Artificial Intelligence (AI) has woven itself into countless industries, but many enterprises still struggle to understand how to effectively harness AI for a sustainable competitive advantage. With the right approach, enterprises can go beyond simply implementing AI to creating transformative workflows and business models. This playbook serves as a comprehensive guide for both enterprises looking to adopt AI and providers aiming to serve them effectively.
Understanding the AI Advantage in the Enterprise
Part 1: The Micro Logic – Unbundling and Rebundling Work
Part 2: The Opportunity – Moving to "Service-as-a-Software"
Part 3: Enterprise Context – Performance, Not Promise
Part 4: Workflow Capture – Economics of Enterprise AI
Part 5: Business Model Advantage – Service-as-a-Software
Part 6: Challenges – Hype, Organisational Disconnect, and Lateral Attacks
Part 7: Competitive Advantage – Creating Moats and Expanding Accounts
Part 8: Defining Winners and Losers in Enterprise AI
Conclusion
FAQs
Adopting AI effectively involves understanding three critical elements:
This playbook is organised into eight sections, each focusing on essential aspects of enterprise AI and offering a step-by-step approach to successful adoption.
Unbundling and Rebundling Tasks: AI provides a new approach to workflow management by unbundling tasks typically performed by humans and reassigning them to software. By using AI-powered tools, enterprises can streamline workflows while maintaining the strategic value added by human decision-making.
For example, in corporate travel booking:
This shift enables enterprises to not only automate tasks but also redefine how tasks are interconnected and executed.
With advancements in Generative AI, the concept of "Service-as-a-Software" is emerging. This model transforms human-led workflows into AI-dominant workflows where key tasks and decisions are executed by software.
Stages of Transformation:
Unlike past technological waves, AI adoption in 2024 requires tangible performance outcomes. Enterprises are cautious, preferring solutions that promise concrete ROI over mere potential. With LLMs undergoing a transition akin to broadband's transformative effect on the internet, enterprises are more likely to invest if AI can deliver reliable, scalable solutions.
The true advantage of AI lies not in individual task automation but in the ability to capture and manage entire workflows. This "workflow capture" allows enterprises to adopt AI without significant disruptions to existing systems.
AI offers a unique advantage by allowing businesses to shift from subscription-based models to performance-based models. Just as IoT led to models where companies charged for outcomes (like jet engine operating time), AI can enable businesses to charge for workflow outcomes, such as leads generated or support tickets resolved.
Benefits of a Performance-Based Model:
Establishing control points within workflows is essential to build a competitive moat. Enterprises need to focus on capturing critical decisions and actions within a workflow, ensuring AI-driven components align with enterprise goals.
Winning with enterprise AI requires the ability to combine vertical expertise with horizontal expansion. Successful players will:
Enterprise AI has moved beyond the hype. Companies that strategically unbundle and re-bundle workflows, adopt performance-driven models and anticipate competitive dynamics are best positioned to leverage AI for lasting advantage. By following this guide, enterprises can navigate the complexities of AI adoption with confidence, transforming AI from a mere tool into a powerful enabler of growth and efficiency.
Service-as-a-Software is an AI-driven approach where complex workflows traditionally managed by humans are absorbed into software, automating knowledge and managerial tasks.
While SaaS offers software tools to perform tasks, Service-as-a-Software goes further by fully automating entire workflows, including decision-making and management, making it a more transformative model for enterprises.
Knowledge-intensive and repetitive managerial workflows, such as customer support, procurement, and corporate travel booking, are ideal for AI-driven workflow automation.
AI can take on repetitive tasks or decisions in workflows, unbundling them from human roles and bundling them into efficient, software-driven processes that increase productivity and reduce costs.
Enterprises benefit from reduced labour costs, faster decision-making, enhanced workflow efficiency, and new opportunities for performance-based pricing models.
It allows enterprises to automate workflows, freeing up human resources for strategic tasks, and enabling businesses to offer performance-based pricing, a distinct edge in competitive markets.
Key challenges include ensuring high AI accuracy, aligning AI with user experience, managing organizational change, and defending against competitors with similar capabilities.
Businesses should establish control over critical workflow components and integrate deeply with client operations to build a moat, making it harder for competitors to take over.
CXOs should champion AI adoption, align AI strategies with business goals, and foster cross-functional teams to ensure the AI solution meets both technical and user needs.
AI-driven workflows can shift enterprises from subscription-based to performance-based pricing, creating new revenue models and fostering long-term client relationships through efficiency and measurable outcomes.