Enterprise Times met with Eoin O’Neill, CTO at Tug. The company is a global digital performance marketing agency supporting brands through smart combination of data, media, content and technology. The wide-ranging conversation explored the evolving role of CTOs, particularly in the context of technology, manufacturing, and eCommerce. The discussions highlighted the importance of data integration and the impact of AI and machine learning on business operations.
Eoin emphasised the significance of understanding how AI and machine learning models interpret and generate content. Furthermore, the necessity for organisations to develop frameworks for measuring the impact of their marketing budgets. The discussion also touched on the potential of low-code solutions to democratise technology.
Developing a measurement framework
Businesses need to develop a comprehensive framework to measure the impact of marketing campaigns and optimise data-driven decision-making. They need to focus on getting a true, connected view of performance data across different systems, such as CRM, marketing, and acquisition.
It is essential to integrate data sources to understand the full customer journey and the actual impact of your marketing efforts. Avoid relying solely on third-party platform data, as that may not provide the full picture. Build your own models and analysis to determine the causal impact of campaigns on business outcomes.
Invest in tools and solutions that can help enterprises understand your organisational data, such as entity optimisation, insight generation, and website content monitoring. This will give you better visibility into how your content and information is being interpreted. The key is developing a framework that allows you to confidently measure and attribute the impact of your marketing activities, rather than making decisions based on incomplete or disconnected data.
The evolving role of CTO
The discussion highlights a few key points about the changing role of the CTO:
CTOs need to continue hiring people who are smarter in technology than themselves. The focus should be on application and understanding how to leverage technology to solve business problems, rather than being the most technically skilled.
CTOs need to stay ahead of technological changes and understand how new developments like AI, machine learning, and low-code/no-code solutions can impact the organisation. This requires a strategic, forward-looking approach.
CTOs need to be able to navigate the different scenarios and challenges organisations face, such as data compliance, privacy, and integration of innovative technologies into existing systems. Flexibility and adaptability are key.
The CTO’s role is evolving to be more about democratizing technology and making it more accessible to non-technical users, rather than solely focusing on the technical implementation.
“CTOs need to maintain a balance between hiring the right technical talent and ensuring the technology serves the business objectives and customer needs. The key is for the CTO to focus on the strategic application of technology to drive business outcomes. Rather than being solely responsible for the technical implementation,” suggests O’Neill.
Democratising technology
O’Neill highlighted the impact of low-code and no-code solutions on democratising technology.
Low-code and no-code tools have the potential to make technology more accessible to non-technical users. This allows business managers and stakeholders to build applications and automate processes without deep technical expertise. This democratisation of technology can empower more people within the organisation to leverage technology to solve problems, rather than relying solely on IT or technical teams.
However, O’Neill cautioned that the scope and use of these low-code/no-code solutions may be limited. “They may not provide the same level of customisation and control as more traditional development approaches,” he added.
Concerns about data compliance and privacy also exist when these tools are used. Organisations need to maintain control over how data is handled and processed. Proper training, support, and governance will be crucial to ensure the effective and responsible use of low-code/no-code solutions within organisations.
“The key is finding the right balance between democratising technology and maintaining the necessary controls and compliance to protect the organisation’s data and systems. A strategic approach is required to leverage the benefits of these tools while mitigating the risks,” says O’Neill.
Looking under the AI bonnet
The discussion highlighted the significance of how AI and machine learning models interpret and generate content. O’Neills advises:
- It is crucial to understand how language models and AI systems interpret an organisation’s content and information. This allows enterprises to optimise their content and structure it in a way that is easily understood by these systems.
- Recognise the proper entity optimisation process. It is important to know where the organisation’s products, services, and business logic are clearly defined and understood by the AI models. This helps ensure the models provide accurate and relevant information to users.
- Utilise insight generation tools that can analyse the business’s content and performance data. These tools can help identify changes or issues that may impact how the AI models interpret the information.
- It is important to monitor website content changes and understand how they correlate with performance metrics. This ensures the organisation’s online presence is optimised for how AI systems interact with it.
- The goal is to structure your organisation’s content and information so that AI and machine learning models can accurately understand and represent the business, its offerings, and the customer experience.
By focusing on how the AI and machine learning models interpret and generate content, organizations can ensure their digital presence and customer interactions are aligned with the capabilities and limitations of these emerging technologies.
An ethical framework for AI
O’Neill agreed that a consensus-based standards or ethical framework is needed to address the challenges posed by AI-generated content. He suggested that such a framework should establish clear guidelines on the appropriate use of AI and LLMs to prevent misuse and malicious applications. It would also entail implementing mechanisms to ensure the accuracy and trustworthiness of AI-generated content. This could include verification and fact-checking processes. Promoting transparency around the use of AI in content creation, including clear labelling of AI-generated content.
Any framework should foster collaboration between industry, academia, and policymakers. It should create a shared understanding and set of principles for the ethical development and deployment of AI. In addition, it should develop robust systems to monitor and mitigate the spread of misinformation or disinformation generated by AI.
O’Neill concludes, “It is important to bring together various stakeholders to establish a consensus-driven approach. To balance the benefits of AI-generated content with the need to maintain trust and integrity in the information ecosystem,”
