Exploring AI’s Role in Organizational Strategy and Innovation

The Strategic Impact of AI in Modern Organizations

By [Student’s Name]

Course Code and Name

Professor’s Name

University

City and State

Question 1: AI can take on different roles within an organization. Create for me a mini playbook for how your organization should both investigate and evaluate a proposed AI project. This playbook can include guidelines for investigation and evaluation in general (regardless of role), but must also include additional meaningful considerations for each role specifically.

Response:

In addition to the three business functions of AI-enabled automation, AI-enabled engagement, and AI-enabled insight, there are other business features such as innovation. Machine learning and neural deep learning networks can automate or improve the process and results of invention. AI data-driven insights, models, and visualizations facilitate creative interpretation of data and support decision-making in the innovation process. Finally, deep learning has the potential to reduce the time it takes to bring a new product to market. As a result, several pharmaceutical and biotechnology start-ups are investing in AI to identify and validate potential drug candidates and speed up the overall drug discovery process. AI technology may not yet be able to develop the entire solution on its own, but it can show human managers the most promising path to innovation. Still, using AI to innovate raises some tensions.

Organizations that fail to transform their operations with AI algorithms and data frequently face one or more of the following challenges:

• Failure to plan and strategically take the first steps.
• Not concentrating on delivering business value.
• Senior management’s lack of commitment
• Inability to find AI talent.

1. Significant process improvement by reducing operating costs.

2. Reduce manual processes and human error in critical data entry Routine interventions such as human input and consumption of information in Multiple IT systems.

3 Automating business processes and speeding up process execution Improve the customer experience with less effort.

4. Organizations need to automate their business with cognitive and AI technologies Process rationalization, contract analysis, contract renewal, communication, and sales, You can also support your customers and automate the delivery and replenishment of their inventory society.

5. AI real-time project data analysis helps decision-makers identify potential potential

Risks and opportunities before they occur, and Customer attrition or credit card fraud prevention.

Question 2: Consider a customer-facing product or service within your industry (or the industry you would like to/intend to work in once you graduate) that could be enhanced for increased customer value by better prediction somehow. This enhancement should NOT already exist in the current marketplace. Apply your playbook from question 1 – is this an opportunity worth pursuing? If you believe there are additional important considerations beyond your playbook, then include them here as well.

Response:

AI has the potential to support my business with SRP (Salt River Project) and can leverage AI for customer satisfaction and engagement. Consumer expectations about how utilities interact have been heavily influenced by their experience with other companies, especially those that provide a fully digital experience, such as Netflix and Google, all highly personalized. Intended for customers with offers and immediate responses. With so much data available about customers, AI can be used to translate them into concrete actions, and customer engagement begins with the device.

Customers rely on the power grid, but they operate the light switch and the start button of the washing machine. AI can intelligently process large amounts of data and learn from it. AI can provide a personalized view of these home interactions between devices through advanced energy decomposition and customer segmentation.

This approach leverages some households’ experience, where they need to make multiple phone calls to their behavior, preferences, expected bills, and utility customer service numbers. You can use the playbook’s machine learning algorithms to view this device-level energy consumption. Data scientists use vast amounts of data to inform algorithms to identify the different usage patterns that air conditioners, pool pumps, or electric heaters support.

This is useful when more data provides accurate and meaningful results. More important than that, AI can provide utilities with the highly personalized, actionable, and timely information they need to communicate with their customers to lead to increased engagement and satisfaction. We may use energy decomposition data in billing notifications and send it digitally to our customers, and this is called a midcycle alert. In this particular scenario, not only was the notification sent that the bill was expected to be higher than average but the action the customer could take to resolve the issue was also sent.

You can use the cognitive insights from the playbook above to develop machine learning algorithms and create personalized customer communications with mental involvement. The technology or tool available here is a deep learning algorithm that proactively predicts customer billing based on usage and sends customized emails. This approach helps low-income families reduce their energy consumption and the number of bills they have to pay. This helps businesses understand consumers and their energy consumption. Providing your customers with the most customized experience, it’s definitely a worthwhile opportunity to pursue, ultimately leading to higher revenue growth and increased brand awareness and loyalty.

Question 3: Consider an internal organizational workflow you have experience with that could be enhanced by better prediction somehow. This could be from any role in any organization you have held, or in general (if necessary) from your prior studies, by using any example outside of those presented in our book/lectures for this class. Apply your playbook from question 1 – is this an opportunity worth pursuing? If you believe there are additional important considerations beyond your playbook, then include them here as well.

Response:

By better predicting data conflicts and missing customer information, I can improve the internal organizational workflow that I have witnessed. AI process automation systems are also being used by businesses to keep their data clean, perform data integration tasks, check for inconsistent data, and enter missing information. To address the issue of swivel chair integration, organizations have traditionally used robotic process automation (RPA) tools. These individuals manually enter or extract data from multiple systems to complete a task.

Many customers, employees, or employees for vendors need to deal with the input or extraction of information from multiple systems to get the job done. Some effort can be made to connect these systems using the API or integrated middleware. Still, these systems are often implemented or built by a third party and therefore communicate with each other. I will not. RPA tools are used to quickly and inexpensively automate the task of entering or extracting data from multiple systems. However, like factory assembly robots, many of these software bots are not immersed in intelligence.

They merely automate human-defined tasks and repeat them indefinitely. AI and machine learning have the potential to provide these bots with the intelligence they require. Natural language processing and computer vision not only enter or extract data as directed but also convert images and documents into machine-readable text and data, allowing these bots to interact with them. The preceding playbook can deploy intelligent agents, such as bots, to integrate with AI process automation systems that use natural language and machine learning algorithms.

These AI process automation systems can handle large amounts of data from different structured and unstructured sources in different formats and languages. Machine learning systems can monitor information, data, and systems and alert users when unusual activity occurs. In this way, these machine learning-based systems identify inconsistent data from different sources, leverage additional systems to fill data gaps, and ensure that the data meets the requirements of the task at hand. Can be confirmed.

These AI-enabled bots complement human agents and provide additional steps to prevent data errors and missing information from spreading throughout the organization. This opportunity is worth pursuing, as customer data remains clean, validated, and complete. In addition, we implement AI process automation tools to improve compliance with ever-changing regulatory and compliance requirements.

Machine learning has been implemented to automatically tag and identifies data that matches these patterns and apply the required compliance-oriented information to the data or operations for proper management. One of the advantages of autonomous intelligent systems is constantly monitoring the system and data. AI-enabled process flows to monitor and identify actual data and workflows between systems without the need to hard-code workflows or integrate them with APIs that do not provide full access to data. It just responds to anything that matches the pattern.

Question 4: Again consider your industry or the industry you plan on joining once you graduate. Compare and contrast what an AI-first organization would (or does) look like in your industry versus a traditional organization. How would they behave differently both externally (to customers and partners etc) and internally (through their own objectives, workflows, boundaries, etc)? Discuss industry-specific benefits and drawbacks to either organizational approach. If yours is currently a more traditional industry, discuss a possible future event that may create an inflection point favoring AI-first organizations. How do you see this shift playing out for existing firms in your industry? How does this impact the balance of power between incumbents and new entrants? If AI-first organizations are already favored in your industry, describe how best a new entrant could leverage the elements and priorities of an AI-first strategy to compete with established industry competitors.

Response:

Traditional analytics is static, and AI analytics is dynamic. Traditional organizations` data analytics typically relies on dashboards composed of visualizations. These dashboards are based on common business questions and are predefined well in advance. Answering a new question requires more time and technical skills, usually multiple days or weeks, and assistance from a data analyst or scientist. These dashboards are static and can`t adapt to the changing needs of the business, as new questions and challenges that arise can`t always afford to be put on hold. AI analytics allows users to dynamically request and combine information to answer business questions without technical assistance.

AI systems that support a conversational interface allow users to ask questions in natural language via natural language processing, which means users can ask questions like “how did our brand perform last quarter” and receive an answer in natural language via natural language processing. Hypotheses drive traditional analytics, and AI analytics is driven by data. As mentioned, dashboards are typically predefined based on common questions or a certain business view. These dashboards are inherently biased because they predetermine what`s most important and only show the data that`s relevant to that set viewpoint.

These hypotheses will be influenced by the individual`s experience and the limitations of their time and energy. In contrast, AI analyzes all the data, producing unbiased answers from exhaustive testing. By allowing the data to lead the analysis, AI won`t miss important insights hidden underneath the metrics on the surface. Of course, biased data can produce biased answers, and companies must ensure data is as complete and neutral as possible to fully leverage AI applications. Cleaning data to be unbiased is an action that should be taken regardless of investments in AI.

However, when it comes to what traditional or AI analysis can do with data, AI analysis can generate a wide range of answers that lead to action plans, while traditional analysis can display data. Ultimately, AI analysis enables business people and data analysts, and scientists to make fair and informed decisions.