Understanding generative AI as a pedagogical innovation opinion

As enterprises expand their gen AI initiatives, they recognize that the cost of developing, deploying and operating these models can be significant as use cases scale and expand. Although the research focused on coders, observing how all employees interact with gen AI can reveal broader areas for flattening hierarchies and increasing efficiency. Hoffmann recommends cataloging tasks—like his team did—to identify which duties can shift from humans to AI. Now, it’s possible to give AI confirmed “good” data and have the AI analyze and learn from it. From there, for all new data, it can apply what it learned, flagging any outliers for manual analysis, significantly reducing the effort required by humans to do the work.

of organisations developing or planning custom AI agents, says OutSystems report

They turn to a LLM bot, which creates custom questions, quizzes them, provides corrective feedback and repeats the cycle until mastery is achieved. This happens without the involvement of the instructor, other students, textbooks or premade quiz banks. This is an example of redefinition, where AI makes possible a personalized, responsive learning experience that fundamentally alters the traditional learning process. While this new approach represents a huge opportunity to scale transformation initiatives, it also makes AI governance more important than ever.

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Helping customers achieve their business goals by addressing key strategic initiatives, OutSystems delivers production software up to 10x faster than traditional development. Recognized as a leader by analysts, IT executives, business leaders, and developers around the world, global brands trust OutSystems to tackle their impossible projects and turn their big ideas into software that moves their business, people, and the world forward. Founded in 2001, the company’s network spans more than 800,000 community members, over 500 partners, and active customers in 75+ countries across 21 industries. Like other pedagogical innovations, each potential benefit of generative AI—whether it’s improving student learning, increasing instructor efficiency or achieving other desirable outcomes—comes with a corresponding challenge.

Document these best practices and encourage wider dissemination through knowledge sharing sessions. IT leaders should consider augmentation and customizations sequentially, only moving to a more advanced approach if a simpler one doesn’t meet the required output quality. They can evaluate the different approaches not only to achieve better output quality, but also to reduce running costs — especially if the model usage will be high volume and predictable. Running costs can be mitigated by careful choice of models that balance price/performance, or even by efficiently fine-tuning a model on a specific dataset through instruction tuning or continuing pretraining.

• With the ongoing transformation of industries and business processes caused by artificial intelligence (AI), AI for leaders is not only a technological benefit but also a leadership requirement.
• Conduct monthly or quarterly reviews of gen AI costs to ensure ongoing optimization and instill a culture of accountability.
• “It shifts the conversation from a more tactical conversation about this one use case or this one risk to more of a strategic plan for dealing with the ‘AI risks’ in your organization,” Marcus says.
• They can evaluate the different approaches not only to achieve better output quality, but also to reduce running costs — especially if the model usage will be high volume and predictable.

According to Check Point’s 2025 AI security report, 1 in every 80 prompts (1.25%) sent to generative AI services from enterprise devices had a high risk of sensitive data leakage. Consider the complexity and cost implications before opting to self-host gen AI models. If an organization decides to self-host, it must ensure it can deliver opex-based pricing models or managed services for it. IT leaders should evaluate their organization’s capacity for upfront investment, ongoing maintenance and expertise before opting for self-hosting, considering that the costs and complexities can escalate — especially with larger models and high usage volumes. Model accuracy needs to be validated through a broad set of accuracy metrics such as fluency, coherence, relevancy and contextual understanding. If choosing a gen AI model delivered as an applied programming interface, remember that the same model may be offered by multiple providers.

The majority of software executives (69%), anticipate AI will introduce new, more specialized roles (e.g., oversight, governance, prompt engineering, agent architect, and agent orchestration) to accommodate the evolving role AI will play within organizations. More than three out of five respondents (63%) also feel that AI will require substantial upskilling or reskilling within existing development teams. Almost half of respondents (49 per cent) are planning to adopt AI agents for customer support to autonomously handle inquiries and support tasks, underscoring a drive to improve customer experience and scale support efficiently. The report says that with agentic AI, leaders can automate large-scale processes, create hyper-personalised digital experiences, and drive innovation with agility. Capable of enabling seamless enterprise-wide data access and upholding rigorous standards for compliance, security, and governance, agentic AI is poised to transform business processes and experiences and help organisations embrace an AI-first future.

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• OutSystems is a leading AI-powered low-code development platform, empowering IT leaders with a better way to build the software that matters most.
• Despite the attention generative artificial intelligence has received in higher education, it is often discussed solely as a technological innovation rather than a pedagogical one.
• As these AI tools evolve, they may not only make teaching more efficient but also inspire educators by alleviating administrative burdens, allowing them to focus more on creativity and innovation in their teaching.
• Finally, picture a student who, despite participating in peer instruction, still struggles with the material.
• With the help of AI in customer service and marketing, leaders will be able to personalize the experience of their clients better, building stronger relationships and increasing the rate of long-term customer retention.
• Companies can’t afford to have human power doing this, and it certainly wouldn’t be as accurate.

With the ongoing transformation of industries and business processes caused by artificial intelligence (AI), AI for leaders is not only a technological benefit but also a leadership requirement. AI has much potential to help leaders make better decisions, operations run smoothly, creativity, and not lag behind in the competition. This paper discusses the seven most important reasons why AI is essential to leaders and how leaders can prepare themselves with the essential skills by enrolling in courses like Data Science Courses and Generative AI Courses. Norton suggests CISOs develop strategic and tactical approaches to define the different types of AI tools, capture the relative risks, and balance potential pay-off in productivity and innovation. Tactical measures such as secure by design processes, IT change processes, shadow AI discovery programs or risk-based AI inventory and classification are practical ways to deal with the smaller AI tools. “Where you have more day-to-day AI — that bit of AI sitting in some product or some SaaS platform, which is growing everywhere — this might be managed through a tactical approach that identifies what elements need oversight,” Norton says.

Conventional teaching—the predominant instructional model at all levels of education—follows a one-teacher-to-many-students format with periodic testing to assess learning. In contrast, mastery teaching integrates frequent formative assessments and feedback loops, ensuring students do not progress until they demonstrate mastery of prior material. As an example, AI may be used to skim through resumes and find certain keywords, assess the experience of the candidate, and even foresee how effective they will be in the business culture. When employees have been recruited, AI can then monitor their performance, detect areas where they are lacking, and propose individual development opportunities to make sure employees become the best they can be. An example is that customer service bots that are AI-based can be used to manage basic customer questions at any time of the day or night, so that human agents can tend to more complicated customer requirements.

The primary objective of the survey was to gather insights on AI and agentic AI adoption in the software development lifecycle from IT leaders. This survey was conducted between April 25, 2025 and May 5, 2025 across the U.S., U.K., Japan, France, Canada, Australia, India and Germany. By championing strategic decision making and scalable execution models, AI TPMs enable organizations to bridge the gap between research and real-world impact.

With AI agents, the technology can dynamically decide what to do, bringing the power of AI into workflows. Previously, processes like this would have had hundreds or thousands of manual steps—with agentic AI, it’s possible to get the desired output more easily and in seconds. For many organizations, this makes work that was previously considered impossible to achieve due to cost now within reach.