TL;DR: This article explores the first of the three distinct eras of marketing and technology that will shape the future: the AI Experimentation Era (2024-2026). It examines the defining characteristics and shifts of the Experimentation era, drawing insights from marketing professionals and experts. The section also identifies and explores the strategic imperative that marketers should adopt to stay competitive during the early stages of AI’s impact on marketing.
- The AI Experimentation Era: initial foray into multi-modal AI for marketing
- AI Learning and Development: Building an AI-Enabled Organization
The Era of Experimentation
This current era can be characterized as one of experimentation, with individuals and organizations exploring Large Language Models (LLMs) and Diffusion Models. This exploration occurs on personal, professional, and enterprise levels – with generative AI functions being integrated into existing enterprise platforms. These advancements are already providing tangible benefits for marketers and their agencies, enabling:
Francesca Hills, PHD Global President, Diageo – Interviewed June 2024
- AI-powered copywriting: LLMs generate marketing copy, product descriptions, and social media posts, rapidly producing tailored content for different audiences and platforms.
- Visual content generation: Diffusion models create product mockups, ad campaign storyboards, and branding inspiration, allowing quick iteration of visual concepts.
- AI-assisted audio production: Generative AI produces synthetic voiceovers and background music for ads and videos, enabling efficient and cost-effective audio content creation.
- Marketing tool development: AI assists in coding chatbots, personalization engines, and data analysis tools, streamlining the creation of custom marketing software solutions.
These early applications of generative AI demonstrate how it is already starting to transform various aspects of marketing.
Our research indicates that during the Experimentation Era, the belief in generative AI replacing people in most executive functions remains relatively low. This is most likely due to the fact that the impact AI will have on business, certainly over the next few years of the experimentation era, is a story of displacement, not replacement.
According to research from WARC, the greatest impact will be on the generating function – 42% believe the function will be enhanced by AI. The people who carry out this function as a core part of their day-to-day jobs will typically transition to spending more time carrying out Developing and Judging functions, with 26% and 29% citing the need for retraining for this transition.
This has implications for the type of talent required, where the focus is less on the ability to generate concepts – in the form of words, videos, and images – and instead on the ability to work with the generation of this content by AI, developing from it.
This shift is already happening for creative and creative-production teams working with generative AI creative platforms like Adobe Firefly.
In the media domain, this will increasingly be the case for strategic planning teams working with AI embedded within strategic workflows, such as Omnicom’s Omni.
Although the changes to the Generating function were the most likely in this first experimentation era, other executive functions started to emerge that would start to be impacted.
For example, although only 17% of respondents predicted that the Judging function will be replaced by AI within the period to 2026, it does indicate a coming change – for later eras. AI will be used for an increasing number of day-to-day functions that require human judgment and ‘decisioning.’
This will liberate individuals to take on more Developing functions where greater value can be extracted. An example is in the application of automated media buying and creative optimizing operating platforms – from third-party platforms such as Google Performance Max or building bespoke bid-factor algorithms, augmenting existing ML approaches.
This requires new capabilities of the individual – working with the AI to supervise, refine, and improve its function.
And with this change, we will see the need for skill development starting to rise. This transition from Generating and Judging to Developing can be seen in the fact that 30% of respondents cite an immediate need for training and development across all executive functions, placing emphasis on organizations to focus on the definition of the new capabilities required from each individual and the learning and development program to support them on that transition.
However, not every executive function is going to be impacted.
The Sensing, Visioning, and Judging functions are perceived as more resilient to AI, with only 9%, 14%, and 14% of respondents, respectively, believing generative AI will replace these functions – across all three eras. The priority is for organizations to build an AI learning and development agenda to enable the transition to an AI organization.
AI Learning and Development: Building an AI-Enabled Organization
As AI continues to transform industries, organizations must prioritize investing in their employees’ understanding and application of AI in their day-to-day work. This is particularly crucial in the marketing sector, where AI is poised to have a transformative impact on both administrative and knowledge-based tasks, such as strategic and creative development. As marketing and marketing services companies embrace AI, developing and/or licensing AI technology will be essential. However, equally important is the development and transition of individuals to work alongside this technology, emphasizing the significance of AI learning and development.
Through extensive research and conversations, we have identified three fundamental strands of investment required for effective AI learning and development:
1
Foundational Knowledge:
It is crucial to ensure that individuals within the organization have a solid understanding of this emerging area. This includes comprehending the differences between various AI models, such as Language Learning Models (LLMs) and diffusion models, and how they are incorporated into existing enterprise platforms.
Providing insights into the potential utility of these platforms for efficiency and effectiveness is also essential. To support this, organizations should maintain an online repository of regularly updated information with the latest developments, allowing employees to stay informed and up-to-date.
2
Working with
AI:
Hands-on training on how to use AI within specific roles is vital. This includes working with enterprise-level AI platforms, where individuals learn how to refine and make judgments based on AI outputs, leveraging their unique skills in the process.
Additionally, training should cover the use of third-party platforms made accessible by the organization, focusing on the skill of prompt engineering. As part of this strand, guidance on ethical AI usage should be provided to ensure responsible implementation.
3
Organizational Development:
A consultative-based workshop program for leadership is necessary to assess the current functions in place and determine how they need to change and what new roles are required for the future. This involves considering the implications for recruitment, development, and transitioning between roles. To support this process, organizations should develop a range of assets, including a consistent taxonomy of required roles and job descriptions, guidance on where to find suitable candidates, recruitment support, interview resources, onboarding programs, and ongoing development programs.
By investing in these three key areas of AI learning and development, organizations can effectively build an AI-enabled workforce, ensuring that employees are equipped with the knowledge, skills, and tools necessary to harness the power of AI in their daily work. This proactive approach to AI learning and development will not only facilitate a smooth transition toward AI adoption but also foster a culture of innovation and continuous learning, positioning the organization for long-term success in an AI-driven future.
Experimentation Era Imperative – Training and Skills Development
The key imperative for the marketing industry that emerges from this experimentation era is learning and developing the skills to enable individuals to elevate with AI. This is going to be as important an area of focus over the next few years as technology investment was over the last few years.
Clarissa Season, Chief Experience Officer, Annalect – Interviewed June 2024
These are the four areas of development that are likely to be required. Although they are currently a focus for some organizations today, they are likely to become increasingly more significant over the next few years as we make the transition towards 2030:
Re-skilling: Executive Functions that Require Development
- Sensing (Intuition and Theory of Mind)
Developing the capacity for intuition and theory of mind is also critical. Intuition allows for sensing the broader context of a situation, while the theory of mind enables a deeper understanding of client or stakeholder needs beyond the briefed information. This trait is invaluable in those who possess it, enabling them to excel and drive businesses forward. Client or stakeholder management is somewhat ‘therapeutic,’ requiring the ability to understand and respond to individual needs—a distinctly human component. Engaging with a ‘sentient being’ is crucial for this therapeutic interaction.
In the post-2030 era, this is arguably one of the most vital functions. Training and developing this skill is essential, yet it is often not optimized within organizations’ learning and development programs, with the psychological aspect frequently overlooked. Addressing this will be crucial for the future. This leads to the topic of cross-organisation connectivity, specifically the area of in-housing versus outsourcing. - Developing (Executive Direction)
Specific training is necessary to assess outputs from generative AI. We should not merely accept the outputs but maintain high standards for the platform’s performance across text, code, image, audio, or film. The role of the generator is evolving into that of a developer and decision-maker. Essentially, all individuals will take on the role of [strategic/creative] executive directors, focusing on judgment, refinement, and ultimately, decision-making. This crucial skill set must be developed to ensure the organization leverages AI for superior outcomes, not just satisfactory ones. - Judging (Decision-making and Conviction)
The generative process shifts the emphasis to decision-making. Individuals must judge the correct path forward from a wealth of viable options. Discernment and decision-making will be key skills. Helping people depart from prior knowledge and commit confidently is a challenge for organizations.
The training and development of individuals is as crucial as building AI infrastructure or procuring third-party enterprise AI platforms. However, this area often receives less attention. Organizations prioritizing individual development will apply AI to their functions more effectively than their competitors, allowing them to accelerate ahead. This focus is essential during the Experimentation Era and lays the groundwork for the subsequent Acceleration Era because that’s where we’re going next.
Slavi Samardzija, CEO, Annalect – Interviewed June 2024
Enterprise AI | Case Study: Omnicom’s Omni
Compiled by Clarissa Season & Kristin Reagan – May 2024, Edited by CS July 2024
Major advertising holding groups have invested in marketing technology to support their organizations. Over the last two years the focus has been on platforming generative AI. This case study outlines Omnicom’s approach with ‘Omni’.
Omnicom has been at the forefront of Artificial Intelligence (AI) and Machine Learning (ML) for more than a decade. Grounded in ethics and responsibility, all associated capabilities and products are underpinned by Omnicom’s AI Code of Conduct and Responsible AI Program.
Omni – the industry’s first marketing orchestration platform – comprises a suite of proprietary applications that integrate with marketers’ first-party data and technology across the entire workflow: Strategy & Planning, Audience & Media Activation, Content Development & Delivery, and Measurement & Optimization. The platform provides a collaborative and unifying solution for teams across Omnicom — covering creative, media, commerce, precision marketing, public relations, healthcare, and more.
Omni Assist is an end-to-end AI-powered virtual assistant embedded throughout the entire Omni workflow accessible to all Omni users. Omni Assist is powered by Agents, or specific applications of Generative AI, that provide insights, notifications, and recommendations across every step of the Omni workflow — from audience development and planning to activation, measurement, and optimization.
The integration of Flywheel Commerce Cloud into Omni enables AI-powered intelligence and investment optimization across retail and brand media, as well as digital and in-store commerce.
Future development will focus on autonomous analyses of datasets leveraged through automatically generated marketing workflows and outputs. As a result, Omni will dynamically surface observations and actions by anticipating end-user objectives and requirements. Further Omni developments in the pipeline include:
- Omni Assist Agent Store. This hub for Omni’s suite of Agents will provide a one-stop-shop for users to quickly find the Omni Assist Agents that are most relevant to their specific business challenges. Additionally, the Omni Assist Agent Store will provide a path for users and teams to contribute (or add) their own Agents, further expanding the range of available options.
- Omni’s chat interface will deliver an enhanced user experience. Users can communicate an objective and Omni Assist will guide them as they work towards achieving that objective. Omni Assist will also make it easier for users to create and navigate their own distinct Omni journey.
- Omni will become fully automated. Through advanced algorithms and analysis, Omni will proactively generate observations based on a comprehensive brand profile, team activity, app usage, campaign history, and business objectives.
- Omni modular components. Auto-generated modules will dynamically generate insights based on live campaigns, team activity, and user-level/role-based engagement. These modules will not only provide valuable insights, but will also suggest cultural trends, audience targeting recommendations, influencer suggestions, and creative inspiration.
