Construction in Crisis! Written by Chrissie McAnelly, Cybit Data & Analytics Consultant – Construction Sector The construction sector isRead article
Is your organisation AI Ready?
By Rob Hankin – CTO – MD Data & Analytics
In today’s competitive landscape, organisations are constantly seeking ways to gain an edge, drive performance and minimise operating costs. Artificial intelligence (AI) is a transformative technology that holds immense potential to revolutionise operations and unlock success. However, successfully adopting AI and realising its benefits requires careful consideration and a well-structured approach.
Why Organisations Are Investing in AI
Organisations are turning to AI for a many reasons, including:
- Enhanced decision-making: AI can analyse vast amounts of data to identify patterns and trends that would be difficult or impossible for humans to detect, providing valuable insights for informed decision-making.
- Improved operational efficiency: AI can automate repetitive tasks, streamline processes, and optimise resource allocation, leading to increased efficiency and cost reductions.
- Personalised customer experiences: AI can analyse customer & stakeholder data to gain a deeper understanding of preferences and behaviour, enabling organisations to deliver personalised experiences and enhance satisfaction.
- Innovation and competitive advantage: AI can turbo-charge innovation by generating new ideas, discovering new opportunities, and developing novel solutions, giving a competitive edge.
Challenges in Adopting AI
Despite the compelling benefits of AI, organisations face several challenges in adopting and effectively utilising this technology:
- Data connectivity: Silos of data scattered across different departments and systems can hinder AI adoption, as AI models require access to connected, integrated data for accurate learning and insights.
- Data quality: Poor data quality, such as inconsistencies, inaccuracies, and missing values, can lead to flawed AI models that produce unreliable or misleading insights.
- Data strategy: Without a comprehensive data strategy, organisations may struggle to identify relevant data, ensure its quality, and govern its usage, hindering effective AI implementation.
Consequences of Misguided AI Adoption
Adopting AI without addressing these challenges can have detrimental consequences:
- Ineffective AI models: AI models trained on poor-quality data may generate inaccurate or irrelevant insights, leading to ineffective decision-making and missed opportunities.
- Biased AI models: If AI models are trained on biased data, they may continue or exaggerate existing biases, leading to discriminatory outcomes.
- Increased Costs: Processing unnecessary or irrelevant data can increase infrastructure costs and strain resources, diverting budgets from more beneficial investments and significantly lowering any return on investment.
Environmental Impact of Irresponsible AI Usage
The environmental impact of AI is a growing concern, as training and running AI models can consume significant amounts of electricity, leading to increased carbon emissions. Powering and cooling the vast data centers required for AI processing demands substantial water usage.
A 2023 study by the University of California Riverside in USA found that for every 20-50 queries ran by ChatGPT, the most popular of the GPT AI models, 500ml of fresh water is lost via cooling processes (https://news.ucr.edu/articles/2023/04/28/ai-programs-consume-large-volumes-scarce-water).
The Solution: A Strategic Approach to AI Adoption
To harness the power of AI and mitigate potential risks, organisations need to adopt a strategic approach that encompasses:
- Data strategy: A well-defined data strategy ensures data is collected, managed, and governed effectively to support AI initiatives.
- Data warehousing: A centralised data warehouse consolidates data from many different sources, providing a single source of truth for AI models.
- Data quality processes: Data quality checks and cleansing processes ensure data accuracy and consistency, improving the reliability of AI insights.
- Aligned AI models: AI models should be clearly defined and aligned with specific strategic objectives to ensure their relevance and impact.
- AI integration: Organisations should plan how AI-generated insights will be integrated into existing data platforms, reporting tools, and data visualisation tools to generate action from insight.
- Lifecycle management: A comprehensive lifecycle management plan ensures AI models are verified, maintained, updated, and optimised throughout their lifecycle.
Partner with Cybit for the AI Driven Future!
Cybit’s expertise in Data & Analytics, Cloud computing, Cyber Security, and Managed IT Support services can help organisations navigate the complexities of AI adoption and achieve their strategic goals.
With Cybit’s Managed Microsoft 365 and Copilot our experts are here to support and guide you through a highly communicative engagement, focusing on all areas of the Microsoft 365 suite to achieve great outcomes.
We support all of your Microsoft 365 products including Teams, OneDrive, SharePoint and Exchange to get the best out of each to meet your needs.We provide end-to-end solutions, including strategy development, solution implementation, health checks, and ongoing support.
Contact Cybit today to help unlock the transformative power of AI technology for your business. Together, we can empower your organisation to make informed decisions, enhance operational efficiency, and achieve sustainable growth.