Digital business needs to quickly make decisions in support of business moments, informed by relevant data. They must then adapt their capabilities to act on those decisions in near real-time. To do this, organisations must rethink how they make decisions.
But these newly reengineered decisions require a new platform for development, assembly and deployment. Current application and app development environments are not helping. DevOps and other new operational practices are helping, but a new architecture is needed. Composable applications provide the building blocks to assemble needed packaged business capabilities to help execute decisions. They provide the flexibility to adapt as information emerges or assumptions change.
Decision making is not about dashboards or reports; it is about increasing autonomy, enrichment and augmentation of how decisions are made (by human or machine) at every level of the organisation. Intelligent business will unleash creativity and innovation. It will also reduce costs while driving the discovery of new business value and business models founded on data value and on being data-driven. Data and analytics, including AI and applications, will merge at the point of a business moment, turned into value for all stakeholders.
AI engineering is a discipline focused on the governance and life cycle management of a wide range of operationalised AI and decision models.
These include machine learning, knowledge graphs, rules, optimisation and linguistic and agent-based models. AI engineering methods also enable the governance and procedures for retuning, reusing, retraining, interpreting or rebuilding AI models. AI engineering also deals with the combination of AI techniques (or composite AI). Organisations are combining different AI techniques to:
As AI techniques do not only solve problems but are starting to creatively generate solutions, generative AI techniques are also coming under the purview of the AI engineering discipline. Organisations can use generative AI algorithms to create models of things that do not exist in the real world. Generative models have the potential to affect many creative activities.
In the longer term, generative AI techniques will fundamentally change industries including manufacturing, architecture, aerospace, pharmaceuticals and the media. The benefits to society are potentially significant; for example, AI research is underway to support the formulation of new drugs for pandemics.
A robust AI engineering strategy will facilitate the performance, scalability, interpretability and reliability of AI models while delivering the full value of AI investments.
Business-driven hyperautomation is a disciplined approach that organisations use to rapidly identify, vet and automate as many business and IT processes as possible. Hyperautomation involves the orchestrated use of multiple technologies, tools or platforms. Examples of these include AI, machine learning, event-driven software architecture, robotic process automation (RPA), intelligent BPM suite, integration platform as a service, low-code tools and other types of decision, process and task automation tools.
Hyperautomation has been trending at an unrelenting pace over the past few years, mainly because of the pent-up demand for operationally resilient business processes.
The collective impact of these business and IT realities is the launch of many initiatives (often disparate and siloed) aimed at applying automation across knowledge work for either efficiency, efficacy or business agility.
The organisational zeal for using hyperautomation has led to many new offerings, vendors and commercial models across an extensive number of technology markets.
Hyperautomation is irreversible and inevitable. Everything that can be automated will be automated. Competitive pressures for efficiency, efficacy and business agility are forcing organisations to address back-, middle- and front-office operations. Organisations that resist the pressures will struggle to remain competitive or to differentiate.