Greg Logan, founder of Narrativity
Image: Supplied
Brands spent decades building unique voices, hiring expensive agencies, and crafting messaging that stood apart from competitors. Machine learning tools effectively eliminated that differentiation within about six months.
Companies are feeding generic prompts into ChatGPT, Claude, and Jasper, then publishing the results that come back. The result reads like corporate Mad Libs—interchangeable phrases about "solutions," "synergy," and "driving value." Every brand sounds identical because they're all using the same language models trained on the same mediocre marketing copy that flooded the internet for years.
Greg Logan, founder of Los Angeles-based consultancy Narrativity, sees the problem daily. After three decades of working with Adobe, Google, Netflix, P&G, and Qantas, he developed a methodology for translating Hollywood storytelling formulas into brand narratives. His newly published book, Creating a Blockbuster Brand, offers 11 frameworks that businesses can use to craft distinct stories. Yet he watches companies throw that work away the moment they hit "generate."
Machine learning tools offer speed and scale, producing content volumes that are impossible for human teams to manage. Marketing departments have slashed budgets and headcount, allowing algorithms to handle website copy, social media posts, email campaigns, and product descriptions. Nobody stopped to ask what gets sacrificed when efficiency becomes the only metric that matters.
Differentiation died because machines optimise for patterns, not personality. Language models analyse billions of existing texts and generate outputs matching statistical probabilities. They produce the most common phrasing, the safest metaphors, the blandest adjectives. Everything regresses toward the mean.
Logan's methodology does the opposite. His Quest formula asks three questions: What do you do? Who do you do it for? And what value do you add to their lives? One British business funder answered, "Become the number one independent funder for SMEs." Logan told them it was terrible. Their target audience—small business owners drowning in financial stress—couldn't care less about market rankings.
They rebuilt the Quest as "relieving business owners from the pressures they're under." Within six months, they became number one. The formula worked because it stopped the company from telling the story it wanted to tell and started telling the story customers wanted to hear.
Machines can't make that distinction. Algorithms don't understand tension, emotion, or what audiences actually care about. They regurgitate what already exists. Logan's 11 frameworks—Genre, Enemy/Superpower, Quest, Tagline, Tone, Controlling Idea, Synopsis, Love Story, Backstory, Logline, Hero's Journey—require human judgment about what matters emotionally to specific audiences.
Companies revert to their comfort zone when machines offer an easier path. They become overly rational, sound like competitors, and focus on themselves rather than customers. They satisfy what they want rather than what customers need. Algorithms accelerate that regression because they have no stake in whether the output connects.
Logan delivers 12 distinct core messages and stories to clients, all in a unique tone of voice. Businesses then apply these across websites, videos, brochures, pitches, and social media. The frameworks teach them to fish rather than handing them a single meal. His Brand Story Lab runs for our days, bringing all key decision-makers into one room to co-create narratives using cinematic formulas.
The process can't be automated because it depends on tension. Logan challenges the biggest misconception in marketing—that you can never say anything negative. Wrong. Movies without tension, drama, or enemies would bore audiences to death. Starting brand stories with what customers hate most hooks them far more effectively than happy product descriptions. Audiences think "you get me," then listen to how you solve their problem. Suddenly you're relevant.
Machines avoid negativity because they're trained on corporate communications that sanitise everything. Logan bans corporate buzzwords—"solutions," "leverage," "customer-focused," "journey"—calling them meaningless waste.
Algorithms would never generate those phrases. They'd flag them as inappropriate, tone-deaf, risky. Yet Logan's clients are processing billions in applications, and securing record funding rounds proves the method works. Logan finds that around 80% of entrepreneurs cry when he presents their new stories back to them. They'd always known their business was strong, but never expressed it so powerfully.
Narrativity now helps clients integrate their unique voice into machine learning tools so outputs sound human rather than robotic. The technology becomes useful only after humans establish the core narrative structure. Feed generic prompts into ChatGPT, and you get generic copy. Feed it frameworks grounded in emotion, tension, and customer-centric storytelling, and the tool can scale that voice across channels while maintaining consistency.
Companies face a choice. Let machines default to mediocrity, or use them to amplify already-strong narratives built on a structure that actually works. Logan's three-act framework—beginning, middle, end—provides the foundation every movie uses to hook audiences. George Lucas, Steven Spielberg, and Quentin Tarantino all rely on identical formulas despite making vastly different films.
Businesses can apply the same rigor. Position customers as protagonists, not your brand. Identify the enemy customers hate most—inertia, complexity, prejudiced perceptions, broken promises, toxic norms—then demonstrate your superpower defeats it. Create a Quest that puts the audience's needs first. When Athena Home Loans fought customer inertia with "powerful shortcuts," they processed $2 billion in applications within 18 months.
None of that emerges from prompting a chatbot to "write compelling brand messaging." Machines lack the judgment to know what's compelling. They optimise for what's common. Peer-reviewed research published in SAGE Open and the Quarterly Journal of Economics confirms audiences retain story-structured information significantly better than data-only presentations. Yet, most brands continue to feed algorithms requests for bullet points, feature lists, and rational proof points.
Logan's methodology stops that cycle. His frameworks force businesses to lead with emotion, hook audiences with tension, position customers as heroes, and deliver transformation through narrative arcs. Machines can scale that approach once humans build it. Companies just have to stop defaulting to whatever the algorithm spits out first.
The alternative means surrendering differentiation entirely. Every competitor can access the same tools, generate the same outputs, and publish the same hollow messaging. Marketing becomes a race to produce the most content rather than the most meaningful content. Audiences tune out because nothing connects.
Brands that reclaim narrative control—by building frameworks grounded in how humans actually process and remember information—can utilise machines as amplifiers rather than replacements. Logan proved that the model works across hundreds of clients, ranging from Fortune 500 companies to start-ups, and across various industries, including pet food, palliative care, and dating apps. The formulas apply universally because they're based on how stories function, not industry-specific tactics.
Machine learning tools are here to stay. They'll get faster, cheaper, and more capable. Companies that treat them as shortcuts rather than tools will continue to produce forgettable content, wondering why their efforts fail to resonate. The ones that recognise technology amplifies whatever you feed it—garbage in, garbage out, or structure in, compelling narrative out—will stand apart from the noise their competitors keep generating.