There is growing pressure on brands to reduce their carbon footprint and adopt greener practices while meeting performance targets
The sustainability practices of companies – particularly advertisers – have never been under greater scrutiny. Carbon emissions are an increasingly critical focal point for industries worldwide, with the UN’s Global Compact considering CO2 reductions an essential part of tackling climate change – one of the 17 Sustainable Development Goals (SDGs). Yet, even with several initiatives designed to promote sustainability, there is still a high level of dissatisfaction – even within the advertising industry itself – about the impact advertising has on the world around us.
But while there is growing pressure on brands to reduce their carbon footprint and adopt greener practices, they're also compelled to continue to meet their performance targets. With budgets under pressure, doing less with more has become a necessity. As such, streamlining processes and enhancing efficiency are top priorities for leadership teams.
Many advertisers are looking to AI tools to meet the demands of the growing complexity of media buying and campaign management, while also acting as a key pillar of their sustainability strategies. After all, studies have shown that AI optimization can reduce the CO2 emissions of advertising campaigns.
And AI has much more to offer. When applied to marketing functions, it can transform how teams operate, giving leaders the ability to make quick, data-driven decisions. AI tools can be used in areas such as audience targeting, content creation, and performance analytics. A recent study by MNTN and AdExchanger found that 92% of brands and agencies see AI’s biggest use as improving the efficiency of existing processes while increasing productivity and outputs was also a major potential application.
But AI comes at a cost so businesses must ensure that they are not wasteful. AI also requires enormous amounts of energy, so organizations must choose carefully which use cases they apply it to, or risk taking a step backward in their carbon reduction efforts. For example, Google set itself the ambitious goal of reaching net zero emissions by 2030, but its most recent sustainability report cited the impact of AI as a key reason that its carbon emissions actually grew last year.
Organizations must take a strategic approach to AI to define where these technologies can bring value, rather than trying to apply them to every use case. Getting this approach right, while striking a balance with environmental responsibilities, requires businesses to address four key areas.
Firstly, businesses must have the right people in place. A diverse set of AI skills is required, but even more importantly, these people must have the correct mindset. While it's crucial that they can build innovative workflows and are prepared to experiment with AI, they must also understand when it's appropriate to use AI – and when it isn't.
Secondly, organizations must have a strong data strategy. Marketers rely heavily on data to inform their decisions and to help them optimize campaigns and measure performance. But without the right tools, getting actionable insights from this data is tough. Although AI offers a solution to this challenge, the output of AI is only ever going to be as good as the quality of the input, so having a robust infrastructure that can manage large volumes of data securely and efficiently is critical.
Thirdly, the business must have a clear vision of what its overall objectives are to fully understand what it can achieve with AI. Building AI-enabled processes relies on identifying SMART business goals and ensuring all relevant teams within the organization can align KPIs and collaborate efficiently to reach these goals. This step is essential for ensuring AI is only applied in scenarios where it can deliver genuine value. Businesses that have done this well often designate a business analyst to develop achievable SMART goals, such as reducing churn by 3% over six months or increasing ROAS by 2% across six campaigns. These objectives have a clear KPI and timeline – they are digestible, measurable, and aligned with the overall business strategy.
Finally, the technology itself has to be fit for purpose. The AI toolset must directly support the company's specific objectives, marketing included. Organizations must ask themselves which solutions they need, such as predictive analytics, machine learning, natural language processing, large language models or other AI technologies. Integrating AI tools that aren't needed would be wasteful; Deloitte research shows that marketing teams only use 56.4% of the martech tools they have available to them, and it is vital that businesses don't continue this trend with AI to further compound this issue. Additionally, IT teams may find that they sometimes design requirements too specific to their immediate needs, limiting the typical benefits of AI implementations. This then means that they end up developing in-house tech solutions that, over time, become far too expensive and reliant on niche skill sets.
How these tools are implemented is just as important as selecting the right ones. AI technology must integrate well with existing systems and be scalable and secure to accommodate growth and protect data. The best way of achieving this is to build a common layer where all AI tools can feed into the existing technology stack; connecting individual tools one by one on a piecemeal basis is likely to create inefficiencies and more internal siloes.
Through taking a responsible approach to technology, businesses can not only achieve their sustainability goals but drive efficiencies with tangible benefits within their organization By eliminating redundancies, marketing teams can increase their focus on high-impact activities, leading to time and cost savings that ultimately result in more effective campaigns.
It's also important that organizations think about how looking at AI through the lens of sustainability can help them improve partnerships and create change in the wider ecosystem. There are additional efficiencies that can be found through the simplification of supply paths, for example, by cutting out unnecessary intermediaries and fraudulent MFA (made for advertising) websites. Working with only partners and vendors that share similar principles when it comes to sustainability and responsible use of AI is another way of achieving meaningful carbon reduction. To ensure these relationships are fully transparent, they must commit to regular auditing so they and all their partners can see the impact their activities – whether enabled by AI or not – are having.
With a typical ad campaign emitting around 5.4 tons of CO2, organizations must weigh up the impact that AI will have on its carbon footprint. While AI can help to drive efficiencies and lower emissions, overusing AI could be counter-productive. Businesses must be discerning in their use of AI to deliver better performance without compromising their environmental responsibilities.
Driving process efficiency shouldn't come at the expense of sustainability, and brands that commit to monitoring the CO2 consumption of every campaign and take a strategic approach to technology implementation can make progress on both fronts.
You May Also Like
