This report was created to fulfil requirements for the Digital Technologies module of the Digital Management Masters programme at Hyper Island.  This report was written for presentation to the CEO of Connector.



  • A critical review of emerging technology trends from academic and professional literature.
  • Evaluation of how AI might impact the advertising industry.
  • Presentation of a solution using artificial intelligence to deliver value to Connector and its clients.



Technology is one of the most common tools used to support many aspects of marketing. Over the past few decades, different technologies trends are emerging and helping companies to improve the effectiveness and efficiency of marketing and advertising processes. This paper delves into these technology trends and examines their impact on the advertising industry.

The first section is a detailed analysis of emerging technology trends that are changing business.


The second part focuses on the application of artificial intelligence in advertising and the significant risks connected with the use of this technology in advertising. The last section highlights an opportunity to use artificial intelligence for increased creativity, helping client service staff to be more efficient handling some of the mundane behind-the-scenes work.

Part I



The world of technology is continuously changing in response to the need for more innovative and efficient technological applications and systems. Various studies such as Ismail et al. (2014) and Kelly (2016) have identified some recent and emerging technological trends that will have a profound impact on how people work, communicate and learn.


One of these trends is what Kelly (2016, 14) describes as ‘Becoming’, which is the trend whereby everyone and everything will be moving from a fixed set of products into a status where they will be constantly  upgrading the quality of these products and services (McAfee and Brynjolfsson, 2008, 38). Essentially, the trend of becoming is the hallmark of the process of technology, which keeps upgrading and improving itself so that change is an inevitable aspect of it. People will continue changing, and things, processes, and products will keep becoming newer and more refined because of technology.


A second trend is that of ‘Cognification’, which implies that soon, everything is likely to become smarter due to the continuous modifications and improvements (Dutta, 2014, 21). People will take advantage of artificial intelligence and use the knowledge and skills gained to make things and processes smarter using the cheaper and more powerful capabilities of AI that can be accessed from the cloud.


The third technological trend is that of ‘Flowing’, which refers to dependency on uninterrupted streaming of things in real time. This means that everything will be in the process of being improved and transitioning from its current form to the next superior and better state so that it will be like some stream (Forrest and Hoanca, 2015, 47).


Technology has set a platform that is inevitable to changes (Consumer Insight, 2017, 76). Every sector of the economy, including the very traditional ones like education, are fast embracing various elements of technology to improve and make the systems better (De Garis et al., 2010, 14). It is only a matter of time to the entire world will experience the effects of these general trends, the following section explores the role of AI and how this technology is at the core of the trends mentioned above and how it can impact the advertising industry.


Part II


5.0 Application of Artificial Intelligence in Advertising


Artificial Intelligence (AI) is a generic term that refers to the ability of computers to take on tasks that previously required human intelligence to complete – for example, speech recognition, playing games or interpreting data.


Machine Learning (ML) is a type of artificial intelligence and refers to the ability of computer programs to learn and adapt without specific programming – for example when the application receives new data for analysis. WARC Trends (2017, 1)




Over the past few years, artificial intelligence has permeated the world of marketing and is being used to enhance each step of creating brand awareness and customer satisfaction (Kaas, 2010, 76).


The current AI use cases in advertising are the analysis of business insights, customer relationship management (CRM) and media planning. According to WARC Trends (2017, 1), the processes of data mining and analysis, which are usually done manually can be executed more efficiently and rapidly with the use of AI. This way, AI can provide more informed outcomes for advertising efforts. With CRM, AI is becoming particularly important in the development of smart systems that can automatically respond to customer queries across different platforms and languages (Consumer Insight, 2017, 7).  


Moreover, most of the AI applications that were previously exclusive to enterprise level organisations have become reasonably affordable and accessible to small and medium-sized enterprises. This implies that many organisations are using AI to support various areas of business including marketing (Loosemore and Goertzel, 2011, 9).




As technology transitions from passive enabler to a more active participant in creative processes, new methods for incorporating AI for marketers are emerging.


AI can be used in advertising to develop  highly personalised and interactive web experiences and to enhance conversion rate optimisation (CRO). According to Luke and Salamon (2012, 12), intelligent algorithms can help in personalising web user experience by analysing multiple data points about a user.  These data points may include user location, search terms, demographics and device type. With this information, AI can display the most relevant and best fitting content.

As behavioural personalisation becomes possible, AI systems can be designed to push notifications that are specific to individual users. Perhaps, the most important application of AI in advertising is in the creation of content. Baum, Goertzel, and Goertzel (2011, 190) predict that in the next few decades, AI systems will be able to generate advertising content. The actual narrative and insights will be based on the format established by and will be specially designed to suit a particular audience or target market.


Smart customer engagement and churn prediction are other significant ways through which AI can be applied in advertising. Specially designed machine learning algorithms can help in identifying disengaged or dissatisfied customers who are about to leave for a competing brand (Bostrom and Sandberg, 2009, 143). Armed with this information, marketers can develop strategies for winning back the departing customers. AI-powered systems in this category can play a critical role in gathering relevant data that can be used to build predictive models for validating customer behaviours (Tegmark, 2016, 3).


AI can be used in advertising to develop recommendation engines. These are intelligent systems, which combine data from a variety of sources to conclude customer actions and behaviours (Hutter, 2005, 34). Recommendations engines are indispensable in marketing because they provide customers with a personalised experience that they can rely on to find products with minimum efforts.


AI virtual assistance will become critical touch-points for service-based brands. Chatbots and voice assistants such as recognition of brand names will improve significantly as marketers tap into AI for optimal returns from their marketing efforts automate conversations between marketers and customers (Rousselet, 2017, 6).


Creative support – Projects can use AI to analyse the visuals, sound and composition of hundreds of films, to evaluate them against data on consumer reaction and then to select the best scenes for editors to patch together into the ad. Ultimately reducing what could be a process lasting several weeks to just one day.


Insight Generation – Strategists can embrace technology and open up new areas of insight via advanced data mining, augmenting the capacity of synthesising large data-sets in meaningful information for creative insights.


Lastly, Generative AI models means that AI can learn how to mimic the data it’s been trained with.  “If you feed it thousands of paintings and pictures, all of a sudden you have this mathematical system where you can tweak the parameters or the vectors and get brand new creative things similar to what it was trained on” (IBM Cognitive – What’s next for AI, 2018) allowing people to test out new ideas and accelerate prototypes.




Many ethical issues might be associated with the adoption of AI in the advertising industry.


Transparency – One of the most pressing ethical issues is the failure to inform consumers about the techniques used to market to them. Intelligence systems gather vast quantities of data about customers (such as location, brand preference, search terms and device type), which is used to develop personalised advertising experience (Omohundro, 2007, 3). Unfortunately, consumers do not give their informed consent when these data are gathered because the process is automated. Thus, there are risks of breaching consumer confidentiality as their personal information is used without their knowledge. A typical tradeoff for this ethical issue is that customer information should be used only to enhance consumers marketing experience and not for malicious intentions.


Privacy – According to Goertzel and Pennachin (2007, 84), AI gives marketers the ability to understand consumers in more profound ways than they (customers) know themselves. The myriad of information collected by AI systems enables marketers to understand consumers’ vulnerabilities and motivations. Marketers can use this information to manipulate consumers through the art of behaviour control (De Blanc, 2011, 29).


Accuracy – AI systems may yield biased data, which may lead marketers to make flawed decisions leading to a violation of consumer privacy. Such an error can cause organisations to lose their strategic competencies especially because the intelligent systems are meant to reduce costs and enhance organisational performance. As such, the obvious biases in the AI can be a reflection of human preexistent biases that require effective measures in the data management process. The emphasis on ethics on the various avenues of the AI system is necessary and called upon to promote morally-based conduct in the advertisement process.

The most effective trade-off for this ethical issue is for the marketers to be aware that technology can be biased too and use multiple AI systems for analysing same data to identify and correct disparities.

Humanity – Another critical ethical issue is the loss of jobs as most business processes become automated. AI systems can perform many of the tasks performed by lower-level employees such as answering customer queries. Loss of employment can be an undesired impact of employing AI in advertising., Many people will object to the use of AI in marketing because it will lead to loss of jobscwhich is an undesired effect. No matter how much unwanted these systems are, many organisations are determined to implement the intelligent policies because they are more efficient and cost-effective than human beings. A possible trade-off for this ethical issue is to provide opportunities for these employees to upskill and develop in new areas.




According to Nagy (2010, 8), companies can use the technique of self-policing to circumvent many of the ethical issues associated with AI use in advertising. Due to the rapid change of technologies that support AI, it will be quite challenging to regulate its application in advertising. Thus, it is important to create best practices guidelines for business organisations to follow. These guidelines should outline the most common potential risks and the best ways of addressing them to make the best out of AI in advertising.




Artificial intelligence (AI) is proliferating into the world of emerging technological trends through the need for better technology in various fields (Barbara, 2014, 4). This technology is being utilised to enhance marketing decisions by making the data collection and analysis processes more efficient, less costly and timely.

By using AI technologies, advertising agencies have more opportunities to increase their output and reduce the costs. The main ethical issues associated with AI in advertising include loss of employment, breach of consumer confidentiality and lack of consumer involvement in marketing decisions. To overcome these challenges to adoption, it is imperative to blend AI from machines with the intellect from human beings. This will ensure that the virtues of trust and moral consideration are inculcated in the process as man and machine create a desirable future for all in advertising.


Part III – Research Activity

Bringing Value to Connector Through AI Adoption


About Connector

The author works at Connector – an advertising agency. Connector started as a digital marketing agency but in its recent past has been going through a transformation trying to move away from short-term marketing campaigns to more integrated digital experiences and products.




As presented in the second section of this report, AI can be used in advertising in different ways, and in a laggard market such as Ireland the early adoption and application of AI/ML can become a competitive edge for Connector creating benefits such as:


  • Creating hyper personalised experiences that deliver better results than competitors
  • Increasing sales conversions by using generative AI to explore new exciting ideas and accelerate prototypes
  • Reducing time spent on tasks by using AI as creativity support tool creating more billable hours
  • Cutting customer service costs for clients with high volumes of interactions by exploring virtual assistants
  • Opening up new areas of insight which couldn’t be reached by human analysts via advanced data mining




The author decided to focus on reducing time spent on projects by using AI as creativity support tool internally, as Connector is already actively exploring the other use cases for its clients. The process started with user interviews with five account managers responsible for recruiting new clients and managing internal and external relationships were the starting point of the research.


Figure 1. Interviews download session to extract key user needs


During the interview it was possible to gather information on the pain points that hinder them from acquiring new accounts and provide the team with insights that can lead to creating better work:


  • It is expected from account managers always to be prepared, impress clients from the very first meeting by being knowledgeable about their industry.  However, this research sometimes is impossible due to the number of tasks they have in hand.
  • During briefing meetings many times they struggle with the balance of active presence paying attention to client briefing meetings while also capturing all things said in the conversation.  This information is vital to share with the team which can hinder the quality of the work.
  • Generating a clear understanding of the client personality and working style is necessary to give the team guidance in how the output should look like. Unfortunately this understanding only comes after the relationship is already established.




As a result of the research phase of the opportunity above,  the problem statement was defined.


“How might we use technology to free up account managers to spend more time exploring new client opportunities and providing the team with better guidance so that they can deliver more value to all parts?”




Account managers rely heavily on their phones for most of their work-related tasks, the solution proposed for this project is a mobile app powered by AI which has features become an account manager’s favourite sidekick.


Klónos – The Account Manager Personal Assistant App


  • Scan client’s social media using linguistic analytics to understand how to tailor the meeting based on the client’s personality, prefered communication styles, areas of interest and mutual connections.  
  • Explore market reports, relevant campaigns, competitors key stats and information related to the client industry, having a big picture digestible in ten minutes to arrive informed at every meeting.
  • Use speech to text to listen to meetings, transcribing audio in real-time outlining conversations and next steps allowing people to focus on the business conversations and outcomes of discussion instead of taking notes.
  • Monitor meetings, listening to the audio as it’s happening and linking with historical and real-time content sending notifications to prompt account managers to ask relevant questions for the creative brief and discover more new business opportunities during the meetings.



In order to create a prototype, it was essential to understand the user’s journey and different needs from prospecting to keeping the relationship fruitful. After the research, the process moved to rapid prototyping as a way to create experiential and visual representations of the concept and features.

Visit the live interactive version on



The key highlight of the testing was to validate the concept but also understand what features had the best potential, to understand changes needed in the UI and see why some features were not well received by the users and their relationship with AI.   


  • All interviewees agreed on the usefulness of the app and stated they would like to start using it immediately.
  • The critical point that participants agreed is that while the personality insights functionally is interesting, it is inherently biased as it is based on data of what people decide to share online which can be different than reactions in real life.
  • Two of the test subjects felt that the personality insights feature can also lead to potentially negative meetings as it can lead account managers to become overly anxious in an attempt to fit client predicted expectations.
  • When asked if they would ultimately discard the feature, one of the respondents suggested the personality insights could be useful if used in another moment of the consumer journey. His suggestion was to use the personality analytics in the conversation data collected during the meeting and evaluate the outputs taking into consideration the account manager perception of the client, creating a more accurate picture of clients preferences.



After the user testing, it was possible to use MoSCoW prioritisation technique to define the importance of each requirement on the delivery of the idea.


  • Must Have: Listen to meetings and convert speech to text outlining conversations.
  • Should Have: Explore news and reports related to the client industry.
  • Could Have: Data powered prompts for account managers to ask relevant questions for the creative brief and discover more new business opportunities during the meetings.
  • Won’t Have this time: Client personality insights and prefered communication styles as the data sources are not perceived reliable enough, and the subjects felt it diminished their soft skills importance.




  • Trust – As an ethical issue entails that the moral obligation of the account manager to disclose that they are using the app to capture notes during the meeting and how this data is being collected, stored and evaluated in the analysis of personal information.


  • Control  – All data needs stored securely and giving full transparency and allowing clients have full power to opt out of the evaluation system.




The digital solution presented in this report respond to user needs and should be considered as a way to tackle the two main hurdles account managers face every day of being informed in every topic and adaptive to clients.


There is an opportunity to remove repetitive tasks and to enable account managers to work more efficiently, focusing on activities that deliver more value.


In the testing phase, the feedback revealed essential points for feature prioritisation and validated the need for this solution. However, additional rounds of testing with users outside the company would be useful to validate a more scalable solution.  


During the processes, it was observed that AI has the tremendous advantage of such high-level data processing powers supersedes the human mind. However, empathetic skills and the impact of the reaction to all this data needs to be taken into consideration in order to avoid reducing people soft skills to only algorithms.


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