Throughout my career, I’ve cultivated a deep appreciation for the practice of bookmarking articles and posts. This practice has enabled me to amass and retain knowledge across various subjects, whether it be in organizational oversight, crafting revenue strategies, or exploring financial management.
I know I’m not alone. We all accumulate fundamental wisdom over our careers, enriched by learning from opportunities, technological advancements, cultural shifts, and environmental events. In the ever-evolving news and digital media industry, preparing for the unexpected is crucial, requiring us to understand how and when to adapt effectively.
Recently, an epiphany has reshaped my perspective. What if I could synthesize and apply the wealth of knowledge I’ve amassed throughout my career, the content I’ve diligently studied, and even the articles I’ve saved and bookmarked in one seamless strategy? This introspective journey led me to a profound realization about generative AI, enhancing my perspective on its potential and application.
Initially, the concept felt theoretical, but it soon revealed its significant potential. I envisioned harnessing generative AI to amalgamate career-spanning learnings into practical revenue strategies. Thoughts of organizational structures, compensation plans, mission statements, value propositions, close ratios, and DMA strategies flooded my mind.
Theory and practice
However, I must note that this is not something that would only work for me or another individual. It suggests an approach that has the potential to revolutionize the way digital news organizations operate, paving the path for a new era of data-driven decision-making, innovation, and growth while also representing a paradigm shift that could reshape the competitive landscape, ushering in a more efficient, agile, and forward-thinking business environment.
By harnessing generative AI’s power to synthesize accumulated knowledge and adapt it into practical revenue strategies, organizations can streamline their approaches, enhance innovation, and excel in a rapidly changing environment.
Consider an example of a hypothetical organization aiming to boost advertising revenue in a specific designated market area. Generative AI—armed with local business data, industry insights, and demographic information—can be used to draft strategies suited to the organization’s unique characteristics and needs.
It might also be used to help craft strategies tailored to specific circumstances, leveraging real-time and historical data. For example, it can be used to systematically flesh out specific details regarding go-to-market strategies, revenue planning, or quarterly and annual goals and break them down into weekly and monthly output objectives in an organized format. These sorts of applications demonstrate the possibility for generative AI to help media organizations shape their futures.
Generative AI is a rapidly developing technology with the potential to revolutionize the way we develop and execute strategies. While it is still in its early stages, the evidence of its practical applications and case studies is undeniable.
Of course, it is important to caution that any theoretical or draft strategies developed by leveraging generative AI should be well vetted and assessed among peers and committees. Not everything that generative AI suggests should be implemented, and you should not disregard your instincts and experience-based reasoning. However, the evidence of its potential is clear. Neglecting the potential of generative AI for strategy means potentially missing out on invaluable insights and efficiency gains.
Like knowledge, generative AI is a tool. And both must be leveraged effectively, in the right hands, with the right guidance to have a positive, significant impact. If you’re considering or hypothesizing about how generative AI may be leveraged within your organization, consider first establishing a guiding North Star mission—a central theoretical outcome that offers purpose and direction beyond mere intentions.
From there, generative AI can then be leveraged to create or enhance multiple distinct revenue strategies, consolidating them into one comprehensive approach. This AI-driven approach allows for the crafting of specific prompts that guide generative AI to execute precise tasks, all aimed at achieving the overarching goal.
Generative AI’s ability to tailor strategies to specific circumstances and prompts could be a game-changer in the world of digital media revenue strategy. It may provide a level of precision and adaptability that isn’t always readily available, especially for startups.
For example, a senior vice president of sales or a chief revenue officer could create a prompt requesting a step-by-step plan to increase EBITDA by 5% and achieve an annual advertiser account and revenue growth of at least 10% would utilize every available dataset and avenue effectively. With this information, you can further clarify and expound upon the North Star mission. This means breaking down goals and objectives into tangible, actionable terms, and translating improvements into practical implications for your organization.
While I can only provide a high-level summary of the most important results and insights from my theoretical exercise, it’s important to note that everyone’s needs and circumstances will vary. For instance, when I created a North Star strategy for a made-up organization and expanded it, I received hypothetical guidance on achieving specific revenue goals. This guidance encompassed expected percentages and insights based on the data used in my prompts.
Viewing it from a startup perspective, I prompted generative AI on CPM, pricing structures, and product offerings strategies. Generative AI provided valuable input, considering margins, commissions, and salaries. It also offered industry-specific advice and recommendations for advertising in the area specified. Additionally, it provided insights into salary expectations for sales and editorial staff, vital for an organization’s growth, and suggested strategies to increase website traffic and expand our audience base.
Reflecting on this experience, I found the process valuable for my strategic thinking. What stood out was that the information’s quality hinged on the input. To utilize generative AI effectively for strategy, understanding its limitations, investing time in learning, and acknowledging the input’s importance are crucial. Proper training, domain expertise, and adaptability are key in determining generative AI’s value for less-experienced users in news media or any field.
Generative AI is already transforming various industries, including digital news media and startups. Through interactions with news organizations of all sizes, I’ve realized that despite naysaying to the contrary, our industry is abundant with innovation, enthusiasm, and focused development. We’ve heard a lot about its use for content creation. However, generative AI can uniquely contribute to streamlining and illuminating the process of uniting innovative ideas, creative concepts, and revenue-generation strategies into one cohesive overarching strategy centered around a clear organizational objective—a guiding North Star for long-term industry sustainability.
While larger companies often have ample data resources, startups and organizations at different stages may not. And every organization can benefit from streamlining knowledge-based processes. Generative AI can be a powerful tool to guide decision-making and provide insights that may otherwise be elusive.
I encourage you to take small steps. Experiment and pivot with the wealth of information and ideas at your disposal. Let your imagination run wild. It’s time to transform our knowledge into a deployable strategy, as the potential awaits exploration. This journey is just beginning, and collectively, we are all in the process of discovery.