Augmenting the future of business, creativity and innovation

Even in a challenging economic environment, companies are pouring investment into artificial intelligence and machine learning projects in the hope of gaining critical business insights, or staying one step ahead of the competition. Yet evidence shows this spending doesn’t always drive value, or the desired results. The typical firm will wait months, even years, to see payback on an ROI project.

The wait for AI ROI

In this edition of Perspectives, Thoughtworks’ experts unpack techniques and practices to help ensure AI is not just capable of delivering return on investment, but also contributes to the organization’s strategy and innovation portfolio.

i. Identifying and acting on AI opportunities

AI has never been more powerful, or more accessible - but that doesn’t mean it’s always the right solution to a business challenge. Understanding the best general use cases for automation, identifying specific processes that are ripe for improvement, and careful planning and testing should all be part of an evaluation that precedes any major AI plunge.

A convergence of technology forces lays the foundation for AI to shine

ii. From automation to augmentation - and creativity 

Automation is the standard goal in AI/ML practice, but is in fact just one aspect of a much broader potential value chain. By graduating from automation to augmentation companies can reap gains that go beyond productivity and efficiency. At its best, AI becomes a tool to help organizations solve complex problems, anticipate and adapt based on real-time data, and even create new products or business lines. 

The AI value continuum

iii. Navigating data, human and ethical dimensions

Building a successful AI practice for the long term requires organizations to manage three key challenges:

Data: If data sources are disparate, distorted or simply unavailable, AI systems can generate incomplete or problematic outcomes that spiral into major issues, as machines learn based on previous faulty conclusions. Sometimes the best approach can be to start fresh.  

People: AI projects inevitably touch on human roles, and those impacted need a say in how they’re rolled out and a degree of education to secure the necessary organizational buy-in. Broad-based support is vital in AI implementations because organizations are likely to find themselves short of dedicated talent.

Talent shortage weighs on AI adoption

Ethics: Allowing systems to make decisions like who gets access to a product or service, or when an image or interaction presents a possible problem, means these systems may have direct ethical implications. That puts the onus on the business to ensure AI-driven decisions are transparent, traceable, and as free of bias as possible.

From old limitations to new possibilities

AI is a fast-developing field, with unprecedented computing power, tailored algorithms and natural language processing all set to extend the frontiers of what’s possible for businesses. Enterprises can look forward to AI playing a more strategic and creative role as the age of augmentation arrives in earnest. At the same time, there are tasks that no system can take over.