The fact that governments are serious about AI adoption is also reflected in the increasing share of AI investments--84% of agencies believe their AI investments will increase by 6% or more in the next fiscal year.4 With budget analysis showing that US Federal funding for AI research and development alone is expected to have already grown by nearly 50% to more than US$6 billion in FY 2021, government leaders are clearly bullish on AI.5As a result, they are making significant investments and exploring new AI projects. With enthusiasm and a growing pool of resources, many government organizations have launched pilots to explore how AI can help their organizations. Government organizations are exploring a range of AI use cases from speech recognition to predictive maintenance. Government sectors such as defense and health that have a long history of AI experimentation are among the leaders in fields such as responsible AI and data-sharing.

Advancing trustworthy AI and machine learning to mitigate agency risk is a priority for the US Department of Energy (DOE), and identifying best practices for implementing AI at scale is a priority for the US General Services Administration (GSA). That's what attendees learned in two sessions at the AI World Government live and virtual event held in Alexandria, Va. last week. Pamela Isom, Director of the AI and Technology Office at the DOE, who spoke on Advancing Trustworthy AI and ML Techniques for Mitigating Agency Risks, has been involved in proliferating the use of AI across the agency for several years. With an emphasis on applied AI and data science, she oversees risk mitigation policies and standards and has been involved with applying AI to save lives, fight fraud, and strengthen the cybersecurity infrastructure. She emphasized the need for the AI project effort to be part of a strategic portfolio.

Organizations of all sizes are embracing AI as a transformative technology to power their digital transformation journeys. Still the challenges around operationalizing AI at scale can still seem insurmountable, with a large number of projects failing. I've worked in big data and AI with several organizations and have seen some clear trends on why AI efforts are floundering after an enthusiastic start. These are large established organizations that have done an amazing job of garnering support from their board, C-suite, business stakeholders, and even customers to embark on AI-powered transformation journeys. They have most likely set up some form of a Center of Excellence (CoE) for AI, with key hires both in leadership and technical roles, and have demonstrated the promise of AI, using a few machine learning projects in a limited scale. Then they move to scale a project into production, and they get stuck.

I grew up in the Star Wars era. I remember sitting rapt in the theater, watching Luke Skywalker and Han Solo battle the dark forces in one intergalactic battle after another. So it's not lost on me when I read an article like this one in Popular Mechanics detailing how Artificial Intelligence (AI) is putting us closer to a Star Wars world every day. Beyond the clouds, AI is helping citizens prepare for the COVID-19 crisis. Not to mention recognizing and interpreting human emotions.

TechRepublic's Karen Roby talked with Greg Douglas of Accenture, an artificial intelligence (AI) company, about the myths surrounding scaling AI. The following is an edited transcript of their conversation. Karen Roby: As the pandemic continues, many companies are looking to scale AI projects, and there are a lot of myths out there about how you actually take this technology to scale. You guys have put together a really interesting study that looked at a real cross section when it comes to AI. Let's talk a little bit about the most important findings and what really jumped out to you from this study. Greg Douglas: We interviewed 1,500 executives across 16 different industries around the world, so a really vast survey, to see where our clients and where companies were at in terms of scaling and deploying artificial intelligence.

As artificial intelligence and machine learning-based systems become more ubiquitous in decision-making, should we expect our confidence in the outcomes to remain like that of its human collaborators? When humans make decisions, we're able to rationalize the outcomes through inquiry and conversation around how expert judgment, experience and use of available information led to the decision. To borrow the words of former Secretary of Defense Ash Carter when speaking at a 2019 SXSW panel about post-analysis of an AI-enabled decision, "'the machine did it' won't fly." As we evolve human and machine collaboration, establishing trust, transparency and accountability at the onset of decision support system and algorithm design is paramount. Without it, people may be hesitant to trust AI recommendations because of a lack of transparency into how the machine reached its outcome.

Perceiving the pandemics' hard reset as a chance to grow stronger, more resilient, and resourceful dominates manufacturers' mindsets who continue to double down on analytics and AI-driven pilots. Combining human experience, insight, and AI techniques, they're discovering new ways to differentiate themselves while driving down costs and protecting margins. And they're all up for the challenge of continuing to grow in tough economic times. Boston Consulting Group's recent study The Rise of the AI-Powered Company in the Postcrisis World found that in the four previous global economic downturns, 14% of companies were able to increase both sales growth and profit margins as the following graphic shows: AI Is Core To Manufacturing's Real-Time Future Real-time monitoring provides many benefits, including troubleshooting production bottlenecks, tracking scrap rates, meeting customer delivery dates, and more. It's an excellent source of contextually relevant data that can be used for training machine learning models.

If the first industrial revolution was set in motion by steam, then Industry 4.0 is being powered by artificial intelligence. And with its ability to automate, digitise, and optimise, AI is the perfect fit for manufacturing operations, from product development to quality control. A computer vision system, for example, allowed GM to detect 72 instances of component failure, preventing massive downtime (a single of minute of which can cost a company of that size up to $20,000) while a machine learning system significantly improved Danone's demand forecast accuracy (reducing forecast error by 20%, lost sales by 30%, product obsolescence by 30%, and demand planner's workload by 50%). The latest report from the Capgemini Research Institute – Scaling AI in manufacturing operations – shows that intelligent maintenance, together with product quality inspection and demand planning constitute a good starting point for manufacturers to focus their efforts in manufacturing operations. To tap into the manifold benefits AI can bring to manufacturing operations, organisations need to move beyond the pilot/proof-of-concept stage and deploy at scale.

If the first industrial revolution was set in motion by steam, then Industry 4.0 is being powered by artificial intelligence. And with its ability to automate, digitize, and optimize, AI is the perfect fit for manufacturing operations, from product development to quality control. A computer vision system, for example, allowed GM to detect 72 instances of component failure, preventing massive downtime (a single minute of which can cost a company of that size up to $20,000) while a machine learning system significantly improved Danone's demand forecast accuracy (reducing forecast error by 20%, lost sales by 30%, product obsolescence by 30%, and demand planner's workload by 50%). The latest report from the Capgemini Research Institute – Scaling AI in manufacturing operations – shows that intelligent maintenance, together with product quality inspection and demand planning constitute a good starting point for manufacturers to focus their efforts in manufacturing operations. To tap into the manifold benefits AI can bring to manufacturing operations, organizations need to move beyond the pilot/proof-of-concept stage and deploy at scale.

A full 84% of C-suite executives believe they must leverage artificial intelligence (AI) to achieve their growth objectives. Nearly all C-suite executives view AI as an enabler of their strategic priorities. And an overwhelming majority believe achieving a positive return on AI investments requires scaling across the organization. Yet 76% acknowledge they struggle when it comes to scaling it across the business. What's more, three out of four C-suite executives believe that if they don't scale AI in the next five years, they risk going out of business entirely.