AI for B2B Marketing
AI for B2B Marketing
The B2B context
It’s early days for the use of AI in B2B marketing. The nuanced nature of B2B means that it’s not as straight forward to apply AI – especially genAI – as it might be for consumer brands where a high volume of content needs to be delivered at speed and scale.
The decision makers we target are a hard-to-reach, sophisticated audience who preside over long sales cycles and complex buyer journeys. This doesn’t mean AI isn’t relevant, but it does need a carefully considered and strategic approach to ensure we don’t sacrifice quality for quantity.
We recently caught up with Scott King, Principal Strategist, Industry for APAC at Adobe, Karen Powell Omnipresence Group CEO, and our own Creative Director Adrian McNamara as expert voices to share their thoughts and discuss the opportunities and challenges with our audience of B2B marketing leaders.
What did we learn? There’s certainly strong interest and curiosity in the unprecedented potential of AI to transform our worlds. There’s also caution about the unknowns – how do we trust the outputs? What does it mean for the creative process? How will it conform to regulatory requirements? What if it goes wrong?
The truth is we’re already using AI in ways we don’t even realise and soon it will be everywhere. Which means now’s the time get onboard. But how? Read on to find out.
Contents
The state of AI adoption
AI’s popularity and potential is clear: ChatGPT smashed the record for the fastest growing consumer app in history when it reached 100 million users in its first two months. Compare that to TikTok, which took about nine months after its global launch to reach 100 million users, and Instagram which took 2.5 years.
A show of hands during our panel discussion revealed that most are new to AI – they’re experimenting in small ways but are yet to use the technology for specific purposes. Individuals are dabbling more than organisations.
The research1 tells us that on a global scale, Australia is more nervous about products and services using AI than the global average – 69% compared with 52%. Japan was the least nervous at 23%, the US came in at 63%, while our APAC counterparts Indonesia and Singapore rated 48% and 53% respectively.
1Ipsos Global AI 2023 Report
Hesitancy about AI
Australia
69%
Global average
52%
An issue of trust
Trust is the key issue behind users’ caution:
- Where is the output being sourced?
- How is the output being used?
- How will organisations combat misinformation?
Best practice dictates that AI must be enterprise-safe, underpinned by accountability, responsibility and transparency. This is even more critical for highly regulated organisations, or those servicing them – think government, financial services and healthcare.
Organisations are already building their own GenAI or Large Language Models (LLM) and training them on their closed information ecosystems. This goes to verifying outputs and applying the tools through an ethical lens that aligns with company values.
Getting started and building team comfort
While Australia is a relatively late adopter, integrating AI into your workplace needs to be a considered, tested and strategic process to win the trust of employees as well as clients. A good place to start is with a use case:
- Pick one specific area. Select a process that’s not business critical or one that is labour-heavy.
- Drill down into each step and test it out.
- Attain approval then roll it out on the next use case.
- If you’re part of a global organisation, offer to use Australia as a test case for a particular function. Once the kinks are ironed out, Australia will be the designated specialist in that function and can deploy it to other regions.
Another option is to build a digital twin that runs alongside BAU. A digital twin is an exact replica of a single or series of business functions that runs in isolation from the rest of the business. AI is tested on various operations, adjustments are made and once the results verified, the AI functionality can be implemented into the live business.
How one organisation gained the trust of its team :
The team was asked what they loved about their job. They were then asked what got in the way of that. AI was applied to the obstacles to allow humans to focus on what gave them satisfaction. An AI playground was created allowing the team to experiment freely and safely. The net result: a higher rate of adoption because they trusted their employer to use the new technology responsibly and in their best interests.
Impact on the creative process
While there’s comfort around how AI can deliver significant time and cost savings, the impact on the creative and content production process delivers less comfort, especially in complex B2B sectors.
So much of the B2B landscape is fuelled by thought leadership, which is informed by SME knowledge to deliver a perspective on how value is created for an audience. By understanding that knowledge and unique point of view, creative value comes in how this is crafted into concepts, words and design to capture and convince the audience of our proposition. Forming these unique points of views and propositions require human skills, emotional intelligence and experience. Tone, personality and style are human characteristics.
No doubt some tasks can be performed by AI but nuance and true emotion are the differentiators and they require a human touch. Our experts all agree that the human element is critical.
Where AI comes in is to augment these creative processes by accelerating and automating production tasks – think shortening music tracks and resizing images. AI is then used to scale the creative and copy across multiple channels and touchpoints, allowing iterations and adjustments at an unprecedented depth and pace to enhance optimisation.
SEO will become less relevant as search moves to GenAI-powered search. We’ll start asking AI for advice, instead of Google. The challenge then becomes how to place your brand in all the different GenAIs. Partnership activity is already gathering pace between information vendors (such as News Corp in Australia) and AI generators.
Efficiency vs effectiveness
The speed and efficiency with which AI can perform various functions is one of the technology’s most appealing features. Automating laborious tasks such as formatting or summarising documents are gains heartily welcomed by many teams. It frees them up to perform other tasks, and the efficiencies deliver significant cost savings.
However, solutions must still resonate with customers and that’s where the human element remains critical. Think of it as quality control or your last line of defence.
Similarly, AI allows personalisation to be applied not only to content but to the customer journey. Historically, it’s been difficult to unpack the organic paths that buyers take to reach conversion. AI is using thousands of data points to understand those paths, conversion stickiness, retention and blockers. AI can do this at scale and then orchestrate multiple personalised journeys to improve flow through.
As we rely more on digital journeys, we should recognise that face-to-face engagements will become more important in B2B to keep customers connected – we are still doing business with humans after all. The more of the process that is executed by AI, the more ways you’ll need to meet with customers in real life.
AI can generate audience groups, segments and cohorts. It can be prompted to generate an audience that’s likely to convert at a higher propensity for this particular product with a given timeframe. This takes modelling to a whole new level.
Tips on tools
There are hundreds of AI tools now available and new functionality and tools emerges daily. Some will go the distance while others have already failed. The key is to determine which tools are most relevant to your business. The existing technology stacks you use today will already incorporate AI, so be sure to engage with your vendors and understand what they offer. And remember that some tools will require training on your organisation’s data, brand and tone of voice, which is best done in your closed environment.
These are some tools our panellists have used which you might want to experiment with:
ChapGPT and Microsoft Copilot
Start by creating internal process documents before testing on market-facing material.
Copy AI
Is an integration layer.
Relevance AI
An Australian firm that is building an AI workforce.
Firefly
Adobe GenAI for creatives.
GONG
A revenue intelligence platform.
Otter
A meeting assistant, note taker and transcription service.
Jasper
An AI copilot for enterprise marketing teams.
Midjourney
Image generation.
Relevance AI’s pitch is that it helps organisations grow their business, not their headcount. Consider its SDR capability – it identifies the leads, does the research and handles communication right through to the point of negotiation. This is zero touch sales. The first human engagement is at the point of negotiation. Net gain? Redeploy the money spent on sales to product development and service.
Enterprise GTM stacks already infused with AI:
CRM:
Marketing Automation:
Sales Engagement:
Customer Data Platform (CDP):
Revenue Operations:
Predictive Analytics:
Watch outs
- Use content authenticity technology.
- Consider having a copyright lawyer review relevant output, some may even indemnify your work.
- Minimise the risk of misinformation or failing to meet regulatory requirements by training your GenAI/LLM on your organisation’s closed ecosystem. Training it on your digital asset management system (DAM) also ensures outputs adhere to brand and style.
- Similarly, using a free service means that tool can use your content to train its model – which in turn
will be used by others. - Humans remain critical as a sense check and quality control.
Adobe has a content authenticity capability built in. If you generate a copy or image from Adobe’s AI it is meta tagged that it has been created by AI. From that point forward, the history of the image
and how it’s been modified can be tracked.
In summary
In much the same way that the internet is all around us, AI will become a core part of our world before we know it.
Marketing teams are ideally placed to address the risk and opportunity presented by AI. Strong briefing and communication skills, cross-function relationships, and understanding of revenue across the business work hand-in-hand with assessing and delivering AI functionality for your businesses. This presents a huge opportunity for marketers to take the leadership position in driving AI projects, in much the same way as driving other digital projects like web, CRM and MarTech.
For B2B marketing teams, your SMEs and brand positioning are critical and always will be. Strong positioning and magical solutions are delivered by human creativity and emotional intelligence. AI helps you to produce that magic at speed, at cost, and at scale – we need to manage this within the bounds of brand governance and trust. The opportunities are exciting.
