Brandon serves as Chief Strategy Officer of UiPath, a leading provider of enterprise automation software.
Every second, vast amounts of data are being collected, and businesses are working to use that data to advance their operations and meet business objectives. The advancement of AI and machine learning technologies allow companies to transform industries through greater agility, predictability and insights from unstructured data. Now, innovations in large language models and generative AI, including GPT models, can add greater value by helping employees with day-to-day work.
Any AI project requires an investment of time, money and skill to operationalize. In 2022, companies worldwide spent $45.8 billion funding AI projects. To justify that spend, businesses need to estimate the potential business value and impact of the solution before making significant development investments. Goldman Sachs expects generative AI alone to increase global GDP by 7% (nearly $7 trillion) and boost productivity growth by 1.5% over the next 10 years, so the potential boost to business growth and wealth is certainly eye-catching.
Leaders need to understand the pain points they are solving, ensuring they are not building AI tools just for the sake of having AI. This can lead to minimizing outcomes and creating barriers to scale that doom initiatives to failure. In fact, it’s estimated that anywhere from 60% to 80% of AI projects fail. Despite the growing levels of investment in and adoption of AI, there have been no substantial increases in reported mitigation of any AI-related risks, according to a study by McKinsey.
This idea that technology can solve any issue is known as “technological solutionism,” a term coined by the technology critic Evgeny Morozov. Technological solutionism, according to Evan Selinger, a professor of philosophy at Rochester Institute of Technology, creates a false belief that we can make great progress on alleviating complex dilemmas—if not remedying them entirely—by reducing their core issues to simpler engineering problems.
When companies think that AI can solve any problem, they can wind up on the technological solutionism bandwagon, especially because it is reassuring and financially enticing to have the promise of a one-size-fits-all solution.
Creating an AI strategy prior to investing in a new solution will increase the likelihood that new AI projects and investments will deliver on their promise to generate business value. A thorough AI strategy will also limit the possibility of technological solutionism, strengthening the overall investment and helping companies solve actual issues with their tools.
The Most Common Mistakes To Avoid
The primary constraint to driving growth and boosting productivity is time. Businesses, their leaders and employees give up on ideas daily because of a lack of time and resources. Here is where AI can step up to the plate: new, efficient ways to work and reduce mundane tasks through automation. To put it simply, AI can transform operations. The successful implementation of AI provides businesses with new revenue streams, allowing profit margins to grow and maximizing return on investment.
But companies, unfortunately, often adopt too many tools at once. AI is not a “set it and forget it” software; quickly jumping in on the latest solutions will mean you are not getting the best out of your investment. Additionally, AI is only as good as the data used. Data lacking detail or insufficient data will limit AI’s value. This will yield unsatisfactory results that undermine investments in AI tools and solutions, so it is critical that data is sourced and vetted correctly.
Part of the challenge is the countless applications for AI within a business, and it’s often difficult for business leaders to determine where to start. Without a proper roadmap that looks for automation opportunities across the entire value chain, the need for AI spend is jeopardized in the future. With a strategy focused on problem-solving, businesses can set the stage for quickly developing and operationalizing AI solutions.
The Best Path Forward
Organizations seeing the highest returns from AI likely follow best practices for strategy, data, models, tools, technology and talent. By building a plan that clearly prioritizes AI initiatives linked to business value across the company, organizations can focus on any projects based on the relative effort and estimated return on investment (ROI).
Besides looking for opportunities across the entire value chain, today’s leaders in AI adoption also strive for a more mature implementation that blends automation with other restructuring techniques, like policies, roles and company-wide behavior. With this, each new project with AI will have the potential to deliver a clear outcome for the organization that can help justify more investment in other AI initiatives to any relevant stakeholders, including the C-suite and the board.
A comprehensive AI strategy should include architectural and best-practice guidance to help employees develop and leverage robust solutions that fit their needs. Training on AI will ensure employees are bought in on this new technology and can champion its use across the organization. Because of this, many employees see AI as a solution to enhance job satisfaction. UiPath recently polled more than 6,400 global workers and found that 58% of respondents believe AI-powered automation can address burnout and improve job fulfillment.
AI can help employees decrease workloads, learn new skills and create opportunities for collaboration. When employees can be upskilled, organizations will see a significant and positive impact on the edge of their workforce. When employees can reach beyond low-hanging fruit with their automation capabilities, they can help the company seek opportunities beyond cost savings. These opportunities can allow organizations to build out automation for customer service, a product launch, and think bigger within the overall customer journey through these newfound digital channels.
As technology continues its rapid advancement before us, businesses can’t afford to delay the strategic implementation of AI. For organizations that have yet to start investing in AI solutions, it’s not too late to use this technology to your advantage.
However, scaling AI across the business will be difficult without a clear technology deployment strategy. Rather than hastily using the latest trending solution, work with your executive team to develop a clear vision, investment strategy and objectives for AI to create more efficient operations for both employees and customers.
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