The text highlights the transformative impact of generative AI (gen AI) on businesses in 2023, emphasizing the need for companies to adapt to this rapidly evolving technology while not losing sight of core business imperatives. It outlines ten key ideas shaping the modern business landscape, such as the significance of building capabilities for innovation, the importance of scaling technology, the need for continuous digital transformation, and the critical role of data in deriving value. Additionally, it discusses the emergence of a workforce empowered by gen AI, the necessity for agile operations, and the importance of focusing on value creation. The text underscores that successful companies will be those that can adapt quickly, test effectively, and leverage technology to create competitive advantages.
| name | description | change | 10-year | driving-force | relevancy |
|---|---|---|---|---|---|
| Rise of Citizen Builders | More individuals are becoming capable of building digital products due to easier tools. | Shift from traditional developers to citizen builders in creating digital solutions. | An increase in the number of non-technical individuals creating tech solutions will democratize innovation. | Advancements in user-friendly software tools and lower costs are enabling more people to build. | 4 |
| Scaling Challenges in Gen AI | Despite the excitement around generative AI, scaling remains a significant challenge. | Move from high visibility of LLMs to a need for scalable implementations in businesses. | Companies will develop structured approaches for effectively scaling generative AI solutions. | The necessity to realize the full potential of AI technologies will drive focus on scalability. | 5 |
| Compounding Value in AI and Digital | Companies that leverage digital and AI effectively are gaining a compounding competitive advantage. | Transition from simple use cases to integrated digital solutions that enhance value creation. | A divide will emerge where only digitally adept companies thrive while others lag behind. | The pursuit of efficiency and value generation in competitive markets will drive integration. | 5 |
| Continuous Digital Transformation | Businesses must constantly adapt to new technologies rather than viewing transformation as a one-time event. | Shift from viewing digital transformation as a project to an ongoing process. | Organizations will be perpetually evolving, constantly integrating new technologies for competitiveness. | Rapid technological advancements necessitate ongoing adaptation and skills development. | 5 |
| Data as a Core Asset | Harnessing data effectively is essential for generating value in AI and business strategies. | Shift from viewing data as secondary to recognizing its fundamental role in business strategy. | Companies will prioritize data governance and accessibility to maintain competitive advantages. | The increasing reliance on data-driven decision-making will emphasize the need for data strategies. | 4 |
| Human-Centric AI Integration | The workforce is evolving to leverage AI capabilities, necessitating a focus on human skills. | Transition from traditional roles to a workforce empowered by AI tools and skills. | Workforces will be highly skilled in collaboration with AI, enhancing productivity and creativity. | The need for companies to maximize the potential of AI will drive investment in human skills. | 4 |
| Agile Organizational Structures | Companies are adopting agile methodologies resembling neural networks for decentralized innovation. | From traditional hierarchical structures to agile, interconnected teams for rapid innovation. | Organizations will be more dynamic, enabling faster response to market demands and innovation. | The need for speed and adaptability in competitive markets will foster agile practices. | 4 |
| IT as a Service Evolution | IT functions will transition to service-oriented models to support distributed innovation. | Shift from controlling IT functions to enabling innovation through service delivery. | IT will be deeply integrated into every aspect of business, enhancing agility and responsiveness. | The demand for rapid innovation and customer-centric solutions will necessitate this shift. | 4 |
| Focus on Value Creation | Companies must maintain a focus on value rather than just technological advancements. | Transition from technology-centric approaches to value-driven business strategies. | Successful companies will prioritize substantial value creation over marginal technology improvements. | The need to meet financial targets and drive growth will keep value at the forefront. | 5 |
| Testing as a Core Competency | Organizations are increasingly prioritizing testing capabilities to adapt to rapid changes. | Shift from static strategies to dynamic testing and adaptation in business practices. | Businesses will evolve to be testing-centric, enabling rapid iteration and innovation. | The acceleration of change will require companies to be more experimental in their strategies. | 4 |
| name | description | relevancy |
|---|---|---|
| Overreliance on generative AI | The widespread excitement about generative AI might distract businesses from core imperatives, leading to poor strategic decisions. | 4 |
| Scaling challenges | Many companies struggle to scale new technologies and products effectively, potentially leading to wasted resources and failed initiatives. | 5 |
| Disparity in productivity gains | Variability in the distribution of productivity gains from generative AI could widen gaps among workers and businesses. | 4 |
| Data mismanagement | Harnessing data effectively is crucial to creating value; poor data strategies can prevent companies from maximizing AI potential. | 5 |
| Resistance to transformation | Companies that fail to fully commit to continuous digital transformation may fall behind more adaptive competitors. | 4 |
| Skill gap in the workforce | As AI capabilities grow, there may be a lack of necessary skills among workers to effectively utilize these technologies. | 5 |
| Innovation stifling due to centralized IT | Traditional IT structures may limit innovative practices and slow down the implementation of new technologies. | 4 |
| Minimal focus on value creation | Companies may overlook the importance of delivering substantial value through digital and AI initiatives, leading to underperformance. | 5 |
| Neglecting adaptability | Businesses that fail to prioritize adaptability and rapid testing may be outpaced by more agile competitors. | 4 |
| AI ethics and governance | As AI applications expand, ethical concerns regarding privacy, bias, and governance may arise, requiring careful management. | 5 |
| name | description | relevancy |
|---|---|---|
| Citizen Builders | The rise of non-professional developers using accessible tools to create digital products, driving innovation at all levels of a company. | 5 |
| Scaling Focus | Emphasis on building capabilities and processes that enable companies to effectively scale new technologies and innovations. | 4 |
| Continuous Transformation | Understanding digital and AI transformation as an ongoing journey rather than a one-time project, requiring constant adaptation to new technologies. | 5 |
| Data as Knowledge | Recognition that effective data strategies are crucial for leveraging AI and driving business success, making data management a core competency. | 5 |
| Superworkers | Workforce enhancement through generative AI tools, empowering employees to increase productivity and adapt to new workflows. | 4 |
| Neural Business Structure | Adoption of agile principles across organizations to create interconnected small teams that foster innovation and speed up processes. | 4 |
| IT as a Service | Transition of tech teams to service-oriented roles, enabling distributed innovation and embedding technology within all business areas. | 4 |
| Value-Driven Focus | Maintaining a clear focus on creating substantial value from digital transformations rather than getting lost in the technology itself. | 5 |
| Testing and Adaptability | Increased emphasis on rapid testing and iteration in business strategies and operations to keep pace with changes and opportunities. | 4 |
| name | description | relevancy |
|---|---|---|
| Generative AI | A class of AI technologies that can create content and provide assistance, revolutionizing how work is performed. | 5 |
| Large Language Models (LLMs) | AI models that understand and generate human language, foundational to generative AI applications. | 5 |
| Digital Twins | Virtual representations of physical systems that can be used for testing and simulation in real-time. | 4 |
| Cloud Technologies | Technologies enabling scalable and flexible computing resources, essential for modern digital businesses. | 4 |
| APIs | Application Programming Interfaces that allow different software systems to communicate and integrate more effectively. | 4 |
| Agile Methodologies | A set of practices promoting iterative development and collaboration in software and product development. | 4 |
| MLOps | A set of practices focused on streamlining the deployment and management of machine learning models in production. | 4 |
| Superapps | Applications that provide multiple services and functionalities, often integrated with AI capabilities. | 4 |
| Data Governance | Policies and processes ensuring data quality, integrity, and accessibility within organizations. | 4 |
| Automation | Technologies and processes that reduce the need for human intervention in tasks, increasing efficiency. | 4 |
| name | description | relevancy |
|---|---|---|
| Generative AI Distraction | The excitement around generative AI may distract business leaders from core imperatives and necessary rewiring of their companies. | 5 |
| Citizen Builders | The rise of citizen builders who can create software and digital products will alter the landscape of business innovation and scalability. | 4 |
| Scaling Challenges | As generative AI technology evolves, scaling its implementation effectively remains a major challenge for businesses. | 5 |
| Compounding Value through Digital and AI | The growing gap between digital and AI leaders and their competitors emphasizes the importance of compounding value in business. | 4 |
| Continuous Transformation in Digital and AI | Businesses must embrace ongoing digital and AI transformation as a continuous journey rather than a one-time effort. | 5 |
| Data Strategy Importance | A comprehensive data strategy is crucial for companies aiming to leverage AI and drive operational value effectively. | 5 |
| Workforce Reskilling for AI Superpowers | The emergence of AI copilots necessitates a focus on reskilling and upskilling the workforce to maximize productivity gains. | 4 |
| Neural Business Model | Companies will increasingly adopt a ‘neural business’ approach to enhance agility and speed in innovation. | 4 |
| IT as a Service Paradigm Shift | The transformation of IT functions into service-oriented models will enable more distributed innovation across organizations. | 4 |
| Value-Centric Digital Transformation | Businesses must keep a strong focus on value generation in their digital and AI initiatives to achieve meaningful results. | 5 |
| Testing Capabilities Enhancement | The need for adaptability and rapid testing capabilities will become essential as change accelerates in the business environment. | 5 |