Artificial intelligence technology (technology related to artificial intelligence) is transforming companies in almost every industry. From chatbots to predictive analytics, from warehouse optimization to customer service, AI is revolutionizing corporate processes. Adopting AI can lead to lower costs, greater efficiency and higher revenues. Successfully integrating this type of technology, however, requires careful planning.
Steps for implementing enterprise artificial intelligence technology
So what are the steps to consider when deciding to use artificial intelligence-based technology solutions? How can a company take advantage of a set of innovative and, in some ways, still unfamiliar tools?
Consider business needs and strategic goals
First, one must ask whether the company really needs this type of technology. How could AI add value to business operations? What are the goals for which this technology has been chosen and how will it be used to achieve them?
Assessing AI readiness
Before implementing any artificial intelligence-based solution, assess the readiness of the organization. Key areas to be checked include:
- Data infrastructure: AI relies on data. We need to make sure that we have data of sufficient quality and quantity to train artificial intelligence models, filling any gaps in collection, cleaning and labeling. Therefore, it will be necessary to review the data pipeline, BI (business intelligence) tools, and governance.
- Staff skills: The team will need expertise in areas such as machine learning, analytics, and process reengineering. Assess skill levels and provide training where necessary.
- Leadership support: Educate executives on the benefits of AI and gain commitment to change management and investment.
- Technology scenario: Review the existing technology stack and identify where AI could improve or automate processes.
Selecting the right AI tools
The AI ecosystem is complex with countless vendors and competitors in the market. Therefore, to equip oneself with a model based on artificial intelligence, a few key points must be taken into consideration:
- Ease of use: Look for platforms with intuitive interfaces that allow even non-experts to train, implement and monitor AI models.
- Data and integration: Ensure that the platform easily connects to existing data sources and IT environment.
- Explainability: Choosing transparent algorithmssuch as linear regression over neural net black boxes when interpretability is critical.
- Scalability: Choose distributed systems that can grow as data volumes and use cases increase.
Implement AI responsibly
The application of AI also carries risks, such as the adoption of biased reference models and the loss of human oversight in some processes. Therefore, it remains essential to adopt responsible practices including:
- Perform tests and adjustments on biases in data (BIAS)and algorithms and validate models to ensure they are accurate and meet defined objectives.
- Keep humans in the process of validation and decision-making with serious consequences.
- Protect user data with encryption and access controls.
- Communicating with transparency with people impacted by AI systems.
Practical applications of AI in businesses
So what are the most common and best optimized use cases of artificial intelligence, to date, in business?
- Process automation: reduction of manual tasks through AI, automating repetitive and redundant processes.
- Customer service: implementing chatbots and sentiment analysis to personalize interaction with customers, offering tailored solutions and services based on their preferences and behaviors to improve CX.
- Demand forecasting: statistically modeling internal and external data to forecast sales.
- Predictive maintenance: using machine learning on sensor data to predict equipment failures.
- Fraud detection: AI can be used to identify anomalous patterns or suspicious behavior, helping to prevent and counter corporate fraud.
- Data analytics: AI enables meaningful information to be extracted from business data, revealing valuable insights to improve business strategies and guide informed decision making.
- Decision optimization: leveraging artificial intelligence can analyze large amounts of data and generate recommendations or predictions that help make more informed decisions.
Measuring business impact
Integrating artificial intelligence-based technology can profoundly benefit companies, but it is also essential to track performance metrics to determine the ROI of adopted AI implementations. It will be useful to measure aspects such as:
- Productivity gains through time/cost reductions
- Increased revenue from sales and higher conversions
- CX metrics such as satisfaction, retention and call volumes
Continuously monitor models for deviations and retrain to optimize results over time.
Implement XCALLY in your own business AI model
The adoption of XCALLY, the Asterisk-based omnichannel software , represents a tremendous opportunity for contact centers wishing to implement artificial intelligence technology in their operational process.
With its advanced features, XCALLY enables the full benefits of AI to be exploited in the business environment.
With the Conversational AI, it is possible to automate interactions with customers, improving efficiency and providing high-quality service.
Sentiment analysis makes it possible to monitor and understand customer sentiment, enabling companies to adapt their communication strategies and deliver a personalized experience. In addition, AI-based self-service apps offer autonomous solutions to customers, reducing agent workload and speeding up response time.
Finally, the simplicity of use, scalability and ease of integration with existing CRMs in the suite make XCALLY an ideal solution for companies looking to improve their operational efficiency, optimize the customer experience, and gain a competitive advantage in the marketplace.
