AI first strategies will increase ROI in 2021

As organizations continue to invest in AI and scale their deployments, a collaboration between business decision-makers and technologists must become more effective as they partner to determine which AI use cases within the business will deliver the best ROI.

Today, the greatest hurdles to deploying AI with confidence are

1. Gaining broad agreement that the projects are vital and feasible,
2. Ensuring the organization has the right data and can effectively manage the data pipeline in support of the projects and
3. Building the right org structure to accomplish the project goals. 

To overcome these challenges, in 2021, businesses, especially those with maturing AI programs, will focus on educating non-technical employees about AI in order to foster collaboration and decentralize how AI projects are designed and developed. This will enable companies to target projects with the most potential for delivering the desired business benefits while giving due consideration to what data will be used for projects and how data models will be trained to ensure their success.

AI first strategies will increase ROI in 2021

Organizations that have an AI-first strategy enjoy increased ROI compared to organizations that keep AI efforts project-based in 2021. 2020 has reinvigorated the way enterprises are thinking about AI. While uncertainty can often be a time when projects get put on hold, earlier this year, 70% of businesses reported that COVID-19 would either cause no impact to their existing plans, or accelerate their strategies, betting on AI projects to have a positive impact on their organizations' resiliency, efficiency, and innovation, according to Appen’s 2020 State of AI report. However, for companies to obtain the maximum ROI with AI, they must commit to expanding their successful projects into an AI program that ensures a continuous training data update strategy. According to the Appen report, 75% of organizations with AI programs update their models at least quarterly to ensure data accuracy and relevance. 

In 2021, we’ll start to see widespread adoption of AI programs that focus on ensuring the quality of data throughout the lifecycle of every AI project. This will include increased reliance on third-party solutions, such as off-the-shelf datasets that are expertly developed and regularly updated to enable companies to accelerate time to market for AI projects and ensure the ongoing availability of high-quality data. Companies that do not shift to a more programmatic approach to AI will lag behind those that do and struggle to achieve the results and ROI they expect.

AI governance will increasingly become a board-level issue, with “responsible AI” a key requirement for AI projects

There is broad agreement that the adoption of enterprise AI is essential for competitiveness based on its ability to improve operational processes, provide deeper business insight, enhance the customer journey, and accelerate innovation. However, there remain several concerns about the impact of AI, including trust in the quality of data, the impact on society of biased training data, and the potential mistreatment of millions of human annotators who train the data. 

In 2021, as boards focus on closing the gap between AI’s potential benefits and the reality (only about 1 in 10 enterprises report obtaining “significant” financial benefits from AI – MIT Sloan), they will increasingly mandate AI governance programs that incorporate the principles of “responsible AI.” Responsible AI sets out standards and best practices for the responsible training of data with the aim to improve quality, efficiency, and transparency – including the elimination of bias in training data – while promoting inclusivity and collaboration. Responsible AI practices also include paying annotators fair wages and adhering to labor wellness guidelines and standards. Greater board involvement in AI projects will increase the adoption of these standards by the larger technology community, which in turn will increase the value of AI to businesses, as well as trust in the use of AI by the public.

Businesses must confront the pitfalls of AI projects if the democratization of AI is to gain momentum in 2021

Our 2020 State of AI survey of vested executives and decision-makers in IT showed that 3 out of 4 respondents felt behind in their AI progress – we think that number will drop in 2021 as their progress ramps up and we start to see more businesses gain access to the raw materials of knowledge, tools, and data required to build an AI system. More innovations are bound to emerge from this increased adoption as efficiency improves and engagement increases. And, as we have heard from numerous partners and customers, with more businesses having initiated their AI projects to rapidly address the demands of 2020, we’ll see the AI playing field level a bit more to include more mid-sized businesses and enterprises that are just beginning their AI journeys.

However, for these trends to take hold, every business launching AI projects must confront the potential pitfalls. Companies developing AI projects that are not based on responsible AI principles risk creating applications based on biased training data that do not work as expected, produce erroneous results, or treat some groups of people differently than others. Further, internal governance and external regulation have the potential to slow the development of AI projects and launch them into production as businesses look to protect their brands and governments explore possible regulation. 
2021 is likely to be a pivotal year when enterprises and AI industry leaders recognize that the key to AI success is an AI governance program that ensures the implementation of responsible AI principles.

Who is Wilson Pang

A software engineering and data science tech leader, Wilson Pang, CTO at Appen, is passionate about driving businesses to succeed through innovation in training data for AI. Before Appen, Wilson was CDO at CTrip in China, Senior Director of Engineering at eBay, and a software architect at IBM.