The pace of change driven by the onset of AI is nothing short of astounding. Startups continuously change perceptions of what best-of-breed might mean. At the same time, we see PoCs progressing to projects literally within a few months. Consequently, many boards are paranoid about the emerging notions of cognitive and AI, but all too often fail to turn those fears into actionable items. Against this background this report takes stock where the enterprise market for AI really is at. How is AI enabling organizations journey toward the OneOffice? A crucial element of this report is to play back the lessons learned from the early deployments.
Key Market Dynamics
- AI in not one market, but AI should be seen as a set of technologies and building blocks. AI not only intersects with Automation and Analytics as the Triple A Trifecta framework suggests, but AI should be seen as a set of technologies and building blocks that span a continuum and should be discussed within the context of business operational impact, service delivery capability, and specific use cases.
- Enterprise AI is still at the periphery of the enterprise or applied as a bolt-on. Reflecting the early development phase, organizations pursuing AI solutions with a "bolt-on" approach, applying AI at the edge of the enterprise. As the market matures, AI has the potential to disrupt and replace enterprise architectures and enterprise software.
- The Enterprise AI market has a duplexity of approaches: Industrialization and project-centric. We have to be cognizant of the disparate starting points and context for projects. Not least in order to get a better sense of the capabilities of service providers, we need more differentiation in the discussions around AI. RPA and chatbots are low level; compare those with the expansion of data science projects, autonomics, or even virtual agents, which have a high complexity and require significant investments.
- The Holy Grail of AI is at the intersection of iterative data inputs and minimal training of algorithms. The are many misguided expectations that one has only to throw Machine Learning at data and that would be sufficient to integrate those data sets in production. Rather organizations have to move to a data-centric mindset where data is the centrepiece of digital strategies.
Readers will get a deeper understanding of the market dynamics around enterprise AI services and where the market really is at. The report provides frameworks and conceptual models to better understand the complexity of disparate AI technologies and approaches. A central theme is how organizations have to progress toward a more data-centric business model. Key pillars of this model are approaches to train algorithms and integrate increasingly unstructured data in an iterative way into production.
Furthermore, we cover discussions on how talent and new skills requirements have to be assessed. An important goal of this research is to provide more clarity on the different starting points for AI projects to get to more meaningful discussions of specific market segments. Equally, we dive into how providers are advancing toward institutionalizing AI and driving AI as a foundational layer across their organizations. Last but not least, we are providing a ranking of the leading service providers while outlining their capabilities and investment priorities.
Who Should Read this Report
Executive leaders and business unit leaders, outsourcing and procurement managers, advisors, investors who have responsibilities for innovation, digital transformation and for building out service delivery capabilities.
- Introduction and Key Definitions
- Executive Summary
- The State of the Enterprise Artificial Intelligence Market
- Research Methodology
- Service Provider Grid
- Service Provider Profiles
- Market Predictions and Recommendations