AI-defined business environment
We are witnessing Cambrian Explosion of Generative AI solutions. Large Language Models now enable use of natural language in human-machine interaction. Abundance of new AI tools and co-pilots leads to quantum leap in human productivity. This rewrites the rules for knowledge work and beyond. Overall, in less than a year, Generative AI has emerged as single biggest disruptor across all industries.
There’s more. Foundation Models that power Generative AI do not limit themselves to language used in ChatGPT conversations. Instead, they are multimodal. That facilitates interaction between all types of data, including text, image, video and speech. The resulting evolution is not only about unprecedented AI solutions but is gradually leading to revolution in the way we humans use all products and services.
Indeed, the idea of Multimodal Generative AI based User Interface is mind-blowing. In the meantime, imagine having access to ChatGPT that knows everything about your products, services, customers and sales process.
However, AI technology advances driven Digital Ecosystem is not only about disruption. It is also valuable source of resources needed to take company’s own digital capabilities to a whole new level.
How to compete in that environment
To compete in AI-defined business environment is to embed AI in all aspects of value creation. This is the way to leverage advances in AI technology and to tap into digital ecosystem capabilities. Strategic goal is simple: Increased customer value, improved customer experience and enhanced operational efficiency – with an extensive amount of versatile AI use cases.
Embedding AI translates to integration with products, services and business processes. In other words, augmenting products and services with AI for higher value and better experience, and equipping business processes with AI co-pilots and AI-boosted enterprise applications for enhanced operational efficiency.
Integration builds on two things: First, understanding business needs in detail and translating them into viable AI use cases. Gaining the necessary in‑depth understanding is easier when the context is bounded – enabled by distributed operating model. Second, technical integration relies on modern software development methodology, high-quality data products, and solid AI model development and deployment process.
Embedding AI in all aspects of value creation leads to massive cognitive load onto the organization. The way this effort is organized and orchestrated makes all the difference in terms of future competitiveness.
Digital capabilities needed to compete
Detailed strategies with regards to How to compete vary from one company to another. However, commonality shared by all companies remains: Need to do AI at scale. With AI-defined business environment and with AI penetrating all aspects of value creation, there’s no alternative. The era of AI point-solutions is over.
Digital capabilities needed to do this are to a large extent determined by AI use case integration discussed above. But there’s a caveat: cognitive load combined with the scalability imperative. That is the fundamental challenge. Solution: Digital Distribution operating model with two overarching design principles.
First, decentralize data ownership and application development to Business Domains. Make their operating contexts bounded with ability to focus on their own business logic and needs, combined with shared semantic understanding, concepts and language within tight-knit cross-functional teams.
Second, eliminate all engineering and IT related cognitive load in business domains. Do this with systematic Plug-and-Play Framework deployment based on Platform Engineering principles. Plug-and-Play Framework is to serve all AI integration needs from decentralized data management to AI model development and to software development. Combine Platform Team in-house capabilities with outsourced capabilities from digital ecosystem players of all types: Platform, Tool and Service Providers.
The ultimate goal is to facilitate business domain growth into digital value creation and innovation engines for sustained renewal and competitiveness. First step: Assess constraints that prevent achieving AI at Scale – constraints that prevent doing hundreds of concurrent AI use cases.