AI for Content Management
Humans and machines have never been more in sync. Advances in deep learning using neural networks have increased the ability of computers to take on more complex tasks over time. In ways not previously possible, a human can lean on its machine sidekick to tackle tedious processes, manage repetitive tasks, and push the bounds of optimization and creativity, for starters. And that machine sidekick will get better at these specific tasks over time as it captures interactions with humans — a process called “active learning,” where humans collect intel on the behaviors of fallible machines that are then stored and used to update their model going forward.
This human-machine partnership is the core of augmented intelligence, a unique way to leverage artificial intelligence (AI). A human-centered partnership model that lets each side do what it does best. AI, for example, can compensate for human limitations by efficiently and effectively tackling tedious administrative tasks while humans can infuse empathy, imagination, and knowledge of the world around us into the equation.
With humans and machines working together, the resulting augmented intelligence delivers automated support that improves cognitive performance, decision-making, and personalization. And that’s helping organizations create better experiences at scale while reducing errors and freeing up humans to do more of what only they can do.
Using augmented intelligence to drive better outcomes
For many tasks, AI or machine systems can’t deliver at the same level as a trained human who thinks, feels, analyzes, and simply pays attention. That said, augmented intelligence use cases are continuing to grow, evolve, and lend value to brands and businesses.
Digital asset management (DAM) platforms, for example, are notoriously hard to manage given their often massive asset collections. Sorting, categorizing, and tagging the assets is a labor-intensive, time-consuming process. Beyond that, people have high expectations for how well the content is maintained — they want it to be fresh, relevant, and, for social platforms, well-moderated.
Enter augmented intelligence. AI can automate key parts of your content management workflows. Humans are still active participants in the process, creating assets, determining campaign priorities, and evolving future content. But because of the automation piece, marketers and creatives can spend less time on the tedium like tagging, sorting, and search, and instead focus on higher-value activities.
Adobe Stock is a great example. This image and video repository maintains a library of fresh and diverse content by ingesting hundreds of thousands of assets per day — a scale that’s feasible only because our platform is powered by Adobe Sensei, our AI and machine-learning engine.
Using the Adobe Sensei features in Adobe Stock, we can automatically filter submitted assets, ensuring they meet our quality standards and rejecting blurry, overexposed, or other lower-quality content. The AI system also flags or blocks content that might be against intellectual property standards — images containing logos or recognizable people who haven’t signed release forms, for example. These computer predictions are leveraged by humans in a new content/moderator interaction workflow that makes the ingestion process far more efficient than it was before. Adobe Sensei then learns from these human decisions, creating a virtuous cycle that continually improves our machine-learning models.
And Adobe Stock isn’t alone. Any content or creative platform that manages a large amount of user-generated content is also a great candidate for augmented intelligence integration — whether it’s detecting a copyrighted song that someone has just uploaded to YouTube or auto-filtering blog comments to simplify the job of the human moderator.
Better together: humans and machines
It’s clear that augmented intelligence is changing the human-machine game — and Gartner agrees. A recent Gartner report puts augmented intelligence in the “transformational” category, predicting it will reach mainstream adoption in two to five years and will ultimately provide more business value than any other type of AI — and that’s an exciting prediction with benefits for virtually every vertical and every data-driven brand.
For now, though, organizations can start to reap plenty of preliminary benefits. Already, in well-designed content systems, humans and AI can work together to cut costs and scale functionality as we’ve never seen before. The machine enables speed, volume, and consistency. Humans — who build and train the machine and adjust its outputs as needed — contribute in ways that only humans can, bringing to bear our right-brain capabilities like imagination, empathy, and storytelling. They just need to get “in the loop,” together.
Learn more about how humans and machines are working together to drive efficient outcomes.