Rapid AI Insights: Edition 21

Hello,

Welcome to the last edition of of Rapid AI Insights for 2023! This week, we share a framework for managers to better understand #AIprojects and #datasets, explore how #alternativefinance is leveraging AI, and dive into the "intelligent" factory and its processes. We wrap up by highlighting some priorities that can guide #digitaltransformation initiatives in 2024.

Manager of a team with AI projects? Ask these questions

Unlocking the potential of AI hinges on one crucial factor: the data on which AI models are trained. Garbage in, garbage out, as the saying goes.?

But how, as a business manager, do you navigate this complex terrain? How can you ensure you're harnessing the power of AI without falling prey to pitfalls like biased algorithms or inaccurate results? Enter a helpful framework outlined in a recent article by MIT Sloan Management Review aimed at empowering managers to ask the right questions about AI models and data sets.

AI seems like the perfect solution for many business problems, promising efficiency, insights, and a competitive edge. Yet, the excitement can quickly turn into frustration if the underlying data fuelling these models is flawed.

This is where the importance of asking the right questions comes in.?

Here are some of the questions.

1. How and where do you anticipate the models that you develop will be utilized?
2. How will you acquire training data that meets the right data criteria?
3. How will you check that future data meets the right data criteria?
4. What are the top three ways you could envision your models failing in deployment? What steps have you taken to mitigate them?

Read the entire framework in the article here .?

AI fuels alternative finance

In the wake of traditional banking limitations, alternative finance has emerged in recent years as a diverse landscape of funding options. Peer-to-peer lending and crowdfunding platforms, among other innovations, now offer businesses and individuals access to capital through innovative channels.

These channels are further fueled today by the use of AI. A Forbes article delves into the transformative impact of AI on alternative financing, exploring its potential to reshape the financial landscape.

• Improved risk assessment and credit scoring: AI tools can spot trends, provide insights and use data to provide risk assessments that existing systems fail to capture.?
• Reduced fraud: AI systems could be trained on existing data and mobilized to empower lenders and loan applicants to identify fraudulent activity sooner and reduce waste tied to fraud.
• More time for employees to focus on value-added work: Tasks such as customer support, document verification, data entries and marketing communications, as well as regulatory compliance can all be automated over time.?

Read more about the role of AI in alternative financing here .?

Manufacturing becomes more "intelligent"

The manufacturing landscape is undergoing a significant transformation driven by the integration of advanced technologies, with machine learning (ML) emerging as a key driver of this change. As factories evolve into "intelligent" production centers, ML is impacting various aspects of operations, leading to increased efficiency, improved quality control, and even product innovation.

A recent article in Manufacturing Today India explores the specific areas where ML is making its mark:

• Enhanced quality control: ML algorithms trained on sensor data can now identify product defects in real-time, minimizing the risk of faulty items reaching consumers and reducing associated costs.?
• Predictive maintenance: By analyzing equipment data, ML models can anticipate potential failures, enabling preemptive maintenance interventions.?
• Process optimization: From energy consumption to raw material usage, ML can analyze vast amounts of production data to identify inefficiencies and optimize processes for increased output and cost savings.?

• Product innovation: By analyzing customer data and market trends, ML can identify emerging needs and preferences, informing the development of innovative new products.?
• Adaptive production: ML models can analyze real-time data and adjust production parameters to meet individual customer specifications.?

The article goes deeper into these applications, providing examples and discussing the challenges and potential of ML integration in manufacturing. For businesses seeking to remain competitive in this evolving landscape, understanding the transformative power of ML and exploring its potential applications is crucial.?

Planning for digital transformation in 2024

In a modern organization, every department is pressured to be efficient and is looking to invest for automation, data capabilities, and improvements in employee experiences, some of which could be addressed with generative AI. With the new year coming up, CIOs can reset and review what is included in a transformational initiative, take steps to avoid hype-inflated goals, and set reasonable priorities with the executive team for the year ahead.?

1. Create productive generative AI workstreams
2. Drive business impacts by closing operational and security gaps
3. Develop transformation leaders to drive more initiatives

Prioritizing the right balance of initiatives in digital transformation programs is critical, to avoid overpromising and under delivering.?

Read the complete guide to propelling digital transformation in 2024 here.

About RapidCanvas

RapidCanvas is a no-code AI platform for business users to go from idea to live enterprise AI solution within hours, reducing time to value by over 90%, when compared to traditional AI build-and-deploy processes. RapidCanvas creates out-of-the-box AI solutions tailored to your needs using our proprietary AutoAI technology. Our data science experts work with you to optimize the results to your satisfaction; we combine the efficiency of algorithms with the experience of human experts. RapidCanvas work with leaders in financial services, retail, renewable energy, and manufacturing.