AI business strategies—5 ways brands and agencies can properly use the tech

Artificial intelligence dominates industry discussions these days. While some are thrilled by the prospect of what the technology can achieve, others are unsure of how to proceed—and they are not alone. Where to begin? How to avoid making costly mistakes? Forty-four percent of respondents to McKinsey’s State of AI 2024 reported at least one negative consequence from AI. The key to success with AI is to take a tactical approach and think about how to use AI in a way that's strategic and sustainable for the business. With the legacy systems that brands and agencies have, improperly adopting AI can create organizational chaos. If parts of the business are trying to adopt AI and others are not, some employees can forge ahead without the right guardrails, putting IP at risk. Check out the latest Ad Age events and award programs At the same time, to push ahead in this brave new world, businesses need to get comfortable with failure. As former IBM CEO Thomas J. Watson put it, “The fastest way to succeed is to double your failure rate.” Some things will work, some won’t and sometimes the rug will be pulled away while the work is still going on; the change is that fast. To learn to live with failure, build a strategic and sustainable approach to AI and sidestep risk-induced inertia, companies should start with an AI audit. This will allow them to put the right foundation blocks in place for success and controlled failure. An audit—a systemic and holistic appraisal of specific business areas—will reveal what must change to adopt a sustainable, scalable AI strategy. Without the right culture, a business will go nowhere. C-suite support and literacy is essential. So is agreement on vision, investment and nature of experimentation. With the right culture, a business can more smoothly plan and execute the following five audit tasks. Data Many companies find themselves trapped by their data—either drowning in it or unable to access what they need quickly enough. If it’s taking too long to do mundane things such as personalizing an email, for instance, it can use AI to streamline the process. A root and branch understanding of a business’s data landscape and its approach to data is the first requirement. Governance and ethics What are the organization’s limits? What are the guidelines for AI governance and ethics? This is the stage to work out what AI uses are acceptable. Setting that up and finding a structure is vital. Without it, a company can find itself in the Wild West with people training on the wrong data or putting IP at risk. Legal and compliance What is a company’s process for assessing third-party AI tools? Is it aware of the latest legislation and consumer opinion around AI? Companies need a process that can identify and quantify risks to ensure that approved AI uses are compliant. Data (science) capability Businesses need practitioners who understand the math and science behind AI to mold open-source solutions to the organization and reduce the risk associated with generative AI. These same individuals ideally should have general data literacy to understand AI’s capabilities and apply the evolving open-source capabilities in innovative and disruptive ways. See all of the Global Automotive Marketing Awards winners here Content If not already doing so, companies need to turn their content into data. Vector databases will become key. Content data can then provide an unrivaled understanding of content performance, which can enhance product recommendation engines, customer profiling, forecasting, e-commerce search and SEO to name a few. Incremental benefits are endless. When using generative AI, a business needs to treat its content differently. It can’t just store core proprietary content on a file server. It also needs to be highly cautious about the images (and their origin) on which algorithms are trained. After an AI audit, a business can assess its readiness for the AI journey. Along the way, it should track KPIs established for AI success to measure the effect of every AI implementation. AI strategies can then be forecast to create a value-engineered priority road map that can create efficiencies and value quickly and sustainably. Ad Age Agency Report 2024 Revenue and analysis for agency companies Click here

AI business strategies—5 ways brands and agencies can properly use the tech

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