Publishing

Scaling AI in Moderation: From Promise to Accountability

September 19, 2025 by Basil Puglisi Leave a Comment

TL;DR

AI moderation works best as a hybrid system that uses machines for speed and humans for judgment. Automated filters handle clear cut cases and lighten moderator workload, while human review catches context, nuance, and bias. The goal is not to replace people but to build accountable, measurable programs that reduce decision time, improve trust, and protect communities at scale.

The way people talk about artificial intelligence in moderation has changed. Not long ago it was fashionable to promise that machines would take care of trust and safety all on their own. Anyone who has worked inside these programs knows that idea does not hold. AI can move faster than people, but speed is not the same as accountability. What matters is whether the system can be consistent, fair, and reliable when pressure is on.

Here is why this matters. When moderation programs lack ownership and accountability, performance declines across every key measure. Decision cycle times stretch, appeal overturn rates climb, brand safety slips, non brand organic reach falls in priority clusters, and moderator wellness metrics decline. These are the KPIs regulators and executives are beginning to track, and they frame whether trust is being protected or lost.

Inside meetings, leaders often treat moderation as a technical problem. They buy a tool, plug it in, and expect the noise to stop. In practice the noise just moves. Complaints from users about unfair decisions, audits from regulators, and stress on moderators do not go away. That is why a moderation program cannot be treated as a trial with no ownership. It must have a leader, a budget, and goals that can be measured. Otherwise it will collapse under its own weight.

The technology itself has become more impressive. Large language models can now read tone, sarcasm, and coded speech in text or audio [14]. Computer vision can spot violent imagery before a person ever sees it [10]. Add optical character recognition and suddenly images with text become searchable, readable, and enforceable. Discord details how their media moderation stack uses ML and OCR to detect policy violations in real time [4][5]. AI is even learning to estimate intent, like whether a message is a joke, a threat, or a cry for help. At its best it shields moderators from the worst material while handling millions of items in real time.

Still, no machine can carry context alone. That is where hybrid design shows its value. A lighter, cheaper model can screen out the obvious material. More powerful models can look at the tricky cases. Humans step in when intent or culture makes the call uncertain. On visual platforms the same pattern holds. A system might block explicit images before they post, then send the questionable ones into review. At scale, teams are stacking tools together so each plays to its strength [13].

Consistency is another piece worth naming. A single human can waver depending on time of day, stress, or personal interpretation. AI applies the same rule every time. It will make mistakes, but the process does not drift. With feedback loops the accuracy improves [9]. That consistency is what regulators are starting to demand. Europe’s Digital Services Act requires platforms to explain decisions and publish risk reports [7]. The UK’s Online Safety Act threatens fines up to 10 percent of global turnover if harmful content is not addressed [8]. These are real consequences, not suggestions.

Trust, though, is earned differently. People care about fairness more than speed. When a platform makes an error, they want a chance to appeal and an explanation of why the decision was made. If users feel silenced they pull back, sometimes completely. Research calls this the “chilling effect,” where fear of penalties makes people censor themselves before they even type [3]. Transparency reports from Reddit show how common mistakes are. Around a fifth of appeals in 2023 overturned the original decision [11]. That should give every executive pause.

The economics are shifting too. Running models once cost a fortune, but the price per unit is falling. Analysts at Andreessen Horowitz detail how inference costs have dropped by roughly ninety percent in two years for common LLM workloads [1]. Practitioners describe how simple choices, like trimming prompts or avoiding chained calls, can cut expenses in half [6]. The message is not that AI is cheap, but that leaders must understand the math behind it. The true measure is cost per thousand items moderated, not the sticker price of a license.

Bias is the quiet danger. Studies have shown that some classifiers mislabel language from minority communities at about thirty percent higher false positive rates, including disproportionate flagging of African American Vernacular English as abusive [12]. This is not the fault of the model itself, it reflects the data it was trained on. Which means it is our problem, not the machine’s. Bias audits, diverse datasets, and human oversight are the levers available. Ignoring them only deepens mistrust.

Best Practice Spotlight

One company that shows what is possible is Bazaarvoice. They manage billions of product reviews and used that history to train their own moderation system. The result was fast. Seventy three percent of reviews are now screened automatically in seconds, but the gray cases still pass through human hands. They also launched a feature called Content Coach that helped create more than four hundred thousand authentic reviews. Eighty seven percent of people who tried it said it added value [2]. What stands out is that AI was not used to replace people, but to extend their capacity and improve the overall trust in the platform.

Executive Evaluation

Problem: Content moderation demand and regulatory pressure outpace existing systems, creating inconsistency, legal risk, and declining community trust.
Pain: High appeal overturn rates, moderator burnout, infrastructure costs, and looming fines erode performance and brand safety.
Possibility: Hybrid AI human moderation provides speed, accuracy, and compliance while protecting moderators and communities.
Path: Fund a permanent moderation program with executive ownership. Map standards into behavior matrices, embed explainability into all workflows, and integrate human review into gray and consequential cases.
Proof: Measurable reductions in overturned appeals, faster decision times, lower per unit moderation cost, stronger compliance audit scores, and improved moderator wellness metrics.
Tactic: Launch a fully accountable program with NLP triage, LLM escalation, and human oversight. Track KPIs continuously, appeal overturn rate, time to decision, cost per thousand items, and percentage of actions with documented reasons. Scale with ownership and budget secured, not as a temporary pilot but as a standing function of trust and safety.

Closing Thought

Infrastructure is not abstract and it is never just a theory slide. Claude supports briefs, Surfer builds authority, HeyGen enhances video integrity, and MidJourney steadies visual moderation. Compliance runs quietly in the background, not flashy but necessary. The teams that stop treating this stack like a side test and instead lean on it daily are the ones that walk into 2025 with measurable speed, defensible trust, and credibility that holds.

References

Andreessen Horowitz. (2024, November 11). Welcome to LLMflation: LLM inference cost is going down fast. https://a16z.com/llmflation-llm-inference-cost/
Bazaarvoice. (2024, April 25). AI-powered content moderation and creation: Examples and best practices. https://www.bazaarvoice.com/blog/ai-content-moderation-creation/
Center for Democracy & Technology. (2021, July 26). “Chilling effects” on content moderation threaten freedom of expression for everyone. https://cdt.org/insights/chilling-effects-on-content-moderation-threaten-freedom-of-expression-for-everyone/
Discord. (2024, March 14). Our approach to content moderation at Discord. https://discord.com/safety/our-approach-to-content-moderation
Discord. (2023, August 1). How we moderate media with AI. https://discord.com/blog/how-we-moderate-media-with-ai
Eigenvalue. (2023, December 10). Token intuition: Understanding costs, throughput, and scalability in generative AI applications. https://eigenvalue.medium.com/token-intuition-understanding-costs-throughput-and-scalability-in-generative-ai-applications-08065523b55e
European Commission. (2022, October 27). The Digital Services Act. https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/europe-fit-digital-age/digital-services-act_en
GOV.UK. (2024, April 24). Online Safety Act: explainer. https://www.gov.uk/government/publications/online-safety-act-explainer/online-safety-act-explainer
Label Your Data. (2024, January 16). Human in the loop in machine learning: Improving model’s accuracy. https://labelyourdata.com/articles/human-in-the-loop-in-machine-learning
Meta AI. (2024, March 27). Shielding citizens from AI-based media threats (CIMED). https://ai.meta.com/blog/cimed-shielding-citizens-from-ai-media-threats/
Reddit. (2023, October 27). 2023 Transparency Report. https://www.reddit.com/r/reddit/comments/17ho93i/2023_transparency_report/
Sap, M., Card, D., Gabriel, S., Choi, Y., & Smith, N. A. (2019). The Risk of Racial Bias in Hate Speech Detection. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 1668–1678). https://aclanthology.org/P19-1163/
Trilateral Research. (2024, June 4). Human-in-the-loop AI balances automation and accountability. https://trilateralresearch.com/responsible-ai/human-in-the-loop-ai-balances-automation-and-accountability
Joshi, A., Bhattacharyya, P., & Carman, M. J. (2017). Automatic Sarcasm Detection: A Survey. ACM Computing Surveys, 50(5), 1–22. https://dl.acm.org/doi/10.1145/3124420

The Growth OS: Leading with AI Beyond Efficiency Part 2

September 4, 2025 by Basil Puglisi Leave a Comment

Part 2: From Pilots to Transformation

Pilots are safe. Transformation is bold. That is why so many AI projects stop at the experiment stage. The difference is not in the tools but in the system leaders build around them. Organizations that treat AI as an add-on end up with slide decks. Organizations that treat it as part of a Growth Operating System apply it within their workflows, governance, and culture, and from there they compound advantage.

The Growth OS is an established idea. Bill Canady’s PGOS places weight on strategy, data, and talent. FAST Ventures has built an AI-powered version designed for hyper-personalized campaigns and automation. Invictus has emphasized machine learning to optimize conversion cycles. The throughline is clear: a unified operating system outperforms a patchwork of projects.

My application of Growth OS to AI emphasizes the cultural foundation. Without trust, transparency, and rhythm, even the best technical deployments stall. Over sixty percent of executives name lack of growth culture and weak governance as the largest barriers to AI adoption (EY, 2024; PwC, 2025). When ROI is defined only as expense reduction, projects lose executive oxygen. When governance is invisible, employees hesitate to adopt.

The correction is straightforward but requires discipline. Anchor AI to growth outcomes such as revenue per employee, customer lifetime value, and sales velocity. Make governance visible with clear escalation paths and human-in-the-loop judgment. Reward learning velocity as the cultural norm. These moves establish the trust that makes adoption scalable.

To push leaders beyond incrementalism, I use the forcing question: What Would Growth Require? (#WWGR) Instead of asking what AI can do, I ask what outcome growth would demand if this function were rebuilt with AI at its core. In sales, this reframes AI from email drafting to orchestrating trust that compresses close rates. In product, it reframes AI from summaries to live feedback loops that de-risk investment. In support, it reframes AI from ticket deflection to proactive engagement that reduces churn and expands retention.

“AI is the greatest growth engine humanity has ever experienced. However, AI does lack true creativity, imagination, and emotion, which guarantees humans have a place in this collaboration. And those that do not embrace it fully will be left behind.” — Basil Puglisi

Scaling this approach requires rhythm. In the first thirty days, leaders define outcomes, secure data, codify compliance, and run targeted experiments. In the first ninety days, wins are promoted to always-on capabilities and an experiment spine is created for visibility and discipline. Within a year, AI becomes a portfolio of growth loops across acquisition, onboarding, retention, and expansion, funded through a growth P&L, supported by audit trails and evaluation sets that make trust tangible.

Culture remains the multiplier. When leaders anchor to growth outcomes like learning velocity and adoption rates, innovation compounds. When teams see AI as expansion rather than replacement, engagement rises. And when the entire approach is built on trust rather than control, the system generates value instead of resistance. That is where the numbers show a gap: industries most exposed to AI have quadrupled productivity growth since 2020, and scaled programs are already producing revenue growth rates one and a half times stronger than laggards (McKinsey & Company, 2025; Forbes, 2025; PwC, 2025).

The best practice proof is clear. A subscription brand reframed AI from churn prevention to growth orchestration, using it to personalize onboarding, anticipate engagement gaps, and nudge retention before risk spiked. The outcome was measurable: churn fell, lifetime value expanded, and staff shifted from firefighting to designing experiences. That is what happens when AI is not a tool but a system.

I have also lived this shift personally. In 2009, I launched Visibility Blog, which later became DBMEi, a solo practice on WordPress.com where I produced regular content. That expanded into Digital Ethos, where I coordinated seven regular contributors, student writers, and guest bloggers. For two years we ran it like a newsroom, which prepared me for my role on the International Board of Directors for Social Media Club Global, where I oversaw content across more than seven hundred paying members. It was a massive undertaking, and yet the scale of that era now pales next to what AI enables. In 2023, with ChatGPT and Perplexity, I could replicate that earlier reach but only with accuracy gaps and heavy reliance on Google, Bing, and JSTOR for validation. By 2024, Gemini, Claude, and Grok expanded access to research and synthesis. Today, in September 2025, BasilPuglisi.com runs on what I describe as the five pillars of AI in content. One model drives brainstorming, several focus on research and source validation, another shapes structure and voice, and a final model oversees alignment before I review and approve for publication. The outcome is clear: one person, disciplined and informed, now operates at the level of entire teams. This mirrors what top-performing organizations are reporting, where AI adoption is driving measurable growth in productivity and revenue (Forbes, 2025; PwC, 2025; McKinsey & Company, 2025). By the end of 2026, I expect to surpass many who remain locked in legacy processes. The lesson is simple: when AI is applied as a system, growth compounds. The only limits are discipline, ownership, and the willingness to move without resistance.

Transformation is not about showing that AI works. That proof is behind us. Transformation is about posture. Leaders must ask what growth requires, run the rhythm, and build culture into governance. That is how a Growth OS mindset turns pilots into advantage and positions the enterprise to become more than the sum of its functions.

References

Canady, B. (2021). The Profitable Growth Operating System: A blueprint for building enduring, profitable businesses. ForbesBooks.

Deloitte. (2017). Predictive maintenance and the smart factory.

EY. (2024, December). AI Pulse Survey: Artificial intelligence investments set to remain strong in 2025, but senior leaders recognize emerging risks.

Forbes. (2025, June 2). 20 mind-blowing AI statistics everyone must know about now in 2025.

Forbes. (2025, September 4). Exclusive: AI agents are a major unlock on ROI, Google Cloud report finds.

IMEC. (2025, August 4). From downtime to uptime: Using AI for predictive maintenance in manufacturing.

Innovapptive. (2025, April 8). AI-powered predictive maintenance to cut downtime & costs.

F7i.AI. (2025, August 30). AI predictive maintenance use cases: A 2025 machinery guide.

McKinsey & Company. (2025, March 11). The state of AI: Global survey.

PwC. (2025). Global AI Jobs Barometer.

Stanford HAI. (2024, September 9). 2025 AI Index Report.

Why AI Detection Tools Fail at Measuring Value [OPINION]

May 22, 2025 by Basil Puglisi Leave a Comment

AI detection platforms promise certainty, but what they really deliver is confusion. Originality.ai, GPTZero, Turnitin, Copyscape, and Writer.com all claim to separate human writing from synthetic text. The idea sounds neat, but the assumption behind it is flawed. These tools dress themselves up as arbiters of truth when in reality they measure patterns, not value. In practice, that makes them wolves in sheep’s clothing, pretending to protect originality while undermining the very foundations of trust, creativity, and content strategy. What they detect is conformity. What they miss is meaning. And meaning is where value lives.

The illusion of accuracy is the first trap. Originality.ai highlights its RAID study results, celebrating an 85 percent accuracy rate while claiming to outperform rivals at 80 percent. Independent tests tell a different story. Scribbr reported only 76 percent accuracy with numerous false positives on human writing. Fritz.ai and Software Oasis praised the platform’s polished interface and low cost but warned that nuanced, professional content was regularly flagged as machine generated. Medium reviewers even noted the irony that well structured and thoroughly cited articles were more likely to be marked as artificial than casual and unstructured rants. That is not accuracy. That is a credibility crisis.

This problem deepens when you look at how detectors read the very things that give content value. Factics, KPIs, APA style citations, and cross referenced insights are not artificial intelligence. They are hallmarks of disciplined and intentional thought. Yet detectors interpret them as red flags. Richard Batt’s 2023 critique of Originality.ai warned that false positives risked livelihoods, especially for independent creators. Stanford researchers documented bias against non native English speakers, whose work was disproportionately flagged because of grammar and phrasing differences. Vanderbilt University went so far as to disable Turnitin’s AI detector in 2023, acknowledging that false positives had done more harm to student trust than good. The more professional and rigorous the content, the more likely it is to be penalized.

That inversion of incentives pushes people toward gaming the system instead of building real value. Writers turn to bypass tricks such as adjusting sentence lengths, altering tone, avoiding structure, or running drafts through humanizers like Phrasly or StealthGPT. SurferSEO even shared workarounds in its 2024 community guide. But when the goal shifts from asking whether content drives engagement, trust, or revenue to asking whether it looks human enough to pass a scan, the strategy is already lost.

The effect is felt differently across sectors. In B2B, agencies report delays of 30 to 40 percent when funneling client content through detectors, only to discover that clients still measure return on investment through leads, conversions, and message alignment, not scan scores. In B2C, the damage is personal. A peer reviewed study found GPTZero remarkably effective in catching artificial writing in student assignments, but even small error rates meant false accusations of cheating with real reputational consequences. Non profits face another paradox. An NGO can publish AI assisted donor communications flagged as artificial, yet donations rise because supporters judge clarity of mission, not the tool’s verdict. In every case, outcomes matter more than detector scores, and detectors consistently fail to measure the outcomes that define success.

The Vanderbilt case shows how misplaced reliance backfires. By disabling Turnitin’s AI detector, the university reframed academic integrity around human judgment, not machine guesses. That decision resonates far beyond education. Brands and publishers should learn the same lesson. Technology without context does not enforce trust. It erodes it.

My own experience confirms this. I have scanned my AI assisted blogs with Originality.ai only to see inconsistent results that undercut the value of my own expertise. When the tool marks professional structure and research as artificial, it pressures me to dilute the very rigor that makes my content useful. That is not a win. That is a loss of potential.

So here is my position. AI detection tools have their place, but they should not be mistaken for strategy. A plumber who claims he does not own a wrench would be suspect, but a plumber who insists the wrench is the measure of all work would be dangerous. Use the scan if you want, but do not confuse the score with originality. Originality lives in outcomes, not algorithms. The metrics that matter are the ones tied to performance such as engagement, conversions, retention, and mission clarity. If you are chasing detector scores, you are missing the point.

AI detection is not the enemy, but neither is it the savior it pretends to be. It is, in truth, a distraction. And when distractions start dictating how we write, teach, and communicate, the real originality that moves people, builds trust, and drives results becomes the first casualty.

*note- OPINION blog still shows only 51% original, despite my effort to use wolf sheep and plumbers…

References

Originality.ai. (2024, May). Robust AI Detection Study (RAID).

Fritz.ai. (2024, March 8). Originality AI – My Honest Review 2024.

Scribbr. (2024, June 10). Originality.ai Review.

Software Oasis. (2023, November 21). Originality.ai Review: Future of Content Authentication?

Batt, R. (2023, May 5). The Dark Side of Originality.ai’s False Positives.

Advanced Science News. (2023, July 12). AI detectors have a bias against non-native English speakers.

Vanderbilt University. (2023, August 16). Guidance on AI Detection and Why We’re Disabling Turnitin’s AI Detector.

Issues in Information Systems. (2024, March). Can GPTZero detect if students are using artificial intelligence?

Gold Penguin. (2024, September 18). Writer.com AI Detection Tool Review: Don’t Even Bother.

Capterra. (2025, pre-May). Copyscape Reviews 2025.

Basil Puglisi used Originality.ai to eval this content and blog.

Building Authority with Verified AI Research [Two Versions, #AIa Originality.ai review]

April 28, 2025 by Basil Puglisi Leave a Comment

***This article is published first as Basil Puglisi Original work and written and dictated to AI, you can see the Originality.ai review of my work, it then is republished again in this same page after AI helps refine the content, my opinion is the second version is the better content and more professional but the AI scan would claim it has less value, I be reviewing AI scans next month***

I have been in enough boardrooms to recognize the cycle. Someone pushes for more output, the dashboards glow, and soon the team is buried in decks and reports that nobody trusts. Noise rises, but credibility does not. Volume by itself has never carried authority.

What changes the outcome is proof. Proof that every claim ties back to a source. Proof that numbers can be traced without debate. Proof that an audience can follow the trail and make their own judgment. Years ago I put a name to that approach: the Factics method. The idea came from one campaign where strategy lived in one column and data in another, and no one bothered to connect the two. Factics is the bridge. Facts linked with tactics, data tied to strategy. It forces receipts before scale, and that is where authority begins.

Perplexity’s enterprise release showed the strength of that principle. Every answer carried citations in place, making it harder for teams to bluff their way through metrics. When I piloted it with a finance client, the shift was immediate. Arguments about what a metric meant gave way to questions about what to do with it. Backlinks climbed by double digits, but the bigger win was cultural. People stopped hiding behind dashboards and began shaping stories that could withstand audits.

Claude Sonnet carried a similar role in long reports. Its extended context window meant whitepapers could finally be drafted with fewer handoffs between writers. Instead of patching paragraphs together from different writers, a single flow could carry technical depth and narrative clarity. The lift was not only in speed but in the way reports could now pass expert review with fewer rewrites.

Other tools filled the workflow in motion. ElevenLabs took transcripts and turned them into quick audio snippets for LinkedIn. Descript polished behind-the-scenes recordings into reels, while Surfer SEO scored drafts for topical authority before publication. None of them mattered on their own, but together they formed a loop where compliance, research, and social proof reinforced one another. The outcome was measurable: steadier trust signals in search, more reliable performance on LinkedIn, and fewer compliance penalties flagged by governance software.

Creative Concepts Corner

B2B — Financial Services Whitepaper
A finance firm ran competitor research through Perplexity Pro, pulled the citations, and built a whitepaper with Claude Sonnet. Surfer scored it for topical authority, and ElevenLabs added an audio briefing for LinkedIn. Backlinks rose 15%, compliance errors fell under 5%, and lead quality improved. The tip: build the Factics framework into reporting so citations carry forward automatically.

B2C — Retail Campaign Launch
A retail brand used Descript to edit behind-the-scenes launch content, paired with ElevenLabs audio ads for Instagram. Perplexity verified campaign stats in real time, ensuring ad claims were sourced. Compliance penalties stayed near zero, campaign ROI lifted by 12%, and sentiment held steady. The tip: treat compliance checks like creative edits — built into the process, not bolted on.

Nonprofit — Health Awareness
A health nonprofit ran 300 articles through Claude Sonnet to align with expertise and accuracy standards. Lakera Guard flagged risky phrasing before launch, while DALL·E supplied imagery free of trademark issues. The result: a 97% compliance score and higher search visibility. The tip: use a shared dashboard to prioritize which content pieces need review first.

Closing Thought

Authority is not abstract. It shows up in backlinks earned, in the compliance rate that holds steady, and in how an audience responds when they can trace the source themselves. Perplexity, Claude, Surfer, ElevenLabs, Descript — none of them matter on their own. What matters is how they hold together as a system. The proof is not the toggle or the feature. It is the fact that the teams who stop treating this as a side experiment and begin leaning on it daily are the ones entering 2025 with something real — speed they can measure, trust they can defend, and credibility that endures.

References

Acrolinx. (2025, March 5). AI and the law: Navigating legal risks in content creation. Acrolinx.

Anthropic. (2024, March 4). Introducing the next generation of Claude. Anthropic.

AWS News Blog. (2024, March 27). Anthropic’s Claude 3 Sonnet model is now available on Amazon Bedrock. Amazon Web Services.

ElevenLabs. (2025, March 17). March 17, 2025 changelog. ElevenLabs.

FusionForce Media. (2025, February 25). Perplexity AI: Master content creation like a pro in 2025. FusionForce Media.

Google Cloud. (2024, March 14). Anthropic’s Claude 3 models now available on Vertex AI. Google.

Harvard Business School. (2025, March 31). Perplexity: Redefining search. Harvard Business School.

Influencer Marketing Hub. (2024, December 1). Perplexity AI SEO: Is this the future of search? Influencer Marketing Hub.

Inside Privacy. (2024, March 18). China releases new labeling requirements for AI-generated content. Covington & Burling LLP.

McKinsey & Company. (2025, March 12). The state of AI: Global survey. McKinsey & Company.

Perplexity. (2025, January 4). Answering your questions about Perplexity and our partnership with AnyDesktop. Perplexity AI.

Perplexity. (2025, February 13). Introducing Perplexity Enterprise Pro. Perplexity AI.

Quora. (2024, March 5). Poe introduces the new Claude 3 models, available now. Quora Blog.

Solveo. (2025, March 3). 7 AI tools to dominate podcasting trends in 2025. Solveo.

Surfer SEO. (2025, January 27). What’s new at Surfer? Product updates January 2025. Surfer SEO.

YouTube. (2025, March 26). Descript March 2025 changelog: Smart transitions & Rooms improvements. YouTube.

Basil Puglisi shared eval from original content from Originality.ai

+++ AI Assisted Writing, placing content for rewrite and assistance +++

Teams often chase volume and hope credibility follows. Dashboards light up, reports multiply, yet trust remains flat. Volume alone does not build authority. The shift happens when every claim carries receipts, when proof is embedded in the process, and when data connects directly to tactics. Years ago I gave that framework a name: the Factics method. It forces strategy and evidence into the same lane, and it turns output into something an audience can trace and believe.

Perplexity’s enterprise release showed the strength of that approach. Citations appear in place, making it harder for teams to bluff their way through metrics. In practice the change is cultural as much as technical. At a finance client, arguments about definitions gave way to decisions about action. Backlinks climbed by double digits, and the greater win was that trust in reporting no longer stalled campaigns. Proof became part of the rhythm.

Claude Sonnet added its own weight in long-form reports. Extended context windows meant fewer handoffs between writers and fewer stitched paragraphs. Reports carried technical depth and narrative clarity in a single draft. The benefit was speed, but also a cleaner path through expert review. Rewrites fell, cycle time dropped, and credibility improved.

Other tools shaped the workflow in motion. ElevenLabs produced audio briefs from transcripts that fit neatly into LinkedIn feeds. Descript polished behind-the-scenes recordings into usable reels. Surfer SEO flagged drafts for topical authority before they went live. None of these tools deliver authority on their own, but together they form a cycle where compliance, research, and distribution reinforce each other. The results are measurable: steadier trust signals in search, stronger LinkedIn performance, and fewer compliance penalties flagged downstream.

Best Practice Spotlight

A finance firm demonstrated how Factics translates into outcomes. Competitor research ran through Perplexity Pro, citations carried forward, and Claude Sonnet produced a whitepaper that Surfer validated for topical authority. ElevenLabs added an audio briefing for distribution. The outcome was clear: backlinks rose 15 percent, compliance errors fell under 5 percent, and lead quality improved. The lesson is practical. Build citation frameworks into reporting so proof travels with every draft.

Creative Consulting Concepts

B2B — Financial Services Whitepaper

Challenge: Research decks lacked trust.
Execution: Perplexity sourced citations, Claude structured the whitepaper, Surfer validated authority, ElevenLabs created LinkedIn audio briefs.
Impact: Backlinks increased 15 percent, compliance errors stayed under 5 percent, lead quality lifted.
Tip: Automate Factics so citations flow forward without manual work.

B2C — Retail Campaign Launch

Challenge: Marketing claims needed real-time validation.
Execution: Descript refined behind-the-scenes launch clips, ElevenLabs produced audio ads, Perplexity verified stats live.
Impact: ROI rose 12 percent, compliance penalties stayed near zero, sentiment held steady.
Tip: Treat compliance checks as part of editing, not as a final review stage.

Nonprofit — Health Awareness

Challenge: Scale content without losing accuracy.
Execution: Claude Sonnet shaped 300 articles, Lakera Guard flagged risk, DALL·E supplied safe imagery.
Impact: Compliance reached 97 percent, search visibility climbed.
Tip: Use shared dashboards to prioritize reviews across lean teams.

Closing Thought

Authority is not theory. It is Perplexity carrying receipts, Claude adding depth, Surfer strengthening signals, ElevenLabs translating research to audio, and Descript turning raw into polished. Compliance runs in the background, steady and necessary. The teams that stop treating this as a trial and start relying on it daily are the ones entering 2025 with something durable, speed they can measure, trust they can defend, and credibility that endures.

References

Acrolinx. (2025, March 5). AI and the law: Navigating legal risks in content creation. Acrolinx. https://www.acrolinx.com/blog/ai-laws-for-content-creation

Anthropic. (2024, March 4). Introducing the next generation of Claude. Anthropic. https://www.anthropic.com/news/claude-3-family

AWS News Blog. (2024, March 27). Anthropic’s Claude 3 Sonnet model is now available on Amazon Bedrock. Amazon Web Services. https://aws.amazon.com/blogs/aws/anthropic-claude-3-sonnet-model-is-now-available-on-amazon-bedrock/

ElevenLabs. (2025, March 17). March 17, 2025 changelog. ElevenLabs. https://elevenlabs.io/docs/changelog/2025/3/17

FusionForce Media. (2025, February 25). Perplexity AI: Master content creation like a pro in 2025. FusionForce Media. https://fusionforcemedia.com/perplexity-ai-2025/

Harvard Business School. (2025, March 31). Perplexity: Redefining search. Harvard Business School. https://www.hbs.edu/faculty/Pages/item.aspx?num=67198

McKinsey & Company. (2025, March 12). The state of AI: Global survey. McKinsey & Company. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

Surfer SEO. (2025, January 27). What’s new at Surfer? Product updates January 2025. Surfer SEO. https://surferseo.com/blog/january-2025-update/

YouTube. (2025, March 26). Descript March 2025 changelog: Smart transitions & Rooms improvements. YouTube. https://www.youtube.com/watch?v=cdVY7wTZAIE

Basil Puglisi, sharing eval by Originality.ai after AI intervention in content.

YouTube AI Music, HubSpot Content Hub, and Google AI Overviews: Aligning Creativity, Campaigns, and Search

May 27, 2024 by Basil Puglisi Leave a Comment

The pace of digital marketing is shifting again, and this time AI isn’t just supporting workflows — it’s steering how discovery, content, and visibility connect. In April, YouTube expanded its AI-powered music features with DJ-style suggestions and text-to-prompt radio stations, offering creators dynamic soundtracks that respond to audience tastes. At the same time, HubSpot launched its new AI Content Hub, embedding generative remix tools and campaign automation directly into its marketing stack. And in search, Google rolled out AI Overviews to U.S. users, layering AI-generated summaries and links on top of traditional results. Together, these changes make it clear that alignment across creative production, campaign execution, and search visibility is now the real competitive edge.

“AI recommendations are reshaping music discovery.” — Billboard, April 28, 2024

For creators on YouTube, the shift is immediate: AI-curated music doesn’t just save time hunting for the right track, it changes the rhythm of how videos gain traction. Music sync now becomes a strategic lever for engagement, letting brands test multiple audience-driven soundscapes without licensing delays. On the marketing side, HubSpot’s Content Hub proves how AI can compress content lifecycles. Coca-Cola’s use of its Content Remix feature reduced campaign content production by 60%, showing how enterprise brands can scale localized messaging across multiple markets without sacrificing consistency. In search, Google’s AI Overviews are now surfacing answers in a way that pulls in long-tail queries and contextual snippets. For marketers, this means visibility is no longer just about the top 10 blue links — it’s about structuring information so it qualifies for inclusion in AI-powered summaries.

The bridge between these updates is efficiency with impact. Content cycle time can shrink by 40–60% when remix tools are applied. Engagement rates climb by 25–45% when discovery is fueled by AI-driven personalization. Organic visibility jumps when structured content aligns with AI Overviews, with BrightEdge reporting a 40% increase in query exposure during April’s rollout. These are not isolated KPIs — they compound. Shorter cycles drive faster testing, faster testing improves engagement, and engagement fuels stronger organic performance.

Here’s where Factics becomes practical. Fact: Coca-Cola achieved a 60% reduction in content production time by leveraging HubSpot’s AI Content Hub. Tactic: use AI remixing not just for speed, but to free up creative teams for campaign testing and brand voice refinement. Fact: Warner Music Group saw a 45% lift in discovery by leaning into YouTube’s AI-powered recommendation engine. Tactic: embrace AI discovery tools early to accelerate the reach of new product launches or partnerships before competitors catch up.

Best Practice Spotlights

Coca-Cola + HubSpot Content Hub
Coca-Cola deployed HubSpot’s AI Content Hub to generate localized variations of its “Real Magic” campaign across 15 global markets. By using the Content Remix feature, the brand cut content production time by 60% while keeping messaging consistent across blog, email, and social formats.

Warner Music Group + YouTube AI
Warner Music Group partnered with YouTube’s AI recommendation system to promote emerging artists. Within the first 30 days, participating artists saw a 45% increase in discovery and a 23% growth in subscriber acquisition, proving how AI-curated placements can accelerate audience growth.

Creative Consulting Concepts

B2B Scenario
Challenge: A SaaS provider struggles with slow content production cycles that delay campaign launches.
Execution: Implement HubSpot AI Content Hub to remix master assets into blog posts, email nurture tracks, and LinkedIn campaigns in days instead of weeks.
Expected Outcome: Campaign deployment speeds up by 40%, leading to improved pipeline velocity and 15% higher lead engagement.
Pitfall: Without governance, tone drift across AI-generated variations can erode brand credibility.

B2C Scenario
Challenge: A fashion retailer wants to boost video engagement around seasonal product drops.
Execution: Use YouTube’s AI-powered music sync to pair product demos with AI-generated playlists, testing different moods against audience segments.
Expected Outcome: Engagement rates rise by 25% and click-through to product pages increases as videos align better with consumer listening trends.
Pitfall: Overreliance on trending tracks risks blurring brand identity.

Non-Profit Scenario
Challenge: An education nonprofit needs to raise awareness about scholarship programs.
Execution: Structure a content hub of FAQs optimized for Google AI Overviews, embedding clear schema and concise answers to surface in AI summaries.
Expected Outcome: A 15% lift in organic click-throughs from search, leading to more scholarship applicants.
Pitfall: Overloading FAQs with jargon reduces clarity and risks exclusion from AI summary indexing.

Closing Thought

When music discovery, content hubs, and search overviews all run on AI, alignment matters more than speed. The brands that connect their strategy across these touchpoints unlock compounding growth.

References

Billboard. (2024, April 28). How YouTube’s AI recommendations are reshaping music discovery.

TechCrunch. (2024, April 10). YouTube Music tests AI-generated radio stations based on text prompts.

The Verge. (2024, April 15). YouTube Music’s AI DJ could change how we discover music.

MarTech. (2024, April 24). HubSpot launches new genAI-powered Content Hub.

VentureBeat. (2024, April 24). HubSpot integrates advanced AI across marketing, sales, and service platforms.

Business Wire (HubSpot). (2024, April 26). Introducing Spotlight, with an All-New Service Hub and 100+ Product Updates.

Search Engine Land. (2024, April 11). Google confirms AI Overviews links to their own search results.

BrightEdge. (2024, April 28). SGE query volume increases 40% as Google prepares AI Overviews launch.

WordStream. (2024, April 30). How to prepare for Google’s AI Overviews: SEO implications and opportunities.

Adweek. (2024, April 16). Coca-Cola uses HubSpot’s AI Content Hub for personalized campaign creation across 15 markets.

Music Business Worldwide. (2024, April 20). Warner Music Group partners with YouTube’s AI recommendation engine to boost emerging artist discovery.

LinkedIn Thought Leader Ads, Descript AI Video Editing, and Google INP: Building Authority Through Smarter Workflows

February 26, 2024 by Basil Puglisi Leave a Comment

The tools shaping authority and visibility are moving faster than campaigns themselves. In January, LinkedIn expanded its Thought Leader Ads, giving brands the ability to amplify executive voices and push trusted content directly into targeted feeds. At the same time, Descript introduced its biggest round of AI video editing upgrades yet, with smarter scene control, AI Actions, and improved audio workflows. Google added weight to the performance side of the equation by finalizing the Interaction to Next Paint (INP) metric, a new Core Web Vital that will replace First Input Delay in March. Each development alone pushes teams toward better creative, faster editing, or more precise technical standards—but together they redraw how authority is built and sustained in digital ecosystems.

The connection between these moves is workflow. LinkedIn Thought Leader Ads extend reach by elevating the credibility of leaders. Descript upgrades collapse editing steps so content can move from draft to publish in hours, not days. Google’s INP enforces consistency in site responsiveness, ensuring the customer journey holds attention once visitors arrive. Factics reinforce the shift: better ad targeting improves engagement rates, improved AI editing reduces cycle times, and stronger Core Web Vitals improve SEO visibility. The KPIs align: lower cost per lead, higher engagement percentages, faster production cycles, and stronger organic search rankings.

For a B2B brand, that might mean distilling a thought leadership article into a sponsored LinkedIn post by the CEO, cutting a highlight reel of commentary with Descript’s scene-based editing, and driving traffic to a site built with INP optimization in mind. For B2C, a brand ambassador’s post can be boosted as a Thought Leader Ad, repurposed into a customer-facing video within hours, and surfaced in search with technical performance that keeps mobile users from bouncing. In both cases, credibility, efficiency, and technical readiness converge.

The results are already measurable. Late last year, HubSpot used LinkedIn Thought Leader Ads to amplify posts from its CMO and executive team, targeting B2B decision-makers with leadership content. The campaign delivered a 25% increase in engagement rates compared to standard LinkedIn ads, proving that trust-driven creative performs more efficiently than traditional paid placements. For marketers, this translates into a sharper KPI framework: executive visibility scales faster, engagement quality improves, and acquisition costs fall when credibility and content velocity are aligned.

“Thought Leader Ads give us a way to scale trust by putting authentic leadership content in front of the audiences that matter most.” — Marketing Dive, Nov 15, 2023

Creative Consulting Concepts

B2B Scenario
– Challenge: A SaaS provider struggles with low engagement in standard sponsored ads.
– Execution: Use LinkedIn Thought Leader Ads to promote leadership blog excerpts, repurpose soundbites into Descript-edited clips, and drive traffic to a site optimized for INP.
– Expected Outcome: 25% lift in engagement and better organic search rankings within one quarter.
– Pitfall: Over-reliance on executives who don’t post regularly, creating inconsistency.

B2C Scenario
– Challenge: An online retailer wants to strengthen brand trust without a large ad budget.
– Execution: Amplify customer-facing posts from known ambassadors, edit unboxing videos with Descript AI Actions, and ensure site load meets INP standards to capture mobile conversions.
– Expected Outcome: Higher click-through rates on boosted posts and lower bounce rates on landing pages.
– Pitfall: Using generic voices in video editing instead of authentic spokespersons.

Non-Profit Scenario
– Challenge: A nonprofit advocacy group wants to influence policy discussions and donor trust.
– Execution: Promote the executive director’s posts as LinkedIn Thought Leader Ads, cut advocacy speeches into snackable Descript clips, and publish on a site tuned to Core Web Vitals.
– Expected Outcome: More visibility with policymakers and a measurable uptick in supporter conversions.
– Pitfall: Focusing too much on promotion without ensuring the site’s performance meets technical benchmarks.

Closing Thought

The advantage now lies with organizations that treat awareness, authority, and conversion as a single continuum. When executive credibility, AI-driven production, and technical performance are aligned, authority stops being a message and starts becoming an experience.

References

LinkedIn. (2023, October 17). Introducing Thought Leader Ads: Help your leaders become industry influencers.

Descript. (2023, November 7). Descript’s biggest update ever: New AI Actions, Video Editing Upgrades, and more.

Google Search Central. (2023, May 10). Interaction to Next Paint (INP) is replacing First Input Delay (FID) in March 2024.

Web.dev. (2024, January 31). Interaction to Next Paint becomes a Core Web Vital on March 12.

Artwork Flow. (2024, January 24). AI trends in creative operations 2024.

Justia Legal Marketing & Technology Blog. (2024, February 8). Google will update its Core Web Vitals metrics on March 12.

Marketing Dive. (2023, November 15). HubSpot leverages LinkedIn Thought Leader Ads to boost executive visibility.

AI, Me, and the Road Ahead: How I Use Artificial Intelligence to Create Content That Works

January 1, 2023 by Basil Puglisi Leave a Comment

If you’ve read my work before, you know I believe technology should serve creativity, not replace it. That’s why in 2023, you’ll see two distinct kinds of content from me—each powered by AI in different ways, but with very different results.

Defining the Two Paths

Artificial intelligence can be an accelerator or an autopilot. When I talk about #AIAssisted, I mean I’m still in the driver’s seat—shaping ideas, fact-checking, editing, and adding that irreplaceable layer of human insight. When I label something as AIGenerated, I’m letting the AI take the lead, producing the content from a simple prompt with minimal intervention. Both have their uses, but only one carries my full creative fingerprint.

Additional Context: The Origins of the Terms

The distinction between AI-assisted and AI-generated content didn’t emerge with ChatGPT’s release. Both terms have been used in research, industry reports, and marketing circles for years.

AI-Assisted Content — This phrase appeared in academic and industry discussions well before 2022, often in contexts like “AI-assisted medical diagnostics” or “AI-assisted writing tools” such as Grammarly and Jasper’s early iterations. By the late 2010s, digital marketing agencies and SEO professionals were already using “AI-assisted” to describe workflows where humans retained creative control but used AI for research, outlines, and optimization.

AI-Generated Content — This term dates back to early experiments in automated journalism and text generation in the 2010s. Newsrooms such as the Associated Press used automated systems to produce financial reports, weather summaries, and sports recaps, labeling them as “machine-generated” or “AI-generated.” In the marketing world, the phrase was in use by at least 2018 to describe content fully produced by natural language generation (NLG) systems like Wordsmith or GPT-2, with minimal or no human editing.

By late 2022, the AI industry — along with journalists, academics, and marketers — was actively debating the quality, trust, and ethical implications of each approach. The public release of ChatGPT intensified that conversation but did not create it.

Why It Matters

The distinction isn’t just technical—it’s about trust, originality, and quality. Research from Nielsen and Spiegel Research has shown that authenticity and credibility drive higher engagement and conversion rates. AI can write fast, but speed doesn’t equal substance. Without human oversight, AI-generated work risks being generic, error-prone, and out of sync with brand voice.

B2B vs. B2C Impact

For B2B, AI-assisted processes protect the nuance needed to address complex challenges, long sales cycles, and specific industry contexts. In B2C, where speed and volume are valuable, AI-generated content can scale basic tasks—but human refinement still ensures emotional resonance and brand consistency.

Factics

Fact: Audiences rate content as more credible when they know a human was actively involved

Tactic: Clearly label content type (#AIAssisted vs. AIGenerated) to build transparency and trust.

Fact: AI-assisted processes can outperform human-only workflows for efficiency without losing quality

Tactic: Use AI for outlining, research, and draft refinement, but keep humans in control of narrative and tone.

Fact: Disclosure policies are becoming common across platforms and publishers.

Tactic: Adopt voluntary disclosure to get ahead of compliance trends and reinforce audience trust.

Platform Playbook

LinkedIn: Publish thought-leadership posts under #AIAssisted to signal human-led insight.

YouTube: Release behind-the-scenes videos showing how AI tools fit into your workflow.

Blog: Pair AIGenerated posts with human commentary sections to provide context and extra value.

Best Practice Spotlight

Nava Public Benefit Corporation’s AI Tool Experimentation — In 2022, Nava integrated AI into public benefits workflows to increase efficiency without losing service quality. By keeping humans in control of review and decision-making, they maintained trust while improving speed—proving that AI works best as an assistant, not a replacement (Nava, 2022).

Hypotheticals Imagined

The AI-Assisted Strategy Deck – You use AI to generate an outline for a client proposal, then add your case studies, data, and narrative. The result: a document that’s faster to produce but uniquely yours.

The AIGenerated Blog Experiment – You feed a topic into AI, publish the output with minimal changes, then compare engagement to an AI-assisted version. Data shows the AI-assisted version drives more shares and longer read times.

Hybrid Workflow – You produce product descriptions using AI, but manually craft the hero copy for the website. This blend saves hours but still delivers a branded experience.

References:

References:
AI‑Generated Content

AI‑Assisted Content / AI Assistance

ChatGPT Media, Press etc.

11. OpenAI. (2022, November 30). Introducing ChatGPT. OpenAI.

12. Lyons, K. (2022, December 1). OpenAI’s new ChatGPT bot: What it is and why it matters. TechCrunch.

13. Reuters. (2022, December 5). ChatGPT crosses 1 million users within a week of launch. Reuters.

14. BBC News. (2022, December 5). ChatGPT: What is it and why is it making waves?. BBC News.

15. Wikipedia contributors. (2022, December). ChatGPT. In Wikipedia.
16. Southern, M. (2022, December 6). The history of ChatGPT (timeline). Search Engine Journal.

Final Thoughts:

A Universal AI Perspective

For me, the use of AI is not limited to when I run prompts through ChatGPT or another named platform. It should be assumed that AI, in some form, touches every part of my work. From research and drafting to editing and formatting, AI tools—whether visible or invisible—are part of the process. Sometimes that means advanced language models helping refine a paragraph, other times it’s background algorithms suggesting the most relevant data sources, or automated systems streamlining workflow management. In short, my entire creative and strategic process is inherently AI-assisted, even when the final product reflects heavy human authorship.

I believe that everything we do is AI-assisted and has been since the first time we asked a computer to output anything after a prompt. The greatest example of this is the evolution of libraries’ card catalogues into searchable online databases and the ease of a simple Google search to find something. Whether we realize it or not, our digital tools—from spellcheck to search engines—are forms of artificial intelligence augmenting our thinking and expanding our reach. Recognizing this reality isn’t just a technical point; it’s a statement about how creativity, strategy, and technology have been inseparable for decades.

Create Your Own E-book For Free

January 13, 2013 by Basil Puglisi 1 Comment

You can create your own ebook in OpenOffice writer with this free Kindle template and EPUB generator from OpenOffice.org.

Kindle Template and EPUB Generator Review

These days, if you want to self-publish a book, the easiest and least expensive way to do it is to make it an ebook. You can save a standard OpenOffice Writer document as a PDF, but it won’t be properly formatted for digital readers, like Kindle, Sony Reader, or Nook. With a free Kindle template for OpenOffice Writer, and a free EPUB generator, you can make sure your Writer file is the perfect size and format, so any digital reader can view your ebook or document exactly the way it was meant to be viewed. Use the free Kindle template and EPUB generator with OpenOffice Writer to take lecture notes that you can read on the go, publish your own digital magazine, or distribute an informative pamphlet. Anyone with a digital reader or other mobile device will be able to read your document easily, and without a lot of scrolling.

Kindle Template by RanRutenberg

Download this free Kindle template from OpenOffice.org to create an OpenOffice Writer document that will display perfectly on a Kindle screen. Standard Writer documents are designed to print on an A4 letter sized sheet of paper. A4 pages are too large to be properly displayed on a 6 inch wide Kindle screen. This free Kindle template formats any OpenOffice Writer document to print on an A6 sized sheet of paper, and it includes custom margins that make it the exact size of the Kindle’s screen.

EPUB Generator by Przemyslaw Rumik

PDF files can be viewed on a digital reader, but the best way to save a document for Kindle viewing is to save it in a digital reader format. EPUB files can be viewed on a Nook, Sony Reader, and Apple iOS devices like iPhone, iPad, and iPod Touch. EPUB files can also be converted to the Kindle MOBI format with a free conversion program or web site. With this free EPUB generator for OpenOffice Writer, you can ensure that your document is compatible with any digital reader or mobile device. Simply download the extension from OpenOffice.org, install it, and click “Publish to EPUB” on any Writer document to start the conversion process. You will need to have installed additional XSLT filters. These can be installed by selecting “XML Filter Settings” from the OpenOffice “Tools” menu. If you want to publish an ebook, digital magazine, or save a document for easy viewing on your digital reader or mobile device, you can do it for free with a Kindle template and EPUB generator from OpenOffice.org. ebooks can be viewed by almost anybody, either online, or on a mobile device with an Internet connection. Download the free Kindle template and EPUB generator from OpenOffice.org, and you can easily turn your OpenOffice Writer document into an ebook perfectly formatted for digital readers like Kindle, Sony Reader, and Nook.

Citations:

Jen Heller Meservey is a freelance writer for Downloadhaus who loves discovering new software and being more productive with freeware apps. Downloadhaus.com brings you the latest free, open source apps with no waiting times or download queues. Connect with Jen Heller Meservey on LinkedIn.

What Digital & Social Media Marketers Can Learn from Business Consultants [Opinion]

June 15, 2012 by Basil Puglisi Leave a Comment

In the last five years I have heard some wild claims about who makes the best marketer – those claims have ranged from PR professionals, who ‘should be the only people to do it’, to Social Media, to ‘it takes a Sales Professional to provide the best internet marketing.’

I’d like you to think of Digital Assets in the form of a building:

The windows are Social Media – transparency of course
The walls are the advertising efforts – the place to display and show
The doors are the PR – as media attention helps get people to walk through the door
The shelves, displays and racks are the event planners – presentation and onsite execution
The Roof is the website – it covers everything else

However, the missing element is the foundation or the business itself. The digital and social media industry has gotten a lot of bad heat on not being effective and I would argue that has happened because the keystone has been missing, the Business Consultant.

I warn almost everyone that I interact with to look for the red flags when meeting a PR, Web, SEO, Social Media, Event Professional, etc. The best way to know if that have any clue what they are talking about will come with the first interaction. Do they start talking to you about their business and products, or do they ask you about yours?

The world is filled with overnight talent and businesses that offer these services and I say talent because most are very good at their niche, unfortunately it seems to end there. Think of it like a great marksman sent off to war to be a sniper without any military training. The ability to hit a target does not translate to being an effect solider, especially in terms of the bigger picture.

The transformed business consultants that are working as project managers and on the rare occasion can provide Web Development, SEO, Social Media and more are carrying with them the greatest lesson the marketing industry can learn, success goes beyond the view, comment and call!

Traditional marketing and advertising was all about visibility and the connection point, the advertising was a success when the consumer connected with your name, product or service.( i.e. someone visited the website, opened the email, opened the text message or called your phone, that is marketing success in the traditional context). The ability to convert that experience into a sale was the business owners problem. This is the reason businesses fail continuously and why corporate leadership is completely in the dark with the digital environment.

How Can We do Better or Demand Better?

Take the Business Consultant approach, inquire about the business model, the products or services, why the target market is the target market. Take the campaign backwards, go from the conversion or sale to the campaigns and tools to reach consumers. Build the model on the business and remember the best in any industry become the best from exploring. Sometimes it’s easier to create new then fix broken.

Why “NO” is so important to the Profession of Digital & Social Media Marketing [Opinion]

The overnight rush of Web developers lead to overnight SEO providers and then the flood of Social Media Marketers. Which in turn lead to every PR, advertising and marketing agency claiming to offer services they knew nothing about to save their revenue streams. The industry changed so fast that quantity quickly overtook quality.

“NO” is crucial to not just the digital and social industry but the recovery of our economy! I was sitting in a session at BlogWorld, it was about monetization, each of the three presenters had the same story the “advertisers found us” and “I spent nothing on advertising”.

I had to go to the mic, this is such a common carless comment that I had an ethical obligation to set straight.

The question: “You said that advertisers found you and that you spent nothing on advertising, but I want you to think of what the cost was… you might not have purchased advertising but clearly you spent time and money to build your…”

All three faces quickly had a look that you couldn’t quite place, perhaps it was horror? Then Lou Mongello of Walt Disney World Radio jumped to answer, “Oh it was so expensive, it cost me time, I had to sell my house and I spent money on all sorts of things”.

Lou Mongello then went on to explain that part of his success came from having his families support and the understanding of sacrifice to accomplish the long term goal.

Don’t Go In Unprepared

Here is the crucial point of this article, because so many enter into digital and social media services unprepared with misrepresentation of their own business model, they are ill equipped to help their clients with the same problem. In the need to create profits they become like AOL, they leap into every adventure without any thought of their clients business model or worse their own long term business model.

Learning to say “NO” allows you to take on clients that will be successful with your talent or service, it garnishes long term revenue for your business and a reputation for growth. It’s not easy being picky in the beginning, or when times are tough, but it is successful! Even more importantly, it keeps others from wasting their life savings on an idea or business that they are underfunded, underequipped or worse ignorant about from losing their time and money. It also prevents the overwhelming false, false from becoming the digital and social media industry. The Social Media Marketer did not intentionally fail you, the web developer did not build a crappy website or fail to generate valuable SEO, the business was flawed and directed to fail from the beginning and the digital and social industry should not take the blame for that.

Pick your clients carefully, for the benefit of them, yourself and our industry.

Author:

@BasilPuglisi is the Executive Director and Publisher for Digital Brand Marketing Education (dbmei.com). Basil C. Puglisi is also the President of Puglisi Consulting Group, Inc. A Digital Brand Marketing Consultancy that manages professional and personal branding for Fortune 500 CEOs, Hedge Fund Managers and Small Business Owners.

Sources:

#140conf in New York City June 19th – 20th 2012 [Event]

June 13, 2012 by Basil Puglisi Leave a Comment

This year the #140c will be hosted at the 92nd Street Y at 1395 Lexington Avenue in NYC.

The History of the #140conf

In its fourth year running, this conference has had no shortages of experts with a plethora of educational and trending information to provide attendees. Each year it simply gets better and better.

In 2009, #140conf hosted events in:

2011 found #140conf spreading even further across the globe with events held in more locations than before.

There have also been a number of #140conf Meetups which have taken place across the United States, and outside of the states including places like the UK, Kenya and Israel.

In addition, check out the writers panel from #140conf 2011 that featured Debre Eckerling from @WriteOnOnline, Jeanne V Bowerman @jeannevb and Tracey Jackson @traceyjackson4.

[youtube=http://www.youtube.com/watch?v=SIIKqAJmGmE]

With many more impressive panels the 2011 #140conf was a rousing success. This year is shaping up to top them all.

Jeff Pulver Speaks

In April, 2012, Jeff Pulver shared a talk in Des Moines Iowa and he had some very compelling things to say about how we act and interact on our social networks. Are you YOU on your social networks? Are you who you think your friends and consumers think you should be? If you are anything but yourself you are not really connecting with those most important when it comes to marketing yourself or your brand. Jeff put it out there pretty well when he asked:

Are you you? Are you connecting to the person who you are? Are you true to yourself? If you Tweeted yourself would you talk back? Would you friend yourself?

I can relate to this as it is very similar to my previous article “Who are you?”

Societal communications are occurring now on a global level. Jeff reminds us that while the communication line is open, we have plenty of social media users who are more than happy to be brash, rude, insensitive, and that perhaps these people are being themselves. But we also have others who are the veritable shrinking violets, who are unlikely to ever be heard on a grand scale. And then we have our social leaders. Those whose Tweets and Status shares compel us, attract us. In most cases, those people have a strength we admire. Each of us have our own strengths when it comes to how we voice our feelings, how we communicate. In each of us is our true voice that when shared with the Twitterverse, or asserted on a Facebook status will have its own selling point, for those with a similar voice, opinions and assertions.

Inspire others whenever you can, because you can.

This Year’s #140conf NYC

The turnout for this year’s conference is expected to be attendees from 17 countries and 31 states.It is already expected to be the largest worldwide gather of entrepreneurs and professionals who are interested in the effects of real-time on people and businesses.

The focus will be on how the internet has the power to change lives and all attendees should expect to leave with a new outlook on how real-time interaction on the web can be used to grow your business or personal life, or even to do something intrinsically meaningful such as activism in charities you support. The options are almost endless.

The schedule is a fast paced and very unique one. It is the intention of the organizers to supply the perfect platform for as wide of a demographic as possible. Everyone is encouraged to share their thoughts or engage with attendees and speakers. Individual talks will be limited to ten minute excerpts and panel discussions will run for 10 to 15 minute sets.

Conference speakers will be arriving in NYC from all over the world. Speakers from the Pacific, South America, Europe, Asia, Canada and of course many from the United States.

Interested in having your voice heard at a future #140conf event? We are always looking for new voices to introduce to our community. Just drop a note to Jeff Pulver. Interested in leveraging the influence of the #140conf NYC community? we are looking for companies interested in sponsoring this event.

The #140conf events –Tweetups, Conferences, Parties and Roadtrips – present an opportunity to consistently broaden your social media knowledge, whether for its own sake, or for application in communication, business, news, politics, philathropy or just about any other sphere. You always meet the most interesting and creative people at these events, and each time, Jeff’s mix of speakers, their topics and perspective leave you substantially more informed and meaningfully inspired.

– Ian Aronovich – @GovtAuctions

Don’t miss this years @140conf #140conf in NYC!

Author:

Sources: