Many people go about their daily lives without giving much thought to the increasing role of artificial intelligence. But for those in the design and make world, there’s no ignoring that generative AI is quickly becoming a collaborator, augmenting human ingenuity and innovation. As companies embark on their AI journeys, CIOs are on the bow ensuring generative AI tools are incorporated the right way at the onset, with the most trusted approach and the right training.
In the architecture, engineering, construction, and operations (AECO); design and manufacturing (D&M); and media and entertainment (M&E) industries, people use their cumulative knowledge to create, innovate, and improve upon past experiments. This is also how generative AI works. People and AI can form a symbiotic relationship: People give AI the data it needs to create, and in turn, generative AI compresses timeframes for new ideas and innovation.
In the design and make world, 66% of leaders say that AI will be essential to their business in the next two to three years. Here are a few tips that can help companies successfully deploy generative AI into their processes to facilitate a better digital experience, stay ahead of disruption, and empower the design and make workforce.
Generative AI needs to know two things: where you are and where you want to go.
First, where has your company been? Design and make industries generate a ton of data—manufacturing alone creates 1,812 petabytes a year. Companies have a wealth of information from past projects, and much of it goes unused. The power of generative AI can be realized when companies leverage that history or data: A generative AI tool ingests it to understand their style and preferences, then starts giving them localized recommendations.
Just like humans need a goal to work toward, so does generative AI. For companies beginning their generative AI journey, it starts with truly understanding what business outcomes they want to achieve and identifying the criteria for success. Then, they can structure a methodical approach for incorporating AI capabilities with the right stakeholders in the right organizations. With clearly defined outcomes, generative AI will help companies achieve goals faster and with more precision.
If generative AI needs to know where you’ve been and where you want to go, generative AI strategy must consider what a company is trying to achieve and how AI can help it happen faster.
When automation first came along, the focus was on how to increase efficiency. But that’s not enough. With generative AI capabilities, we’re looking at delivering both efficiency and effectiveness. For instance, let’s say an architecture firm is using automation to reach out to 20 prospective customers instead of 10. This is an increase in efficiency. But the true power comes when technology can also deliver effectiveness, in this case determining which three out of those 20 customers should be contacted for a 100% response rate.
Improving efficiency and effectiveness helps productivity gains multiply across the company. This productivity increase can support companies through major hurdles such as labor shortages, skills gaps, and fragile supply chains while also helping achieve new goals such as decarbonization. It ensures resiliency and reaffirms a commitment to stay on course despite disruptions.
Data is the fuel that powers AI. The way companies manage their data is vital to the success of deploying generative AI tools. But, according to McKinsey, 72% of companies say their current data management strategy is hindering them from taking their AI journey further.
Investing in the right infrastructure to effectively collect, store, and analyze data is a foundational step. Then, it’s critical to establish governance policies that define ownership, answering questions like, “Who owns this set of data, and what are the access controls?” or “What about compliance and security?” There must be measures in place so AI tools do not share information without appropriate permissions.
Proper data management also avoids silos that confuse AI tools and act as roadblocks to efficiency. When companies create and store data, they must create data dictionaries so the AI model knows where to fetch specific information upon request. Data needs to be open, and that is best realized in cloud-based platforms.
Finally, focus on data quality. Generative AI is only as good as the data you provide. Having high-quality datasets will give it the right information to work with to arrive at the desired outcomes.
In addition to the focus on outcome and data, there needs to be a governance mechanism to accelerate value delivery. Autodesk has established an AI Center of Excellence approach that helps us review AI use cases from multiple perspectives with a structured prioritization framework. All key decision makers from legal, trust, security, human resources, and finance engage in the process from the start and understand the risk associated with the experimentation. This approach accelerates speed to value.
There’s no question that people hesitate when they hear AI is entering the workplace. There’s a natural fear of how it will impact jobs. But the danger isn’t AI itself; rather, it’s failing to embrace AI to do your job better.
As generative AI evolves, it will bring with it an economic benefit of $2.6 to 4.4 trillion annually, according to a study by the World Economic Forum. That study states, “Over 93% of employers expect to use GenAI within the next five years to increase innovation and creativity, automate repetitive tasks, and boost learning.” By adopting AI tools, firms can increase work-life balance by eliminating mundane tasks, freeing up workers for higher-value and meaningful work.
One way companies can eliminate mundane tasks is by harnessing the power of large language models to synthesize and automate complex commands. For example, when architects listen to customer views, using the power of AI-based speech and language synthesis and translation, they can find subtle insights that can expedite the decision-making process.
Leaders can help alleviate workplace fears with education, exposure to new tools, and fostering a culture of learning and innovation. This encourages experimentation without a fear of making mistakes. Establish pilot programs with the expectation that some may not work out. Develop comprehensive training and education programs so employees understand what AI actually is and how they can put it to work.
A real challenge is change management. People are leaning in to play with AI, but how do you get them to truly add generative AI as a regular part of their workflow? People need a little nudge to be shown what it can do for them. Otherwise, they will keep doing what they’re doing. AI becomes more effective when people move from simply using AI to summarize data, for example, to using AI to understand what data should be summarized. When people move from simple queries to advanced prompts, AI’s usefulness increases.
A change-management strategy should include upskilling employees so they’re comfortable working in a new way. While communication from senior leaders is important, peer-to-peer encouragement is also essential. Every company has AI enthusiasts. Find them in your organization and empower them to champion the adoption of generative AI. It’s important to continue educating people during the journey to move them from “playing with AI” to really understanding what is possible and knowing how to augment and improve their results. Initial excitement about AI is at times followed by a slowing of use, but with additional coaching, training, and tips, use becomes more sophisticated throughout the workday.
This process is underway at Autodesk. We’ve created training programs that address the specific needs of a range of personnel in the company, and there’s a general AI degree pathway for every employee that covers the basics of AI and generative AI. We’re also creating teams that find AI enthusiasts in various business divisions to provide guidance and support to their colleagues.
A few years back, digital acceleration moved into warp speed. Every company had to become a technology company to compete in the world. Now, every company, irrespective of the vertical, is beginning to include AI because it adds real value. Adoption is in the early stages, but it’s a bet most people are willing to take.
According to Autodesk’s 2024 State of Design and Make report, 76% of respondents—the highest percentage yet—say they trust in AI. While AI previously served to automate manual processes, it’s now playing an active role in decision-making processes, too. There are so many things that AI can and will do. Just a few years from now, we’ll wonder how we ever worked without this help.
As senior vice president and chief information officer, Prakash Kota drives a multifaceted enterprise strategy that enables Autodesk to grow and scale its business, and seamlessly deliver world-class technology experiences. Kota leads the Enterprise Systems and Experience team, which manages critical technology supporting the company’s global enterprise systems, customer operations, business platforms engineering, and infrastructure, including data and security operations, and workforce collaboration and productivity services. Prakash and the team deliver innovative solutions that allow people to focus on higher-value work and accelerate business results. Kota was named a Bay Area CIO “Global” Orbie award winner, a Forbes innovative technology leader on the “CIO Next” list, and a “Top 100 CIO” by the National Diversity Council. Throughout his nearly 20-year tenure at Autodesk, Kota has held roles including vice president of enterprise infrastructure and operations, senior director of enterprise operations, and director of dev ops. He holds a master’s degree in electrical engineering from Oklahoma State University and a bachelor’s degree in electronics and communication from the University of Madras.
POV
Emerging Tech
PD&M
Image courtesy of Chris Mueller.
