Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Even though responsible artificial intelligence (AI) is considered a top management concern, a newly released report from Boston Consulting Group and MIT Sloan Management Review finds that few leaders are prioritizing initiatives to make it happen. Of the 84% of respondents who believe that responsible AI should be a top management priority, only 56% said that it is, in fact, a top priority -- with only 25% of those reporting their organizations has a fully mature program in place, according to the research. Further, only 52% of organizations reported they have a responsible AI program in place – and 79% of those programs are limited in scale and scope, the BCG/MIT Sloan report said.

When artificial intelligence augments or even replaces human decisions, it amplifies good and bad outcomes alike. There are numerous risks and potential harms created by AI systems, including bias and discrimination, financial or reputational loss, lack of transparency and explainability, or invasions of security and privacy. Responsible AI enables the right outcomes by resolving dilemmas rooted in delivering value versus tolerating risks. Responsible AI must be a part of an organization's wider AI strategy. Here are the steps that chief information officer and information technology leaders, in partnership with data and analytics leadership, can take to progress their organization towards a vision of responsible AI.

There is increasing evidence of the business benefits of responsible AI (RAI), when companies mitigate risks through training and testing data, measuring model bias and accuracy, and model documentation. Companies that adopt responsible AI experience higher returns on their AI investment. Raj Shekhar writes that business leaders globally must coalesce around the imperative to develop rigorous, consistent standards for responsible AI adoption. Much has been spoken and written about the risks to public trust and safety arising from the adoption of artificial intelligence (AI)-based applications across multiple sectors. In finance, the use of AI has led to discriminatory credit decisions.

The notoriously secretive Meta has set a milestone for transparency. The company this week offered the entire research community access to a fully-trained large language model (LLM). Named the Open Pretrained Transformer (OPT), the system mirrors the performance and size of OpenAI's vaunted GPT-3 model. While GPT-3 has a stunning ability to produce human-like text, it also has a powerful capacity for biases, bigotry, and disinformation. OPT's creators said their system can reduce these risks: Our aim in developing this suite of OPT models is to enable reproducible and responsible research at scale, and to bring more voices to the table in studying the impact of these LLMs.

Not that long ago organisations were being urged to'just get started' – to set up a dream team of data scientists and protect them from the rest of the company, letting them get on with developing proofs of concept and allowing them to fail or succeed fast. Those carefree days are over. The time has come to operationalise AI across organisations at scale and to start deriving clear business benefits, including improved customer service and ROI from time and money already spent. Beyond that, the pandemic has undoubtedly accelerated digitalisation – compressing years into months – and data-first, AI-enabled organisations are the ones that are going to succeed in the future. In a way, AI needs to become boring.

Conversational AI is becoming more popular as a way of automating messaging and speech-enabled applications that offer human-like interactions between computers and humans, but it's often hard to get these projects right There are three main forms of communication in every organisation where conversational AI can dramatically change how the organisation works, and they are: the speech elements around customer dialogue; email, and instant messaging. To access data and information that is useful to an organisation from these communication formats takes a huge amount of manual processing. With the right conversational AI in place you can deliver cost savings and improve customer experience significantly, improve NPS scores and increase customer retention and revenue. Many companies have used voice recognition technology in the last ten years. However, until recently, customers were saying it was a poor experience as the technology wasn't good enough and it wasn't really helping improve the customer experience.

Conversational AI is becoming more popular as a way of automating messaging and speech-enabled applications that offer human-like interactions between computers and humans, but it's often hard to get these projects right There are three main forms of communication in every organisation where conversational AI can dramatically change how the organisation works, and they are: the speech elements around customer dialogue; email, and instant messaging. To access data and information that is useful to an organisation from these communication formats takes a huge amount of manual processing. With the right conversational AI in place you can deliver cost savings and improve customer experience significantly, improve NPS scores and increase customer retention and revenue. Many companies have used voice recognition technology in the last ten years. However, until recently, customers were saying it was a poor experience as the technology wasn't good enough and it wasn't really helping improve the customer experience.

Following bold claims about the business benefits and even world-saving power of artificial intelligence (AI), it's not surprising that talk of broken promises is growing. However, has AI failed to deliver, or are we setting our expectations too high? Recently, consternation has centered around Covid-19 as some initial AI-assisted breakthroughs quickly fizzled out. For many, frustration stretches back much further, with a 2010 study showing 38% of organizations lacked the understanding of how to use analytics to make better and faster decisions. Today, fewer than 25% of global organizations have developed an enterprise-wide AI strategy.

Is AI driving digital transformation? Or is digital transformation driving the adoption of AI? Are they synergistic, or just getting in the way of each other? Numerous studies show that large organizations are realizing only limited success in digital transformation projects. For example, BCG and McKinsey research have put success rates in the 30% range. However, it is still unclear whether or not that acceleration is leading to the success of transformation efforts beyond the ability to work remotely. In many cases, acceleration has simply meant getting a program initiated and deployed quickly, but at the cost of increased technical debt and less attention to foundational processes and data quality.

Artificial Intelligence (AI) is boosting business efficiency and productivity by automating procedures and operations that previously required human intervention. AI can also understand data on a level that no human has ever been able to. This skill has the potential to provide significant business benefits. Every function, business, and sector may benefit from AI. There are both general and industry-specific applications in this category.