Software companies worldwide face a fundamental choice as artificial intelligence reshapes product development and customer expectations. SAP’s Sean Kask, speaking with The Next Web, argues that hesitation carries real consequences. Companies that delay integration of AI capabilities risk falling behind competitors who treat intelligence as a core component rather than an add-on feature.

Kask, who serves as SAP’s global head of AI and machine learning, draws from his experience leading Prior Labs, an AI research group acquired by the enterprise software giant. His perspective carries weight because he has witnessed the transition from experimental AI projects to production systems that handle billions of transactions. According to the interview, every software organization must now view itself as an AI organization first, with traditional development practices evolving around intelligent systems rather than the other way around.

The pressure comes from multiple directions. Customers increasingly expect applications that anticipate needs, automate routine decisions, and provide insights without requiring complex queries. Developers themselves demand tools that reduce repetitive coding tasks so they can focus on higher-value problems. Meanwhile, competitors who adopt AI aggressively capture market share by delivering faster results at lower operational costs.

SAP itself underwent this transformation after acquiring Prior Labs in 2021. The integration allowed the company to embed advanced machine learning models directly into its enterprise resource planning systems. Rather than building separate AI applications, SAP engineers began redesigning core business processes with intelligence at their foundation. This approach produced measurable improvements in supply chain optimization, financial forecasting, and customer service automation.

The shift requires more than adding new features to existing products. Organizations must reconsider their entire development methodology. Traditional waterfall approaches give way to iterative cycles where models train continuously on fresh data. Data governance becomes central because AI systems perform only as well as the information they receive. Teams that once specialized in specific technical domains now collaborate across disciplines including data science, user experience design, and business analysis.

Kask emphasizes that successful AI adoption starts with clear business objectives rather than fascination with the technology itself. Companies that begin by asking what problems they want to solve typically achieve better outcomes than those chasing the latest model architectures. This focus helps teams avoid common pitfalls such as building sophisticated systems that fail to deliver practical value.

Training and talent development represent another significant challenge. Many established software companies employ developers with decades of experience in conventional programming but limited exposure to statistical learning methods. Bridging this knowledge gap requires structured education programs, external partnerships, and sometimes strategic hiring. The most effective organizations create mixed teams where experienced engineers learn alongside data scientists, creating knowledge transfer that benefits both groups.

Infrastructure decisions also play a decisive role. Running AI workloads demands substantial computational resources that many companies have not previously managed at scale. Cloud providers offer convenient access to specialized hardware, yet data sovereignty concerns and cost considerations push some organizations toward hybrid approaches. The choice between training custom models and using pre-trained foundation models depends on specific use cases, regulatory requirements, and available expertise.

Data quality issues frequently undermine AI initiatives. Enterprise systems often contain inconsistent records accumulated over years of different business practices. Cleaning and organizing this information takes considerable effort but yields substantial returns when models can rely on trustworthy inputs. Organizations that invest early in master data management and quality assurance processes position themselves for faster progress once they begin deploying intelligent features.

The competitive dynamic creates urgency. Startups with AI-native architectures move quickly because they face fewer legacy constraints. Established players must balance innovation with the need to maintain reliable systems that millions of users depend upon daily. This tension explains why some companies announce ambitious AI strategies while making slower progress implementing them in production environments.

SAP’s experience demonstrates that large organizations can adapt successfully when leadership commits to the necessary changes. After integrating Prior Labs’ technology, the company accelerated development of intelligent features across its product portfolio. Customers now interact with systems that automatically suggest optimal inventory levels, detect anomalies in financial transactions, and generate natural language reports from complex datasets.

Yet success stories should not obscure the difficulties involved. Many companies underestimate the organizational changes required beyond the technical implementation. Departments must adjust workflows, incentive structures need realignment, and success metrics evolve from traditional key performance indicators to ones that reflect AI-specific outcomes such as model accuracy and prediction confidence.

Ethical considerations add another layer of complexity. As systems make increasingly autonomous decisions, questions arise about accountability, transparency, and potential bias. Organizations that address these topics early develop governance frameworks that support responsible deployment. Those that treat ethics as an afterthought often encounter regulatory challenges or public backlash that slows their progress.

The talent market reflects these pressures. Professionals with combined skills in software engineering and machine learning command premium compensation. Companies compete not only with each other but also with specialized AI organizations that offer different career paths. Building internal academies and certification programs helps retain existing staff while developing the next generation of AI-capable developers.

Looking across industries, patterns emerge among organizations that advance quickly. They typically designate executive sponsors who understand both business strategy and technical possibilities. They establish centers of excellence that spread knowledge throughout different divisions rather than concentrating expertise in isolated teams. They measure progress through business outcomes rather than technical milestones alone.

Customer expectations continue rising as consumer applications demonstrate what intelligent systems can accomplish. Business users who enjoy personalized recommendations and predictive features in their personal lives naturally ask why enterprise software cannot provide similar capabilities. This pressure forces companies to accelerate their AI integration or risk losing users to more responsive alternatives.

Technical architecture decisions made today will influence capabilities for years to come. Organizations that design flexible data pipelines and modular model serving infrastructure adapt more easily as new techniques emerge. Those that hard-code specific algorithms into core systems face expensive refactoring when better approaches become available.

The economic implications extend beyond individual companies. Entire industries may see productivity gains as routine cognitive work becomes automated. Supply chains could optimize in real time rather than through periodic reviews. Financial reporting might shift from monthly cycles to continuous monitoring with instant alerts. These changes will reshape job descriptions and organizational structures across sectors.

SAP’s message through Kask carries particular resonance because the company serves some of the world’s largest enterprises. When such a major player declares that every software company must become an AI company, the statement carries implications for the broader technology market. Vendors who supply tools to these enterprises must similarly adapt their offerings or face declining relevance.

Developers at all levels will need to acquire new skills. Understanding prompt engineering, evaluating model outputs, and debugging learning systems become essential competencies alongside traditional coding abilities. Educational institutions face pressure to update curricula while companies invest in reskilling current employees.

The pace of change creates both opportunities and risks. Organizations that move thoughtfully can gain significant advantages through improved efficiency and novel capabilities. Those that rush without proper preparation may deploy systems that generate incorrect results or expose sensitive information inappropriately. Finding the right balance requires careful planning and continuous evaluation.

Industry observers note that successful transformations share common characteristics. Leadership communicates a clear vision that connects AI initiatives to business strategy. Teams receive adequate resources and realistic timelines. Failures are treated as learning opportunities rather than career risks. Knowledge sharing occurs across organizational boundaries rather than remaining trapped in departmental silos.

As more companies follow this path, the definition of standard software will evolve. Features once considered advanced AI capabilities will become baseline expectations. Applications that cannot learn from usage patterns or provide intelligent assistance may appear outdated to users accustomed to more sophisticated tools.

The transition demands patience alongside ambition. Building reliable AI systems takes time, especially in domains where accuracy carries financial or safety consequences. Organizations that acknowledge this reality while maintaining steady progress typically achieve more sustainable results than those promising rapid transformations that fail to materialize.

SAP’s acquisition of Prior Labs and subsequent integration efforts provide a practical example of how established software companies can remake themselves. The experience suggests that while the challenges are substantial, they are not insurmountable. Companies willing to examine their current practices, invest in their people, and redesign processes around intelligent systems can position themselves for success in an environment where AI capabilities increasingly determine market leadership.

This evolution touches every aspect of software creation and deployment. From initial requirements gathering through ongoing maintenance, intelligent systems influence how products are conceived, built, and improved. Organizations that embrace this reality early gain advantages that compound over time as their models learn and their processes mature. Those that delay may find catching up increasingly difficult as the gap between AI-native and traditional approaches widens. The coming years will likely separate companies based on how decisively they act on the recognition that software development has fundamentally changed.