A B2B marketer’s AI wishlist

by Megan Arnold

Like many of us, I spent the better part of 2023 absorbing the impact of generative AI, exploring its use cases, and considering its potential to transform the ways we work. As a marketer in the tech industry, generative AI (and in particular ChatGPT) became widely available at a time when there were widespread doubts about the near-term resilience of our economy, increased interest rates forced a reckoning in the world of venture capital, and layoffs in the industry became ubiquitous. For much of the year, these forces created a working environment of intense pressure to perform with more uncertainty, change, and the fewest resources available to do the work in years. 

I became somewhat obsessed with the potential for AI to automate and simplify many rote marketing tasks - copywriting, first drafts of blogs, or perhaps even the localization or customization of content, for example. While I do currently use generative AI in my day-to-day work, without the organization around it (the features, processes, systems, and workstreams), its ability to transform the ways marketing teams work is extremely limited. 

In the spirit of the holiday season, here’s my wishlist of ways I’d like to see marketing organizations adopting AI tools to drive efficiency, improve ROI, and reduce burnout for their team members. Emphasis on wish list, by the way. 

Self-service content development and production

One of the biggest challenges every organization faces - especially enterprises - is content management. Core content teams can’t possibly achieve the “right message, right person, right time” holy grail of digital marketing with current production costs. Messaging takes too long to develop, content too long to produce, and products and services change too rapidly for true personalization of content. Another challenge is that in a world where everyone has Powerpoint and a LinkedIn profile, every single frontline go-to-market professional (from sales to partnerships to marketing) is creating content, no matter how good or how big an enterprise’s content team is. Today, this proliferation of content comes with inconsistent branding, inaccurate product information, and a long tail of unused and out of date assets on content management systems. 

How many times have you heard an account executive ask for a solution brief or sales presentation deck on one of your products, but specifically speaking to C-level decision makers in the automotive industry for a niche use case (and written in German)? With today’s production timelines, no organization can centrally generate or control the thousands of versions of a single asset needed by go-to-market teams to address each customer’s unique pain points. 
A self-service content generation tool built on the back of a foundational knowledge base (brand guidelines, messaging frameworks, and technical documentation), a marketing team could empower their entire organizations to achieve true personalization and deliver the right message at the right time to the right person. 

AI tools built for marketers today already include the ability to generate copy (and in some cases design) by relying on libraries of existing brand documentation and assets that ensure the AI tool generates content that is both technically accurate and adheres to brand guidelines.

Imagine if your highly intelligent, experienced, and capable content teams could focus on building the core foundational knowledge base of your AI tool. You could then allocate more resources to a brand review team to rapidly review content for quality assurance. Even two or three week’s turnaround time on content review is light-years ahead of where we are today in the time it takes to produce content. 

A fully-integrated and automated AI-fueled program management system

Today, marketers open up millions of Asana tickets (or Monday or Wrike or Trello or Basecamp tickets, etc.)  around the world. And those same marketers spend millions of hours managing their program management platform, not producing the work. And, if you’re like me, you experience a deluge of email inbox notifications on tasks. We also spend millions of hours in planning meetings, trying to agree on not just the work to be done, but how to do the work and softly (or not) negotiating who is going to do the work and by when. Projects stall because team meetings have to wait 2-3 weeks until there’s a free timeslot in the group’s calendar. People in geographically distributed teams get left out when they’re in a distant time zone. 

I’d like to have a program management system that is a marketing platform multi-tool: it combines a content management system, has access to performance data and analytics, integrates with Outlook, knows the company’s org chart (and roles and responsibilities), and understands the time it takes for tasks to be done. 

Imagine telling this AI-driven platform the business problem you’re trying to solve, or the business impact you want to have (generate 3,000 marketing qualified leads from this set of accounts in the next 8 weeks, help my sales team use LinkedIn more effectively, etc.) and that platform gives you three ways to solve that problem based on historical performance of your company’s own marketing campaigns that generated similar results. 

Then, that platform identifies all the tasks involved in producing that campaign: the bill of materials, vendors required, the people involved, and the cadence of the workflow required to meet the business objective. This platform then schedules a kickoff meeting, records the discussion, synthesizes the feedback, develops the brief, and tees up tasks in order for the right people with all dependencies taken into consideration. 

The teams review the progress asynchronously by providing written feedback that the AI tool consolidates and shares, eliminating the need for excessive meetings and improving global collaboration. It reduces to almost zero the amount of time your teams spend managing the task versus doing the critical thinking work only humans can do. 

In this case, that critical thinking work - the kind only humans can do - includes setting business goals and KPIs, negotiating costs with vendors, negotiating project prioritization with internal resources, resolving conflict between team members, ensuring that the output of the project meets quality requirements, and communicating the delivery of the project and its impact to stakeholders. Last - and perhaps the most important work only humans should do - is the final decision to go live with the campaign. 

A system like this not only saves time and reduces the hidden costs of inefficiency, but it is also highly fluid. With every reorganization or restructuring, every role change, there’s often months where your teams are left figuring out how to do the same work they’ve always done but with different people and processes that haven’t been rebuilt. An AI-fueled program management tool could be managed on the backend to take these changes into consideration and fluidly adapt to where each of the humans on your team sit in the organization as it changes. 

Further to that - in enterprise marketing organizations - best practices often don’t make their way around the world as important learnings. A centralized AI-fueled program management tool like this would automate the knowledge sharing without cycles being spent by teams teaching each other what they did that worked so well, it would be immediately applied. 

Performance optimization & resource management

Today’s martech stack remains fragmented and unable to produce consolidated digital insights across the full customer journey. From social media performance to web analytics to lead progression, we can only derive isolated insights and performance metrics. Currently, significant investments in IT are required to build data lakes and corresponding interfaces to tell a single story of the customer journey. Part of a centralized AI-fueled program management tool would inevitably have to be integrated with all these reporting platforms to understand campaign performance against business goals to identify best practices. 

To some extent we have these capabilities today, but they are based on “hard” diagnostic metrics like peak time of day for web traffic, referral source, time spent on page, number of clicks, etc. AI promises insights based on softer metrics like identifying that a certain tone of voice produces more engagement, or videos with three speakers instead of two are watched more often, or web pages with images of your product lead to more sales. 

As ROI requires an understanding of not just results, but the investment against them, this program management tool would observe the work being done, the time it takes and the dollars spent. This should be anonymized in order to not be misused by leadership as a surveillance tool, but used to better understand how many hours producing a major event costs, or how many hours marketers in sales-facing roles spend talking to reps, or how many hours it takes to build and launch a webinar. 

Competitive intelligence and market insights

Using publicly available information only (earnings calls, product launches, financial statements, analyst reports, customer reviews, product listings, press releases, conference session recordings, executive social media, etc.) I’d love to see an AI tool that could analyze historical information on a company’s competitive environment and provide insights into its position in the market, its strengths and weakness, and its opportunities relative to its competitors. Perhaps this tool could even identify patterns in competitor behavior to provide probabilities on certain moves - likely acquisitions, future product launches, risks, etc. 

Today that work is done by consultants, corporate strategy teams, and analysts, but all are prone to bias and none can process the vast amount of information and synthesize it fast enough to remain relevant and useful. By no means am I suggesting AI is not prone to bias, but because it can process massive amounts of data, it is less susceptible to biases like confirmation, affinity, or anchoring bias. 

Corporate strategy is an enterprise’s function of understanding itself, it’s the firm’s center for self-awareness. An AI tool would allow corporate strategy to see the world more objectively, not only through the lens of itself, and ultimately lead to more informed decision-making and better preparedness to meet its customers’ needs. 

A commitment to being good

Artificial intelligence, especially when baked into the very systems that determine how we work and what work we do, is a tremendously powerful tool. Like many innovations that came before it, it has the ability to exacerbate our worst flaws, or to amplify our virtues. My last wish list item is that every organization, public, private, or personal, makes a commitment to being good, not just doing good. AI will reflect back to us who we are, and as a result the risk to AI is not in itself, it’s in us. 

I’d like to see companies commit to being good through the intention to do right by their customers and employees by leading with compassion, kindness, and wholeheartedness. I’d like to see companies take action on their intent with policies that support decision making based on goodwill and global citizenship, not just shareholder wealth creation. I’d like to see those companies follow through on their actions by basing formal incentive structures on more than business metrics. I’d like to see those organizations maintain healthy and continuous skepticism of themselves and a desire to always strive for goodness. And I’d like to see organizations hold themselves accountable and responsible when they make mistakes, despite their best intentions. We can only allow mistakes when we see a collective willingness from corporations to hold themselves accountable, and we will make many mistakes with AI. 

I’d like to see organizations truly celebrate goodness from the inside out, not just in holiday ad campaigns or employee giving programs, but that see their core mission as being good, manifested in the business of solving customer challenges and investing in new ideas.