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Home » Generative AI: The Pros and Cons and which tools to use for propelling businesses into the future.

Generative AI: The Pros and Cons and which tools to use for propelling businesses into the future.

By Khytsse Garcia

Artificial Intelligence (AI) has emerged as a game-changer, with its power to revolutionize the way companies operate and compete in the global marketplace. Among its many branches, AI is gaining significant attention for its potential to shape the future of business operations. This advanced technology, fueled by powerful machine learning algorithms, could generate content, designs, and solutions, enabling companies to optimize their processes, enhance creativity, and achieve unprecedented efficiency.

AI tools have the potential to streamline processes, increase productivity and enable new forms of artistic expression, however it’s important to consider ethical implications. In this article, we will explore some of benefits and challenges that comes with our changing world of Generative AI.

Benefits of Generative AI

Enhancing Creativity and Innovation – AI tools can generate a wide range of possibilities by enabling teams to get inspiration from content and designs, brainstorm new ideas, and by unlocking new avenues for growth with AI generated suggestions. Examples:

  • ChatGPT – A language model for generating human-like text.
  • Notion AI – A premium productivity and note taking web application.
  • Open AI Playground – Enables quick & easy build of test predictive language models.
  • Ayoa – mind mapping software that includes online & kanban boards, task management.

Accelerating Data Analysis and insights

Data plays a central role in in driving effective change and transformation, however with an overwhelming amount of volume of data, it can be time consuming and complex. AI tools can analyse vast amounts of data, identifying patterns and extract meaningful insights. Examples:

  • Tableau – An Interactive data visualization tool based on business intelligence.
  • Microsoft Power BI – A collection of apps & services that work together to turn data into interactive insights.
  • Polymer (No coding needed!) – Creative visualization & dashboards.
  • Akkio (great for beginners) – A business analytics and forecasting software

Process Optimisation and Automisation

AI tools can analyse workflows, identify bottlenecks, and propose optimisation strategies. Examples include:

  • Clarifi – helps customers choose the right options by analyzing images and videos.
  • Fyle – Automates the expense management process.
  • MorphL – Develop digital products and create personalized experiences.
  • KalendarAI – Automate customer outreach by using chatbots to book meetings.

Predictive Analysis

Business success and sustainability relies on companies being able to predict and forecast future trends, market shifts and customer demands. AI tools can leverage historical data and make accurate predictions in an efficient manner. Some tools that can help are:

  • H2O Driverless AI – The company’s H20 Driverless AI simplifies AI development and predictive analytics for both experts and citizen data scientists through open source and custom recipes.
  • Microsoft Azure Machine Learning – Management of the predictive analytics lifecycle
  • RapidMiner Studio – Simplify and clean data from a diverse set of sources and incorporate into predictive modelling workflows.
  • SAP predictive analysis – Enterprise Predictive Analysis for supply chain, logistics and inventory management.

Risk Assessment

Transformation efforts often involve taking calculated risks. AI tools can assist businesses in assessing and mitigating potential risks associated with change initiatives. These tools can simulate various scenarios, evaluate the impact of different decisions, and provide insights into potential risks and rewards.

  • Logicgate – Automating risk and compliance processes through its Risk Cloud Platform.
  • Fusion Risk Management – anticipate, prepare, respond and learn about risk management.
  • MindBridge – An AI-powered risk discovery platform for financial integrity.

Considerations and Challenges of AI

Ethical considerations

Generative AI raises ethical concerns, particularly regarding the creation of deepfakes, misinformation or biased content. The potential of misusing generative AI technology has the potential to harm individuals, organizations, and society. Similarly, to when introducing social media and search engines, businesses and governments will need to consider establishing robust guidelines and ethical frameworks to mitigate these risks and ensure responsible use of these tools.

Intellectual property and copyright issues

The generation of content, videos, images, and other by-products of AI raises concerns about ownership and copyright. Businesses will need to look at establishing clear guidelines and better understand how they navigate through legal requirements.

Accuracy of content

Generative AI content creation is sourced from various platforms, which means the quality and accuracy of information is ‘as good as you put in’ to the tools. It is not fool proof and can produce erroneous content. Businesses will need to consider the implications of the content that is distributed using these methods.

Important Note

The ability to adapt, innovate and transition teams in the most appropriate way will become even more critical for success. As companies transform into the future with AI, it is important to ensure you include Change management and transformation professionals into your resource plans.

For my Change & Transformation buddies – I found this insightful link on the main challenges and opportunities for change management.