As an acquisitions editor at O’Reilly, I spend appreciable time monitoring our authors’ digital footprints. Their social media posts, talking engagements, and on-line thought management don’t simply mirror experience—they instantly impression e-book gross sales and reveal promotional methods price replicating. Not surprisingly, a few of our best-selling authors are social media experts whose posting output is staggering. Maintaining with a number of superposters throughout platforms shortly turns into unsustainable.
I just lately constructed an AI answer to handle this problem. Utilizing Relay.app, I created a easy workflow to scrape LinkedIn posts from one creator (let’s name her Bridget), analyze them with ChatGPT, and ship me weekly electronic mail summaries about her posts and which received essentially the most consideration. The principle purpose was to comply with what she mentioned about her e-book, adopted by thought management in her area. The setup took 5 minutes and labored instantly. No extra periodically reviewing her profile or worrying about lacking essential posts.
However by the second abstract, some limitations turned obvious. Sorted by likes and impressions with generic summaries, each LinkedIn publish was receiving the identical remedy. I had solved the knowledge overload downside however now wanted a technique to extract strategic perception.
To repair this, I labored with Claude to show the immediate into one thing nearer to an agent with fundamental decision-making authority. I gave it particular targets and determination standards geared toward shedding gentle on promotional patterns that aren’t all the time straightforward to comply with, not to mention analyze, in a flurry of posts: autonomously choose 10–15 precedence posts per week, prioritizing direct e-book mentions; examine present efficiency towards historic baselines; flag uncommon engagement patterns (each constructive and unfavourable); and routinely modify evaluation depth primarily based on how posts are performing.
The brand new report now supplies deeper evaluation centered totally on posts mentioning the e-book, not simply any widespread publish, together with strategic suggestions to enhance publish efficiency as a substitute of “this had essentially the most likes.” Suggestions are sorted into short-term and long-term promotion concepts, and it has even proposed testing novel methods similar to posting brief video clips associated to e-book chapters or incentive-driven posts.
The report isn’t good. The historic evaluation isn’t fairly proper but, and I’m engaged on producing visualizations. On the very least, it’s saving me time by automating the supply and evaluation of data I might in any other case should get manually (and probably overlook), and it’s starting to offer a place to begin for understanding what has labored in Bridget’s promotional program. Over time, with additional work, these insights may very well be shared with the creator to plan promotional campaigns for brand spanking new books, or integrated into bigger comparisons of promotional methods between authors.
Whereas engaged on this, I’ve requested myself: Is that this an AI-enhanced automated workflow? An agent? An agentic workflow? Does it matter?
For my functions, I don’t suppose it does. Generally you want easy automation to seize data you may miss. Generally you want extra goal-directed, versatile evaluation that leads to deeper perception and strategic suggestions. Extra of a useful assistant working behind the scenes week after week in your behalf. However getting caught up in definitions and labels generally is a distraction. As AI instruments develop into extra accessible to everybody within the office, a extra priceless focus is present in constructing options that tackle your particular issues utilizing these new instruments—no matter you may name them.
