Hurtling into the year of the Dragon, enterprises face a challenging scenario: adopt automation tools or risk being left behind by more innovative companies that harness the augmented potential of GenAI.
With so many products on the market claiming to deliver AI superiority, and a clear need for automation to enhance workflow throughout the content lifecycle, this summary of neoteric tools can help both startups and established enterprises evaluate scalable solutions for their unique content needs.
A multitude of modalities
Generative Adversarial Networks (GANs) took the world by storm in 2023 with startups sprouting in just about any segment you can imagine — not to mention the chatbot mania flooding headlines and social feeds. Today, as of this writing, over 100 real-world applications and potential use cases of generative AI are primed to stir attention in 2024. Listed below are 6 modalities adapted from a recent McKinsey report, illustrating the broad range of functionalities.
Encompassing each modality awaits a world of infinite automation possibilities, immersed in deep learning and augmented intelligence.
GenAI tools for content automation
Grasping the complexity of such use cases can be a daunting task. To optimise parts of your content lifecycle, consider the leading vendors listed in the document below. Each tool relates either to McKinsey’s modality taxonomy, or to a particular enabler (based on a representative content lifecycle).
A crucial concern is whether some of the AI startups listed above will still be around in 3-5 years’ time. Hence the need to scale incrementally by conducting pilot projects to determine compatibility and adoption potential.
Contextualising the automation problem space
Before diving into solution mode, consider the content automation context. What is your problem space? What kind of challenges are you trying to address? How might your team reduce effort, personalise UX features, or add analytics?
Framing your problem space will pave the way for a more precise automation solution that addresses underperforming areas.
2 charts to inform your diagnosis
Marketing and sales leadership sentiment appears to be heading in the direction of lead identification, marketing optimisation, personalised outreach, dynamic content, and up/cross-selling recommendations.
Interestingly, GenAI is most prevalent in 3 functions: marketing & sales, product/service development, and service operations. Adopters should consequently expect more budget runway in areas that prioritise customer experience.
Moreover, generative AI is currently most prevalent for use cases involving: editing, personalisation, summarising, forecasting trends, and conversational engagement.
Augmenting the content workflow
Reducing effort along the content development journey, from brief to delivery, can save teams significant time and energy.
On review of generative AI features currently available on the market, the below task flow helps diagnose which parts of the content development process to prioritise — outlining numerous automation components that can be applied along a retrospective journey of the content lifecycle.
During the conceptual phase, Natural Language Processing (NLP) and Machine Learning can facilitate better analysis, planning, governance and audience segmentation.
When creating and iterating content, NLP and Large Language Models (LLMs) — such as GPT-4, Co-pilot, Bard and Claude — can be used for writing generation and text enhancement.
When it comes to content delivery, conversational AI tools — such as answer bots and Retrieval Augmented Generation (RAG) — can be applied for responding to user questions, collecting feedback and tracking engagement.
Lastly, machine learning and RAG can be leveraged as analytics tools to monitor site usage and user behaviour as well as track how users interact with content across target platforms.
GenAI applied to the content lifecycle
Categorising AI tools according to 3 enablers (advisory, creation, curation) can ensure you address the end-to-end spectrum throughout the content lifecycle.
Advisory involves conceptual planning and analytics; creation includes the iterative journey for editorial and design projects; and curation covers how content is delivered to the audience (i.e. activation and user experience).
With so much disruption gearing up for the year ahead, taking a step back to contextualise your problem space in relation to the above mentioned modalities and enablers can work wonders in steering your content automation vision.
Feature image by Negative Space
