AI Business Transformation Compared Compared
In the dynamic world of business, transformation is the norm. Whether driven by technology or evolving market trends, businesses must adapt to thrive. Two prominent transformation journeys are AI Business Transformation and traditional Digital Business Transformation. While they share common objectives, their governance frameworks diverge significantly. In this article, we delve into the distinctive governance aspects that set AI Business Transformation apart from its digital counterpart.
The Evolution of Governance: From Digital to AI Transformation
Traditional Digital Business Transformation Governance
Digital Business Transformation typically involves adopting digital technologies and processes to enhance operational efficiency, improve customer experience, and remain competitive. Governance in this context is well-established, often followings a linear and traditional best practice path:
- Centralised Leadership: Typically a hierarchical centralised series of governance team/s starting with a Senior Responsible Owner (SRO) steer business transformations, ensuring alignment with business goals and unblocking issues and risks.
- Structured Roadmaps: Detailed project plans and timelines guide the transformation process, with clear milestones.
- Risk Mitigation: Risks are anticipated and mitigated through well-defined governance protocols and best practices.
- Change Management: Established change management strategies to help employees adapt to new digital tools and processes and retire old tools and processes.
- Metrics-Driven: KPIs and metrics track progress, allowing for data-driven decision-making.
AI Business Transformation Governance
AI Business Transformation goes beyond digitisation; it harnesses the power of Artificial Intelligence to reshape business models, operations, and customer engagement. Governance in this realm takes on a distinct character:
- Experimental R&D Attitude to AI Development: AI applications do not just depend on algorithms based around largely open-source models but on data. Its all about the data its quality, quantity, accessibility and how it is segmented and feed to the chosen model. This recipe does not deliver guaranteed outcomes. Its an art more than a science in many ways. The outcomes are far from guaranteed and may need a number of iterations to deliver the target outcomes required. This means Governance has to be more flexible and less hard nosed on schedule slippages and cost. Governance has to develop a more research based attitude to AI development and embrace failure as a positive and not a negative as long as it provides meaningful progress towards the target outcomes.
- Cross-Functional Leadership: AI Transformation often requires a multidisciplinary governance team, including data scientists, AI experts, and domain specialists. This diverse group ensures holistic decision-making. This group does not replace the more traditional Governance members such as SRO, key Third Party Suppliers, Finance Director etc but compliments and supports these key decision makers.
- Agile Frameworks: Traditional linear roadmaps may not suit AI Transformation. Agile frameworks are more common, allowing for flexibility and adaptation to evolving AI capabilities. This is driven by the iterative AI training approach to developing Business AI Systems.
- Ethical Considerations: AI introduces ethical complexities, necessitating governance frameworks that address fairness, transparency, and accountability in AI decision-making. This in the real world usually means that during the Potential AI Initiative Identification phase (PAI’s) these are reviewed and approved by an ethics committee. This committee scope is wide but typically always includes assessing AI Bias and fairness, transparency of its decisions, privacy, accountability etc.
- Compliance: At time of writing its clear that Governments are behind the curve concerning legislation regulating AI development, adoption and use. I fully expect this to catch up sooon but in the meantime the key considerations are in the areas of Data Privacy (GDPR) and Copyright.
- Data-Centricity: Data governance becomes paramount, with a focus on data quality, privacy, and security to fuel AI algorithms.
- Human-AI Collaboration: Change management extends to fostering collaboration between employees and AI systems. Ensuring that human-AI interactions are productive and ethical is a unique challenge. Also retiring old business processes and retraining for new business processes and new roles to support the outcomes that be achieved through AI. Unfortunately as in the case of BT will mean staff redundancies as AI drives productivity growth.
Why AI Transformation Governance is Unique
Usually transformation programmes demand delivery governance which is disbanded once the transformation is delivered. Currently AI Business Transformations require ongoing governance beyond delivery and embedding of new ways of working :-
- Algorithmic Accountability: AI introduces algorithms that require monitoring and accountability. Ensuring AI decisions are unbiased and ethical demands a distinct governance approach beyond completion of a Transformation.
- Data Dynamics: AI relies on vast datasets, often requiring governance mechanisms for data collection, storage, and usage that differ from traditional digital processes.
- Continuous Learning: AI models evolve over time. Governance must facilitate continuous learning and adaptation, unlike static digital solutions.
- Regulatory Landscape: The AI regulatory environment is evolving, with unique compliance requirements. AI governance must stay abreast of these changes.
Conclusion: A New Frontier of Governance
AI Business Transformation represents a new frontier in business governance. It demands a multidisciplinary approach, emphasises ethical considerations, and requires adaptable frameworks. While lessons from traditional Digital Business Transformation can be valuable, AI governance is a distinct expedition, charting a course towards innovative, responsible, and successful AI integration.
In this era of AI-driven possibilities, governance becomes not just a framework but a compass guiding businesses through uncharted territories. Embrace the nuances of AI Transformation Governance, and your organisation can navigate this transformative journey with confidence and foresight.