Penned in and cornered by GenAI promises? You’re not alone. As vendors crank out invention after invention, enterprises need real answers, not buzzwords. Here’s your no-fluff guide to automation tools that transform content workflows — tested, vetted, and ready to deploy.
As the inventive duo, Wallace and Gromit might attest: while a nifty gnome improvement gadget may promise garden-variety miracles…
…putting too much trust in the chirpy claims of smart AI tools could land you in a feathery predicament (or should we say, a rather fowl situation). After all, when it comes to reasoning, there’s often more than meets the AI.
Recap: A year of highs & lows
Generative Adversarial Networks (GANs) took the world by storm with startups sprouting in just about any segment you can imagine — not to mention the chatbot mania flooding headlines and social feeds.
On the multimodal front, OpenAI launched its cutting-edge GPT-4o and Sora models, Anthropic introduced a formidable rival in Claude, Google DeepMind announced its Genie 2 large-scale foundation world model, while Meta and Mistral AI unleashed an open-LLM market.
From classrooms to workplaces, students and knowledge workers, still drunk on ChatGPT euphoria, were gifted mesmerising gadgets of computational magnitude to add to their cheat sheets and workflows. Meanwhile, passive investors saw staggering returns from Exchange Traded Funds — most notably, iShares U.S. Technology ETF up +31% YTD.
Yet, however convincing the deep learning revolution has been, institutional adopters have faced significant challenges — from accuracy and security concerns to integration difficulties and prompt engineering complexity, as noted in McKinsey’s 2024 CXO survey. Moreover, a litany of lawsuits stoked AI-nxiety fears — with some companies devouring data at the expense of ethical reason.
Fast, free, sparse, exponential… OpenAI has been mesmerising users, but an ominous business model is unfolding.
Nevertheless, practical implementations have shown promise with notable gains in code generation and information retrieval. Examples include GitHub Copilot’s adoption at Google and Microsoft, and JPMorgan Chase’s deployment of GPT-4 for Quest IndexGPT.
Following the industry’s USD 36 billion investment in 2023, and with tech titans Google, Microsoft, Apple, Meta, and Amazon all throwing billions of dollars into closed- and open-weights models, what are the leading developments we can expect in the next five years?
Today, over 100 real-world applications and potential use cases of generative AI are primed to stir attention in 2025.
A multitude of modalities
Listed below are 6 modalities adapted from a foundational 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).
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 to address underperforming areas.
3 charts to inform your diagnosis
A 2024 McKinsey Global Survey places GenAI adoption at around 72%, almost doubling from sentiment reflected in the previous year.
Interestingly, GenAI is most prevalent in 3 functions: marketing and sales, product/service development, and Information Technology.
Adopters should consequently expect more budget runway in areas that prioritise customer experience and user experience. Moreover, generative AI is currently most prevalent for use cases involving: content support, personalisation, prioritisation, prototyping, and conversational engagement.
In contrast, the most flagged risks facing enterprises over the past year have ranged from model management (i.e. inaccuracy or hallucination), to data management (i.e. privacy, bias, and IP infringement).
Augmenting the content workflow
Taking a big picture view, how might such advanced tools weave together?
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-4o, Copilot, Claude, Gemini and Mistral— 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 in the content lifecycle
Categorising AI tools according to 3 enablers 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 further 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.
As Wallace might say:
“Get me up, Gromit. Another great year of inventing beckons.”
Feature image by Negative Space
