According to Gartner, only 47 percent of AI enterprise projects are in production. The rest are struggling to get their AI projects into operation. Despite production rates, executives at the enterprise level are confident that this technology will soon transform their business. Success stories and use cases across industry verticals are demonstrating how AI technologies are transforming other industries: Retail (Amazon’s growth), automotive (GM’s predictive maintenance program), and healthcare (cancer screenings by FullFocus).
The AI revolution is well and truly underway, but organizations need to harness the power of artificial intelligence with the latest industry advancements:
AI comes in many forms. When evaluating a framework for applying AI in the enterprise, it’s easy to get caught up in the technology and industry buzzwords to gain the approval of key stakeholders. Instead of focusing on the technology, focus on the organization’s needs, outcomes, and assets:
The first step in the AI framework process is to identify business needs that require a high percentage of human intervention. Business problems that require heavy cognitive processes or complex decision-making are excellent candidates for setting up AI applications.
Algorithms for computers to process and comprehend language are a popular starting point for enterprise AI. These algorithms leverage AI technologies such as natural language processing and computer vision to answer customer and employee questions, fulfill requests, update records, and file documents in the cloud.
Identify Outcomes and Business Value
Focusing on “low-hanging fruit” for initial business value will deliver more impact than a broad, overreaching approach. For example, if customer service is part of the business needs, consider customer loyalty or improving customer experience rather than focusing directly on sales.
By limiting the initial outcome, technical feasibility becomes more manageable, and it’s easier to measure milestones and markers of success. Remember, AI exists not only for customers, but employees within every organization must also adopt the technology.
Identify Data Sources
The process of implementing AI depends on high-quality data in large quantities. If organizations haven’t invested in cloud infrastructure, they’ll need to securely store their data with no multiple databases. With the Capacity alternative, cloud storage, knowledge management, and automation all live under the same roof, helping organizations accelerate the use of their data sets and information for AI systems.
If a company’s data storage offers limited functionality, third-party plugins improve search features and performance.
Need more information? Download Best Practices for Driving AI Engagement and Adoption.
As companies focus on business needs, define outcomes, and identify data sources, it’s a good idea to consider these points before making a final decision:
Decision-makers for a company’s AI models should know they constantly evolve as they encounter new data. Time and resources should be part of the budget and schedule to guide new iterations. In addition, executive AI leaders should collect more edge cases to improve their understanding without disruptions in processes.
One of the most common AI technologies that are at the top of the implementation list is chatbots. AI helps power satisfactory customer interactions through the use of chatbots. It has proven potential to provide personalized experiences for customers and prospects who visit the website. When paired with devices, location data, and a robust knowledge base as a source of data, chatbots can give customers a superior experience at every touchpoint.
Not sure about chatbot technology? No problem. Capacity provides a guided conversation option for customers and employees.
If a chatbot is an option for the future, download the chatbot productivity eBook.
To remain competitive, organizations are driving change by actively modernizing job functions and roles to create a work environment where digital mindsets thrive. This shift in perspective has led to employee upskilling, creating new possibilities that were not possible before implementing machine learning algorithms and predictive analytics.
As employees become more technologically savvy, automation is expected, no longer a perk for the engineering team. Capacity’s workflow builder enables your team to take offline processes and build them out online. Even better? Tracking is available so that employees can continually improve each workflow.
When company leaders first learn about enterprise AI technology, they often discuss it with the leadership team. Creating an AI framework requires discussions with future power users, including the customer service and tech support team. Companies should have a solid starting point to form a plan using the definitions and system classifications above.