AI adoption continues to expand across the globe, with Gartner predicting that organizations over the next five years will “adopt cutting-edge techniques for smarter, reliable, responsible and environmentally sustainable artificial intelligence applications.” And as the industry matures and machine learning (ML) models become cheaper, faster, and more accessible, every enterprise will be looking at how and where the technology may benefit their organization.
Expectations are high, from driving productivity and efficiency gains to delivering new products and services. AI platforms are being enhanced by developments in related fields, including ML, computer vision, language, speech, recommendation engines, reinforcement learning, edge IT hardware, and robotics. However, with so much noise and hype around AI, it’s tough for many businesses to figure out how to harness the technology effectively.
Below are some trends that enterprises should be cognizant of as they look to integrate AI into their operations.
- Three stages of AI adoption: AI adoption for organizations will encompass three distinct stages: 1) AI-ready – where there are some AI technologies within the company, 2) AI-capable – when an organization has AI capabilities built in-house within its products/services and people are using them every day, 3) AI-enabled – businesses that have adopted enterprise-grade AI. Enterprises mustn’t try to skip a stage as these foundations are vital to scale.
- Buy, don’t build: There is no need for every organization to hire a swath of data scientists when AI as a service (AIaaS) is readily available from Microsoft, Google, and Amazon. Instead, enterprises should look to buy, not build, to accelerate the adoption of AI capabilities. AI should now be consumed as a service and then customized to suit the needs of each organization. By 2030, AI as a platform will be pervasive across enterprises.
- Explainable use case AI: Organizations should only implement AI if they can easily explain the benefits to the business. Otherwise, they run the risk of adopting technology for the sake of innovation. If you can’t explain it, it’s not enterprise-ready AI.
- Augment, don’t replace: More and more enterprises recognize they should use AI to augment rather than replace human workers. Organizations will realize that AI is not a panacea to every issue and that, in some instances, it’s cost-prohibitive to replace everything with AI.
- Plug people gaps: With workers set to remain an increasingly scarce commodity for the foreseeable future, enterprises will turn to intelligent process automation (IPA). IPA is an excellent solution for repetitive tasks, enabling humans to take on more challenging roles. As noted previously, AI will augment employees for the foreseeable future, allowing people to take on more creative and challenging work while routine tasks are automated. AI will help alleviate much of the digital grind employees currently deal with, allowing them to shift their focus to solving problems.
- Mitigate disruption: With uncertainty a constant, AI will help businesses understand and predict where problems are most likely to occur. They will use this intelligence to reduce the impact of potential disruption, helping build a more resilient organization better able to weather events with limited impact.
- Improve complex decision-making: AI will power organizations to make better decisions quicker, helping them drive their business forward. These decisions will positively impact performance, operations, and employee satisfaction. Over the next couple of years, AI will help make timely and accurate decisions in increasingly complex areas like autonomous transport to earlier detection of diseases that human intelligence can’t keep pace with.
- The rise of structured data: AI systems require vast amounts of data to be effective, and for some use cases, this is not available, or it will take too long to generate. Knowledge databases can alleviate that problem and provide a lower-cost alternative. By harnessing domain-specific context-sensitive data, enterprises can scale AI automation, helping improve productivity and agility. For software testing, harnessing synthetic data avoids creating data privacy and security issues. Knowledge graph databases will be the foundation of digital twin adoption as businesses look to accelerate and reduce the cost of designing products by creating a digital model.
- Sustainable AI: As enterprise adoption continues to scale, attention will focus on exactly how data is shared, helping drive interest and uptake in sustainable AI. This approach prioritizes data privacy by design and focuses on the minimum viable data set to achieve the business goal.
- AI as a superpower: The role of AI in the enterprise is to help make everyone a superhero. It will increasingly help automate many repetitive tasks, making lives easier and more enriched and allowing organizations to do more with less. To sum it up, AI will ultimately help humans with humanity.
- Governing AI: It’s vital to ensure that AI is doing the right thing and behaving as expected and this will put more focus on testing AI to validate and monitor its actions. This area will garner more attention to avoid bias creeping in. It will also ensure that the AI aligns with an organization’s ethical and sustainability goals.
These trends will help drive and accelerate the core benefits of AI automation. Intelligent technologies will usher in a slew of innovative digital products and services that few would have thought possible a decade ago, helping reshape how we work and live. Enterprises will continue to look for ways to harness and scale AI’s capabilities to transform their business operations and performance. With AI maturing, no business can afford to ignore AI’s potential if it wants to stay competitive in a digital-first world.