multilingual use, providing us with a secure alternative.
• Effort and cost estimation: Specialized solutions like ScopeMaster can analyse detailed user stories to identify functional components and translate them into recognized sizing methods (e.g. Function Points). Based on these results, effort and cost ranges can be generated. This makes estimation faster, more consistent and easier to validate against historical data, while avoiding the reliance on manual spreadsheets or intuition.
• Management product generation: AI services embedded in tools such as Microsoft 365 Copilot can draft sections of project documents (for example a Project Initiation Document or status report) directly from project metadata and meeting transcripts. Project managers can then focus on validation and decision-making instead of repetitive writing.

Taken together, these examples show that AI is already able to act as an assistant (or “co-pilot“) for project managers, reducing administrative workload, increasing consistency across teams and raising the overall quality of project governance.

 

AI is making meaningful progress in the domain of software testing, supporting activities across all subdomains of the testing process. While many tools claim to operate at an advanced level, using terms like “self-healing,” “autonomous agents,” or “intelligent test orchestration”, most function at a lower maturity level than advertised. In practice, they offer valuable assistance but still rely heavily on human prompts, oversight, and validation.

• Test planning, analysis & design: Custom reusable prompts (e.g., customGPTs on ChatGPT, Gems on Gemini, Artifacts on Claude) and AI agents can generate strategies, risk maps, and test cases based on requirements or working systems, and can identify redundant or low-quality artefacts. However, these outputs still require careful review and human interpretation.
• Test implementation & automation: Tools such as Cursor, LLMs integrated with MCPs, and AI-enabled testing platforms/libraries (e.g., TestCraft, Testers.ai) assist in generating test scripts, test data, and even exploratory flows. These tools increase speed and consistency, but human oversight remains essential to refine results, ensure accuracy, and maintain test assets.
• Test execution & reporting: AI-enhanced platforms/libraries (e.g., Testers.ai, KushoAI) support log interpretation, failure clustering, and predictive reruns, often via natural language interfaces. Reporting is becoming more automated, with dashboards summarising results and tracking progress against exit criteria. However, genuine autonomy, where AI drives test flows and adapts dynamically, is still emerging.

Overall, AI helps reduce manual effort and improve test coverage, but true AI-human collaboration remains limited. Vendors are progressing steadily, but current tools still require strong human involvement to be effective.

Within software development, AI has quickly moved from being an experiment to becoming a practical accelerator. By supporting tasks such as coding, testing, and updating existing systems, it helps teams deliver features faster, reduce repetitive effort, and improve overall software quality. For development teams, AI can already be treated as a trusted partner that integrates smoothly into daily work.

• • New Feature development: Tools such as GitHub Copilot or JetBrains AI can suggest options or approaches as developers write. This speeds up delivery of new features and ensures greater consistency, while still leaving creative and critical decisions to the human actor.
  • Updating existing systems: Services built into modern development environments (like IntelliJ or Visual Studio Code) can highlight outdated elements and recommend improvements. This reduces the effort needed to keep systems up-to-date, though complex upgrades still require significant human oversight.
  • Testing support: Solutions such as CodiumAI or GitHub Copilot can propose test cases automatically. This helps teams spot issues earlier and strengthen the reliability of software without manually writing every test from zero.
  • Security checks: While less advanced than other uses, tools like Snyk Code can point to potential vulnerabilities. This provides an additional safeguard, but expert review remains essential to ensure compliance and robust protection.

Taken together, these examples show that AI is beginning to act as a reliable assistant for software teams: it reduces repetitive and predictable work, helps deliver results more quickly, and contributes to higher quality, while human expertise continues to guide design, security, and final decisions.

Overall, AI technologies within the realm of office productivity have attained a maturity that enables iterative collaboration between users and AI. This advancement facilitates seamless integration into practical use cases, whether through generalist tools embedded into mainstream software suite like Microsoft Office or Google Workspace, or through specialist solutions targeting specific areas like text creation, data analysis or presentation design.

The incorporation of these tools into workplace environments significantly enhances both contextual relevance—by leveraging enterprise data sources such as emails, files, and collaborative platforms—and security protocols for managing proprietary and sensitive information, like Compliance Framework for Data Residency & Processing or Enterprise Data Protection Agreement.

While AI solutions now support robust human–AI collaboration, their output quality still varies across subdomains, which can be categorized into three tiers:

• High-quality outputs: These allow for direct adoption in daily workflows, with real-world value observed in Document Creation and Editing, Content Analysis, Synthesis and Insights, Enterprise Information Retrieval and Search, and Meeting Assistance. These areas deliver clearly measurable ROI in terms of time savings and quality improvements, though challenges such as hallucinations remain.
• Moderate-quality outputs: Tools in this category are beneficial for routine tasks but require additional user intervention to achieve professional-grade results. Presentation Design and Email Management (beyond basic thread summaries) fall under this tier, where ROI is less distinct, although recent progress is promising.
• Low-quality outputs: In these domains, outputs often need considerable user oversight. Data Entry & Analysis, and Calendar Management provide useful functionalities but are more prone to errors and may generate inadequate results requiring frequent correction.

In the latter two tiers, specialist tools generally yield more reliable outcomes than generalist solutions like Microsoft Copilot and Google Gemini, although the quality gap is gradually narrowing as of late.

Perhaps more so than with any other domain with clearly defined tool purposes, extracting value from AI Tools in bureautica will require an ability to translate their very generic functionalities into practical Use Cases that enhance real work processes. Assisting end-users though this appropriation process should be a key consideration when deploying these tools.

At its current stage of maturity, AI is positioned as a powerful creative accelerator for communication teams, rather than as a replacement. For copywriting and translation, it proves effective in producing drafts and translating documents, although with a fidelity that can still be improved. Similarly, for image and video generation, AI helps overcome creative blocks by quickly generating personalized visuals and short clips, thereby democratizing content creation for marketing or training purposes.

However, human oversight remains essential where reliability and context are critical. In the field of copywriting and translation, current AI agents like ChatGPT, MS Copilot, Anthropic Claude and Gemini tend to provide soulless, overly standardized texts. Guaranteeing semantic, contextual and factual accuracy, along with an understanding of cultural nuances, remains a notable weakness. For images and videos, noticeable aberrations (six fingers, three feet) from MidJourney, a lack of brand consistency (logos, characters), and ethical and legal risks (copyright, deepfakes) demand rigorous validation. Human expertise is therefore essential to be the final guarantor of the message, visual identity and compliance.

In conclusion, generative AI is transitioning from a collection of distinct tools to an integrated creative partner. But to leverage it effectively, a targeted and nuanced approach is required:

• Copywriting & translation: adopt these tools to gain efficiency in producing “first drafts” and purely repetitive tasks like writing test protocols, while making systematic human validation a requirement before any publication.
• Image generation: use these technologies for illustrating concepts and replacing generic stock photos, while establishing clear guidelines to ensure brand identity is respected.
• Video generation: experiment with caution, focusing on low-stakes content (internal communications, mockups) where the technology’s current imperfections are acceptable.

The goal is not to replace skills, but to augment human creativity. Strategic judgment remains a uniquely human skill.