2025 is shaping up to be a year of immense change across industries, and media organizations need to adapt to stay ahead in a rapidly changing advertising landscape. When we look at traditional large media entities, there is a lot of potential for growth, but there’s also an element of consolidation and cost cutting going on throughout many of these organizations.
Growth is going to be a little measured as we go through this year, and we are seeing a greater emphasis being put on things like Agentforce and AI-based applications to drive larger initiatives forward. There are many pain points that reps and clients are struggling with in ad sales today, so we’re going to explore a variety of Agentforce use cases that can streamline the process and help organizations do more with AI assistance.
Receiving and processing RFPs
The ad sales process starts with receiving an RFP. What do we do when we get an RFP and how is Agentforce going to help us here? This is an interesting use case because it is a combination of both Agentforce and MuleSoft, which is a fantastic technology that is also part of the Salesforce ecosystem. Those two technologies together allow us to leverage functionality like Intelligent Document Processing. You can now take one or multiple files in any format, feed that into an IDP engine, and then have Agentforce parse through all of that data and summarize the key campaign details.
Instead of reviewing dozens of different pages of RFP material or emails that went back and forth with little pieces of a campaign, you can get a nice, clean summary around what is this campaign, what are the goals, and what are the KPIs the client is actually looking for, reducing the amount of time you have to spend in that upfront RFP review process. Additionally, Agenfforce can recommend things to clarify or responses that need to be sent back through the RFP response process, leveraging Agentforce and its generative AI functionality to help craft those emails and reduce the amount of time that is spent putting those together in the back-and-forth communication.
This can also be done in a self-service environment. As opposed to a rep being behind the scenes working this deal, everything is happening through a self-service environment where RFPs are being submitted and parsed out through a back-and-forth interaction with an Agentforce agent to better define what the goals of their campaign are.
Media planning and proposal creation
Media planning and proposal creation is the next step in the process where we would be looking at Agentforce, and this is something that is also on the roadmap for Media Cloud as a product to develop a media planning assistant. Now that we understand what the overarching goals of this campaign are, what should we sell? How should we build a plan to best meet those goals?
This is where we can look at agents that can pull data from anywhere that you want. If we’re looking at order management systems, ad servers, and other inventory or pricing tools that you’re using internally to store all this data, these can all be tied into an agent. The agent then looks at the KPIs of this campaign, what is available to sell, what is available within this client’s budget, and what has performed well for this specific client or similar clients in the past.
The agent uses all of that information to provide recommendations on what you should be proposing from an ad product perspective based on all of these factors, eliminating the guesswork of what you can sell or eliminate. Instead of needing a seller to reach out to different parts of the organization for expertise surrounding the RFP, a lot of that information can all be consolidated, streamlining the media planning process. The rep gains a great idea of the plan they will need to build.
Negotiation and IO creation
Negotiation and IO generation is where you can leverage a lot of the generative AI functionality that comes with Agentforce. Let’s say you have an email going back and forth to craft how you respond to a particular ask from a client surrounding things like discounting and campaign flights. You can leverage Agentforce to better craft those responses and make them available for someone to then tweak after the fact to customize based on any particular need that the client may have. Once you get through the initial negotiation, Agentforce can craft the insertion order for you as well and send that off to the client for signature through an e-signature tool like Docusign.
Campaign setup and launch
Now we’re into campaign setup and launch. So how do we make the transition from the sales process to the post-sales process more seamless? This is where we can use Agentforce to summarize everything that we have been talking about to date into campaign briefs that can be handed off to an ad ops team to say here’s the insertion, here’s everything that was discussed, here’s how the plan evolved over time and the discussions that were held, and here’s what we actually need to now deliver and execute on.
This is a great use case, because often this is where we see things really start to break down between the two sides of the house due to the sheer number of changes in information that is available. It’s often difficult to convey back to your ad ops team that this is what we found other than what’s explicitly written in the IO. Leveraging the generative AI functionality from Agentforce to build out these campaigns briefs helps in the handoff to post-sales teams and is a valuable use case we can look at outside of the core sales piece here as well.
Campaign optimization and reporting
Campaign delivery is a huge part of the success of every single organization, but how do you stay on top of what a campaign is doing, how it’s performing, and when it is either underperforming or potentially overperforming, how do we know what modifications to make here? Too many impressions is just as bad as not enough impressions, and we want to be hitting that sweet spot of delivering exactly on the initial commitments to our client, but also proactively notifying individuals responsible for ad trafficking about issues we may see before they actually become glaring problems for the campaign.
This is where we pull in all of this campaign delivery data through a variety of systems and tools like Marketing Cloud Intelligence, which is already aggregating a lot of this data. You can take all of that information and then drive action from it, so the agent provides those types of recommendations to the trafficking team. It can say this product isn’t working, try swapping it out for this one. Or this particular tactic is working better in this format than another, so shift ad dollars throughout a campaign.