The Fourth Edition of the State of AI in the Enterprise
Few businesses are fully AI-powered now, but a sizable and growing percentage are exhibiting the behaviors that will lead them there. What can we learn from cutting-edge companies’ practices?
Who is now dominating the AI market?
AI-enabled companies use data as a resource and apply human-centered AI to all of their fundamental business activities. They improve workforce and consumer experiences by making quick, data-driven decisions. Our analysis identified four different sorts of businesses that are working toward this goal. To discover more about these four profiles, simply click or tap on the boxes below.
Transformers
794 executives | 28% total survey population
This group has recognized and mostly implemented leading practices linked with the strongest AI outcomes, although it is still transforming. They average 5.9 out of 10 full-scale deployments of various sorts of AI applications and 6.8 out of 17 high-level outcomes.
Best methods for transforming your business to be AI-powered
A clear, well-communicated vision, business-led work transformation, established development standards, an agile workforce, and a broad network of ecosystem partners are often the foundations of AI success. We discovered that the four cornerstones of AI-powered enterprises are strategy, operations, culture and change management, and ecosystems.
Strategy
Transformers were three times as likely as non-Transformers to have a company-wide AI strategy. What lessons can you take away from them?
1. Prioritize approach
Connect your AI strategy to your company’s strategic north star and use it to guide your AI investments.
2. Innovate and automate
Don’t put too much emphasis on efficiency gains. You may also use AI to reinvent how you do business.
3. Explain your vision
Huge results are motivated by big visions. Talent and money are attracted as a result of public awareness.
4. Continue to iterate
Develop flexible methods for evaluating and adjusting your strategy as the market changes.
Operations
New ways of working are frequently required when implementing new technology such as AI. Only about a third of the organizations polled stated they’d implemented cutting-edge operational procedures. What steps can you take to rethink your operations from both a business and an IT standpoint?
1. Ensure that the company is profitable
It’s tempting to delegate the application of data-driven technology to data scientists, but business needs must come first.
2. Rethink roles and workflows
Organizations that have made significant workflow changes or added new roles are nearly 1.5 times more likely to achieve high-quality outcomes.
3. Implement MLOps procedures
DevOps alone is insufficient. The development and maintenance of machine learning algorithms necessitate a distinct approach. Organizations that strictly follow these procedures are three times more likely to succeed.
Management of culture and transformation
Trust, data fluency, and agility are required for successful AI outcomes. Why is it so vital to use change management to develop the correct culture?
1. Fear is overcome by trust
AI visions that are bold elicit both healthy and unhealthy fear. Through it, your team’s trust keeps them moving forward.
2. Creative insights are fueled by data fluency
Data literacy skills help organizations gain confidence and trust in AI, which helps them achieve positive outcomes.
3. Agility allows you to fail quickly
AI-powered businesses can turn insights into rapid experimentation and pivot swiftly after failure.
Ecosystems
A diversified and well-executed ecosystem approach provides flexibility, stability, and perspective. Two or more types of ecosystem partners are used by 83 percent of responding high-achieving firms. How do we create thriving ecosystems?
1. Select partners with a variety of viewpoints.
Organizations with a diversified ecosystem are much more likely to have a transformative AI vision, enterprise-wide AI plans, and leverage AI as a strategic differentiator.
2. Make things difficult.
If there are too few external partnerships, it may be tough to break up with vendors in the future.