@BasilPuglisi

Content & Strategy, Powered by Factics & AI, Since 2009

Business Networking

The Agent Era Is Quietly Here

by Leave a Comment

AI agents are emerging as the hidden infrastructure shaping the next wave of digital transformation. They are not simply chatbots with plugins, but adaptive systems that reason, plan, and act across tools. For businesses, nonprofits, and creators, agents promise a shift from reactive digital processes to coordinated, self-correcting copilots that expand both capacity and impact.

The stakes are high. Teams today manage fragmented platforms, siloed data, and slow manual workflows that drain time and resources. Campaigns are delayed, insights are lost in noise, and leaders struggle to hit cycle-time, customer responsiveness, and content ROI targets. Agents offer an answer, embedding intelligence into the tactic layer of work, where data meets decision and execution.

Orchestration Is the Differentiator

Most early adopters think of agents as executors, completing a task when prompted. The real unlock is treating them as coordinators, orchestrating specialized modules that each handle a piece of the problem. Memory, context, and tool use must converge into a reliable workflow, not a single output. This orchestration layer is where agents cross the line from experiment to infrastructure (Boston Consulting Group, 2025).

Trust, Governance, and Memory

Capabilities alone are not enough. For agents to be trusted in production, workflows must be transparent, auditable, and resilient under stress. Governance and evaluation separate a flashy demo from a system that scales in a regulated, high-stakes environment. That is where frameworks like HAIA-RECCLIN step in, layering oversight, alignment, and checks into the orchestration layer. HAIA-RECCLIN assigns specialized roles — Researcher, Editor, Coder, Calculator, Liaison, Ideator, Navigator — to ensure each workflow is auditable, verifiable, and guided by human judgment.

Memory is the second bottleneck. Long-term context retention, consistent recall, and safe state management are what allow agents to scale beyond one-off tasks into continuous copilots. Without memory, orchestration is brittle. With it, agents begin to resemble durable operating systems (McKinsey & Company, 2025).

The Hidden Critical Success Factors

The conversation around agents often highlights features like multi-step planning or retrieval-augmented generation. Less attention goes to latency and security, yet these are the critical success factors. If an agent slows processes instead of accelerating them, adoption collapses. If security vulnerabilities surface, trust evaporates. Enterprises will not scale agents until these operational foundations are solved (IBM, 2025; Oracle, 2025).

Best Practice Spotlight: Beam AI and Motor Claims Processing

Beam AI demonstrates how agents move from concept to production. In a deployment with a Dutch insurer, Beam reports vendor-verified results of 91 percent automation of motor claims, a 46 percent reduction in turnaround time, and a nine-point improvement in net promoter score. Rather than replacing humans, the agents process routine data extraction, classification, and routing tasks. Human adjusters focus only on exceptions and oversight. In a domain where compliance, accuracy, and customer trust are paramount, the result is higher throughput, lower error, and faster resolution (Beam AI, 2025).

Creative Consulting Concepts

B2B Scenario: Enterprise Workflow Automation
A global logistics firm struggles with redundant reporting across regional offices. By piloting agents that integrate APIs from ERP and CRM systems, reports may be generated and distributed automatically. The measurable impact may be a 30 percent reduction in reporting cycle time and fewer data errors. The pitfall is governance, as without proper monitoring, agents may propagate inaccurate numbers.

B2C Scenario: E-commerce Customer Support
A retail brand faces rising customer service demand during holiday peaks. Deploying an agent to triage inquiries, handle FAQs, and escalate complex cases may reduce average response time from hours to minutes. Customer satisfaction scores may increase while human agents focus on high-value interactions. The challenge is bias in responses and ensuring cultural nuance is respected across markets.

Nonprofit Scenario: Donor Engagement Copilot
A nonprofit uses agents to personalize supporter outreach. By retrieving donor history, summarizing impact stories, and drafting tailored updates, the agent frees staff to focus on fundraising events. Donation conversion may improve by 12 percent in pilot campaigns. The pitfall is privacy, as agents must not expose sensitive donor information without strict safeguards.

Collaboration and Alignment

A final tension remains: will the biggest breakthroughs come from multi-agent collaboration or safer alignment? The answer is both. Multi-agent setups unlock coordination at scale, but without alignment, trust collapses. Alignment governs whether collaboration can be safely scaled, and governance frameworks must evolve in parallel with architectures.

Closing Thought

Agents are not the future, they are already here. The question is whether organizations will treat them as tactical add-ons or as strategic copilots. For leaders who measure outcomes in KPIs, the opportunity is clear: shorten cycle times, improve responsiveness, scale engagement, and reduce operational waste. The challenge is equally clear: build trust, apply governance, and ensure adoption across teams.

References

Scaling AI in Moderation: From Promise to Accountability

by 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

  1. Andreessen Horowitz. (2024, November 11). Welcome to LLMflation: LLM inference cost is going down fast. https://a16z.com/llmflation-llm-inference-cost/
  2. Bazaarvoice. (2024, April 25). AI-powered content moderation and creation: Examples and best practices. https://www.bazaarvoice.com/blog/ai-content-moderation-creation/
  3. 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/
  4. Discord. (2024, March 14). Our approach to content moderation at Discord. https://discord.com/safety/our-approach-to-content-moderation
  5. Discord. (2023, August 1). How we moderate media with AI. https://discord.com/blog/how-we-moderate-media-with-ai
  6. 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
  7. 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
  8. GOV.UK. (2024, April 24). Online Safety Act: explainer. https://www.gov.uk/government/publications/online-safety-act-explainer/online-safety-act-explainer
  9. 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
  10. 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/
  11. Reddit. (2023, October 27). 2023 Transparency Report. https://www.reddit.com/r/reddit/comments/17ho93i/2023_transparency_report/
  12. 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/
  13. 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
  14. 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

Multimodal Creation Meets Workflow Integration

by Leave a Comment

Ever been that person who had to sit with a nonprofit director needing videos in three languages on a shoestring budget? The deadline is tight, the resources thin, and panic usually follows. Except now, with the right stack, the story plays differently. One script in Synthesia becomes localized clips, NotebookLM trims prep for board updates, and Midjourney V7 provides visuals that look like they came from a big agency. What used to feel impossible for a small team now gets done in days.

That’s the shift happening now. Multimodal tools aren’t just for global giants, they’re giving small businesses and nonprofits options they never had before. Workflows that once demanded big crews and bigger budgets are suddenly accessible. Translation costs drop, campaign cycles speed up, and the final product feels professional. A bakery can localize TikToks for new customers. An advocacy group can roll out explainer videos in multiple languages without hiring a full production staff.

Meta’s LLaMA 4 brings native multimodal reasoning into normal workflows. It reads text, images, and simple tables in one pass, which means a screenshot, a product sheet, and a few rough notes become a single, usable brief. The way to use it is simple, gather the real assets you would hand to a teammate, ask for an outline that pairs each claim with a supporting visual or citation, and lock tone and brand terms in a short instruction block. Watch outline acceptance rate, factual edits per draft, and how long it takes to move from inputs to an approved brief.

OpenAI’s compile tools work like a calm research assistant. They cluster sources, extract comparable data points, and produce a clean working draft that is ready for human review. The move is to load only vetted links, ask for a side by side table of claims and evidence, then request a narrative that uses those rows and nothing else. Keep an evidence ledger next to the draft so reviewers can click back to the original. Track cycle time per asset, first draft on brand, and the number of factual corrections caught in QA.

ElevenLabs “Eleven Flash” makes voiceovers feel professional without the usual invoice shock. The model holds natural pacing and intonation at a lower cost per finished minute, which puts multilingual narration and fast updates within reach for small teams. TechCrunch’s coverage of the one hundred eighty million raise is a signal that voice automation is not a fad, production barriers are falling, and smaller players benefit first. The workflow is to create consented voice profiles, normalize scripts for clarity, batch generate by language and role, and keep an audio watermark and rights register. Measure cost per finished minute, listen through rate, turnaround from script to publish, and support ticket deflection on pages with audio.

Synthesia turns one approved script into localized video at scale. The working number to hold is a ten language rollout that lifts ROI about twenty five percent when localization friction drops. Use it by locking a master script, templating lower thirds and brand elements, generating each language with native captions and region specific calls to action, then routing traffic by locale. Watch ROI by locale, video completion, and time to first localized version.

NotebookLM creates portable audio overviews that actually shorten prep. Teams report about thirty percent less time spent getting ready when the briefing sits in their pocket. The flow is to assemble a small canonical packet per initiative, generate a three to five minute overview, and attach the audio to the kickoff doc or LMS module. Measure reported prep time, meeting efficiency scores, and downstream revision counts once everyone starts from the same context.

Midjourney’s coherence controls keep small brands from paying for a second design pass. Consistent composition and style adherence move concept art toward production faster. The practical move is to encode three or four visual rules, subject framing, color range, and typography hints, then prompt inside that sandbox to create a handful of options. Curate once, finalize in your editor, and keep a short gallery of do and don’t for the next round. Track concept to final cycle time, brand consistency scores, and how quickly paid performance decays when creative is refreshed on schedule.

ElevenLabs for dubbing trims production time when you move a base narration into multiple languages or roles. The working figure is about a third saved end to end. Set language targets up front, generate clean transcripts from the master audio, produce dubbed tracks with timing that matches, then add a bit of room tone so it sits well in the mix. Measure total hours saved per release, multilingual completion rates, and engagement lift on localized pages.

“This research is a reality check. There’s enormous promise around AI, but marketing teams continue to struggle to deliver real business impact when they are drowning in complexity. Unless AI helps tame this complexity and is deeply embedded into workflows and execution, it won’t deliver the speed, precision, or results marketers need.” — Chris O’Neill, CEO of GrowthLoop

FTC guidance turns disclosure into a trust marker. Clear labels, watermarking, and provenance notes reduce suspicion and protect credibility, especially for nonprofits and local businesses where trust is the currency. Operationalize it by adding a short disclosure line near any AI assisted media, watermarking visuals, and keeping a lightweight provenance section in your QA checklist. Track complaint rates, unsubscribe rate after disclosure, and click through on assets that carry clear labels.

Here is the point. Build small, repeatable workflows around each tool, connect them at the handoff points, and measure how much faster and further each campaign runs. The scoreboard is simple, cycle time per asset, first draft on brand, localization turnaround, completion and click through, and ROI by locale.

Best Practice Spotlight

Infinite Peripherals isn’t a giant consumer brand, it’s a practical tech company that needed videos fast. They used Synthesia avatars with DeepL translations and cranked out four multilingual explainers for trade shows in just 48 hours. Not a typo, two days. The payoff was immediate, a 35 percent jump in meetings booked and 40 percent more video views. For smaller organizations, this shows what happens when you combine tools instead of adding headcount [DeepL Blog, 2025].

Toys ’R’ Us is a big name, sure, but the lesson scales. The team used OpenAI’s Sora to create a fully AI-generated brand film. It drew millions of views and boosted brand sentiment while cutting costs. For a nonprofit or small business, think smaller scale: a short mission video, a donor thank-you message, or a seasonal ad. The principle is the same — storytelling amplified without blowing the budget [AdWeek, 2024].

Marketing tie-ins are clear. AdAge highlighted how localized TikTok and Reels campaigns bring results without big media buys [AdAge, 2025]. GrowthLoop’s ROI analysis showed how even lean campaigns can track returns with clarity [GrowthLoop, 2025]. The tactic for smaller teams is to measure ROI not just in revenue, but in saved time and extended reach. If an owner or director can run three times the campaigns with the same staff, that’s value that counts.

Creative Consulting Concepts

B2B Scenario
Challenge: A regional SaaS provider struggles to onboard new clients in different languages.
Execution: Synthesia video modules and NotebookLM audio summaries.
Impact: Onboarding time cut by half, fewer support calls.
Optimization Tip: Add a customer feedback loop before finalizing translations.

B2C Scenario
Challenge: A boutique clothing shop wants to engage younger buyers across platforms.
Execution: Midjourney V7 ensures visuals stay on-brand, Synthesia creates Reels in multiple languages.
Impact: 30 percent lift in engagement with international customers.
Optimization Tip: Rotate avatar personalities to keep content fresh.

Non-Profit Scenario
Challenge: An advocacy group must explain a policy campaign to donors in multiple languages.
Execution: ElevenLabs voiceovers layered on Synthesia explainers with disclosure labels.
Impact: 20 percent increase in donor sign-ups.
Optimization Tip: Test voices for tone so they fit the mission’s seriousness.

Closing Thought

Here’s how it plays out. Infrastructure isn’t abstract, and it’s not reserved for companies with large budgets. AI is helping the little guy even the field. You can use Synthesia to carry scripts into multiple languages. NotebookLM puts portable voices in your ear. If you want more, Midjourney steadies the visuals, though many small teams lean on Canva. Still watching every penny? ElevenLabs makes audio affordable without compromise. Compliance runs quietly in the background, necessary but not overwhelming. The teams that stop testing and start using these workflows every day are the ones who gain real ground, speed they can measure, trust they can defend, and credibility that holds. Start now, fix what you need later, and don’t get trapped in endless preparing.

References

DeepL Blog. (2025, March 26). Synthesia and DeepL partner to power multilingual video innovation.

Google Blog. (2025, April 29). NotebookLM Audio Overviews are now available in over 50 languages.

TechCrunch. (2025, April 3). Midjourney releases V7, its first new AI image model in nearly a year.

Meta AI Blog. (2025, April 5). The Llama 4 herd: The beginning of a new era of natively multimodal AI innovation.

TechCrunch. (2025, January 30). ElevenLabs, the hot AI audio startup, confirms $180M in Series C funding at a $3.3B valuation.

FTC. (2024, September 25). FTC Announces Crackdown on Deceptive AI Claims and Schemes.

AdWeek. (2024, December 6). 5 Brands That Went Big on AI Marketing in 2024.

AdAge. (2025, April 15). How Brands are Using AI to Localize Campaigns for TikTok and Reels.

GrowthLoop. (2025, March 7). AI ROI explained: How to prove the value of AI for driving business growth.

Basil Puglisi used Originality.ai to eval the content of this blog. (Likely the last time)

Why AI Detection Tools Fail at Measuring Value [OPINION]

by 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.

AI in Workflow: From Enablement to Autonomous Strategic Execution #AIg

by Leave a Comment

*Here I asked the AI to summarize the workflow for 2024 and try to look ahead.


What Happened

Over the second half of 2024, AI’s role in business operations accelerated through three distinct phases — enabling workflows, autonomizing execution, and integrating strategic intelligence. This evolution wasn’t just about adopting new tools; it represented a fundamental shift in how organizations approached productivity, decision-making, and market positioning.

Enablement (June) – The summer brought a surge of AI releases designed to remove friction from existing workflows and give teams immediate productivity gains.

  • eBay’s “Resell on eBay” feature tapped into Certilogo digital apparel IDs, allowing sellers to instantly generate complete product listings for authenticated apparel items. This meant resale could happen in minutes instead of hours, with accurate details pre-filled to boost buyer trust and reduce listing errors.
  • Google’s retail AI updates sharpened product targeting and recommendations, using more granular behavioral data to serve ads and promotions to the right audience at the right time.
  • ServiceNow and IBM’s AI-powered skills intelligence platform created a way for HR and learning teams to map current workforce skills, identify gaps, and match employees to development paths that align with business needs.
  • Microsoft Power Automate’s Copilot analytics gave operations teams a lens into automation performance, surfacing which processes saved the most time and which still contained bottlenecks.

Together, these tools represented the Enablement Phase — AI acting as an accelerant for existing human-led processes, improving speed, accuracy, and visibility without fully taking over control.

Autonomization (October) – By early fall, the conversation shifted from “how AI can help” to “what AI can run on its own.”

  • Salesforce’s Agentforce introduced customizable AI agents for sales and service, capable of autonomously following up with leads, generating proposals, and managing support requests without manual intervention.
  • Workday’s AI agents expanded automation into HR and finance, handling tasks like job posting, applicant screening, onboarding workflows, and transaction processing.
  • Oracle’s Fusion Cloud HCM agents targeted similar HR efficiencies, but with a focus on accelerating talent acquisition and resolving HR service tickets.
  • In the events sector, eShow’s AI tools automated agenda creation, personalized attendee engagement, and coordinated on-site logistics — allowing organizers to make real-time adjustments during events without manual scheduling chaos.

This was the Autonomization Phase — AI graduating from an assistant role to an operator role, managing end-to-end workflows with only exceptions escalated to humans.

Strategic Integration (November) – By year’s end, AI was no longer just embedded in operational layers — it was stepping into the role of strategic advisor and decision-shaper.

  • Microsoft’s autonomous AI agents could execute complex, multi-step business processes from start to finish while incorporating predictive planning to anticipate needs, allocate resources, and adjust based on real-time conditions.
  • Meltwater’s AI brand intelligence updates added always-on monitoring for brand health metrics, sentiment shifts, and media coverage, along with an AI-powered journalist discovery tool that matched organizations with reporters most likely to engage with their story.

This marked the Strategic Integration Phase — AI providing not just execution power, but also contextual awareness and forward-looking insight. Here, AI was influencing what to prioritize and when to act, not just how to get it done.

Across these three phases, the trajectory is clear: June’s tools enabled efficiency, October’s agents autonomized execution, and November’s platforms strategized at scale. In six months, AI evolved from speeding up workflows to running them independently — and finally, to shaping the decisions that define competitive advantage.

Who’s Impacted

B2B – Retailers, marketplaces, HR departments, event planners, and executive teams gain faster cycle times, automation coverage across functions, and AI-driven strategic intelligence for decision-making.
B2C – Customers and job applicants see faster service, personalized experiences, and more consistent engagement as autonomous systems streamline delivery.
Nonprofits – Development teams, advocacy groups, and mission-driven organizations can scale donor outreach, volunteer onboarding, and campaign intelligence without expanding headcount.

Why It Matters Now

Fact: eBay’s “Resell on eBay” tool and Google retail AI updates accelerate resale listings and sharpen product targeting.
Tactic: Integrate enablement AI into eCommerce and marketing workflows to reduce manual entry time and improve targeting accuracy.

Fact: Salesforce’s Agentforce and Workday’s HR agents automate sales follow-up, onboarding, and case resolution.
Tactic: Deploy role-specific AI agents with performance guardrails to handle repetitive workflows, freeing teams for higher-value activities.

Fact: Microsoft’s autonomous agents and Meltwater’s brand intelligence tools combine execution and strategic oversight.
Tactic: Pair autonomous workflow AI with market intelligence dashboards to inform proactive, KPI-driven strategic shifts.

KPIs Impacted: Listing creation time, product recommendation conversion rate, automation efficiency score, sales cycle length, time-to-hire, process automation rate, brand sentiment score, journalist outreach response rate.

Action Steps

  1. Audit current AI usage to identify opportunities across Enable → Autonomize → Strategize stages.
  2. Pilot one autonomous workflow with clear success metrics and oversight protocols.
  3. Connect operational AI outputs to brand and market intelligence platforms.
  4. Review KPI benchmarks quarterly to measure efficiency, agility, and strategic impact.

“When AI runs the process and watches the brand, leaders can focus on steering strategy instead of chasing execution.” – Basil Puglisi

References

Closing / Forward Watchpoint

The Enable → Autonomize → Strategize progression shows AI moving beyond support roles into leadership-level decision influence. In 2025, expect competition to center not just on what AI can do, but on how fast organizations can integrate these layers without losing control over governance and brand integrity.

AI in Workflow: HubSpot’s Breeze Redefines CRM Efficiency #AIg

by Leave a Comment

What Happened
In November 2024, HubSpot launched Breeze, a fully integrated AI platform combining Copilot functionality, Breeze Agents, and over 80 embedded AI features. Designed to eliminate inefficiencies in go-to-market (GTM) operations, Breeze delivers capabilities ranging from automated lead follow-ups and contextual sales recommendations to predictive forecasting and pipeline optimization. This release positions HubSpot as a major force in AI-driven CRM, offering both breadth and depth of AI features inside a single platform.

Who’s Impacted
B2B – Sales teams can leverage Breeze’s AI agents for prospecting, qualification, and nurturing, freeing up reps to focus on relationship-building and closing deals.
B2C – Customer service and marketing teams gain tools to deliver personalized experiences at scale, from tailored campaigns to AI-assisted service interactions.
Nonprofits – Fundraising and outreach teams can automate donor engagement, track impact metrics more efficiently, and improve forecasting for donation drives.

Why It Matters Now
Fact: Breeze integrates over 80 AI features in a unified CRM environment.
Tactic: Audit your current sales and marketing workflows to identify the highest-impact AI features for immediate deployment—such as automated outreach or predictive deal scoring.

Fact: AI-driven forecasting improves GTM planning and resource allocation.
Tactic: Use Breeze’s predictive models to refine quarterly targets and anticipate shifts in lead conversion rates.

KPIs Impacted: Sales cycle length, forecast accuracy, lead-to-close ratio, pipeline velocity, customer retention rate, campaign ROI.

Action Steps

  1. Conduct a CRM workflow review to pinpoint top automation opportunities.
  2. Train teams on high-value Breeze features to accelerate adoption.
  3. Integrate Breeze predictive analytics into strategic GTM planning.
  4. Track and benchmark KPIs quarterly to quantify AI’s impact.

“Breeze doesn’t just add AI to CRM—it builds AI into the DNA of how sales and marketing operate.” – Chat GPT

References
HubSpot. (2024, November). HubSpot launches new AI Breeze plus hundreds of product updates. Retrieved from https://ir.hubspot.com/news-releases/news-release-details/hubspot-launches-new-ai-breeze-plus-hundreds-product-updates

Disclosure:
This article is #AIgenerated with minimal human assistance. Sources are provided as found by AI systems and have not undergone full human fact-checking. Original articles by Basil Puglisi undergo comprehensive source verification.

LinkedIn Sponsored Articles, Adobe Premiere Pro AI Speech Enhancement, and the Google Core Update

by Leave a Comment

LinkedIn continues to evolve as a content platform, Adobe brings AI precision into video editing workflows, and Google shakes up the search landscape with another core update. Together, these shifts redefine how content is created, distributed, and discovered in real time. For marketers and communicators, the alignment matters because it directly connects storytelling, technical delivery, and audience trust into one continuous cycle. The value shows up in measurable terms like higher quality leads, shorter campaign production cycles, improved organic visibility, and stronger click through rates.

LinkedIn now extends its credibility as the professional network of record by giving marketers access to Sponsored Articles. Unlike quick ads or promoted posts, Sponsored Articles are long form, content rich placements that appear directly in the feeds of targeted professionals. The model allows brands to scale thought leadership by embedding their insights inside the platform where business decisions are already happening. The demand for trustworthy B2B content is rising and Sponsored Articles tap that expectation by positioning companies as educators first, sellers second.

Adobe Premiere Pro strengthens its role as a production cornerstone with new AI speech enhancement features. Marketers who depend on video storytelling often lose valuable time to poor audio quality or expensive post production fixes. By automating clarity, cleaning background noise, and sharpening voices, Premiere Pro reduces editing cycles while improving viewer experience. The tool is not just about saving hours in the editing bay. It is about delivering professional grade content that holds attention, drives engagement, and elevates brand perception.

Google’s October core update, which continues into November, is another reminder that the search ecosystem is a moving target. Sites built on thin, outdated, or untrustworthy content feel the impact quickly while those investing in expertise and authority see stronger visibility. This is Google reinforcing its message that content must not only be helpful but also be credible and trustworthy. Publishers that adapt win impressions and clicks while laggards face shrinking visibility.

“Young people are using TikTok as a search engine. Here’s what they’re finding.” — The Washington Post, March 5, 2024

This reminder from earlier in the year underscores why every channel decision matters. Social platforms train expectations for immediacy and relevance. AI tools set standards for speed and personalization. Search engines define the rules of discoverability. Together, they create the operating system for digital communication. Factics in this moment highlight that sponsored articles reduce cost per lead by up to 35 percent when supported by strong creative, AI audio tools can cut production time by 30 percent, and content aligned to Google’s E E A T framework increases visibility by more than 80 percent after a recovery period. These are not abstract benefits. They are trackable outcomes tied to pipeline growth, campaign efficiency, and discoverability.

Best Practice Spotlight

Gong and LinkedIn Sponsored Content
B2B SaaS provider Gong uses LinkedIn Sponsored Content and Conversation Ads to target high intent professionals with ungated whitepapers and webinars. This campaign strategy produces a 35 percent increase in marketing qualified leads and demonstrates how precise targeting paired with value first content accelerates trust and conversions.

Healthline and Google Core Updates
Healthline undertakes a sweeping content audit guided by Google’s principles of expertise, authoritativeness, and trustworthiness. Articles are updated by medical professionals, author bios are expanded with credentials, and outdated content is removed. This proactive alignment with quality standards results in an 80 percent recovery of traffic and search visibility, reinforcing that authority driven updates deliver measurable returns.

Creative Consulting Concepts

B2B Scenario
Challenge: A mid market software firm struggles with low engagement on gated whitepapers.
Execution: Repurpose insights into LinkedIn Sponsored Articles targeting vertical specific decision makers with narrative rich content.
Expected Outcome: Generate a 25 percent increase in qualified leads while reducing cost per acquisition.
Pitfall: Overly promotional tone risks being ignored by readers seeking substance over sales pitch.

B2C Scenario
Challenge: A lifestyle brand’s video campaigns suffer from high bounce rates due to poor audio quality.
Execution: Use Adobe Premiere Pro’s AI speech enhancement to clean dialogue and improve listening experience across all product demo videos.
Expected Outcome: Increase average watch time by 20 percent and boost click through rates on shoppable video content.
Pitfall: Relying solely on automation may overlook the nuance of emotional tone in voice delivery.

Non Profit Scenario
Challenge: An advocacy organization loses visibility after Google’s core update penalizes thin resource pages.
Execution: Conduct a structured audit to enrich articles with expert quotes, add author credentials, and remove low quality content.
Expected Outcome: Regain 70 percent of search visibility within six months and raise online donations by 15 percent through improved credibility.
Pitfall: Without continuous content review the gains may erode with the next algorithm adjustment.

Closing Thought

When LinkedIn strengthens authority, Adobe improves clarity, and Google sharpens standards, the alignment shows one truth. Authority, precision, and trust are not separate workflows but one marketing rhythm that drives measurable growth.

References

Adobe. (2024, October 15). Adobe MAX 2024: New AI powered features for Premiere Pro.

Google Search Central. (2024, October 9). October 2024 core update rolling out.

LinkedIn. (2024, April 16). The B2B edge: Building a brand that drives performance.

LinkedIn Marketing Solutions. (2024, June 12). How a B2B SaaS company used LinkedIn to generate high quality leads.

MarketingProfs. (2024, May 29). B2B content marketing: Key benchmarks for 2024.

Search Engine Journal. (2024, October 10). Google releases October 2024 core algorithm update.

Search Engine Land. (2024, May 15). How a health site recovered 80 percent of its traffic after the helpful content update.

Search Engine Roundtable. (2024, October 17). Early Google October 2024 core update volatility and tremors.

The Verge. (2024, October 15). Adobe’s new AI tools for Premiere Pro can automatically add sound effects and improve bad audio.

AI in Workflow: Executive Strategy Transformed by Autonomous AI Agents #AIg

by Leave a Comment

What Happened
In October 2024, Microsoft launched a new class of autonomous AI agents capable of executing complex business processes end-to-end without ongoing human intervention. Positioned as a direct competitor to Salesforce’s Agentforce, these agents are designed to operate across multiple enterprise functions—from operations and sales to customer service—using predictive planning, data-driven decision-making, and integrated workflow execution. This move marks a significant step toward embedding AI deeper into strategic decision cycles, not just tactical task management.

Who’s Impacted
B2B – Enterprise leaders gain the ability to delegate multi-step operational workflows to AI, freeing human teams to focus on high-value strategy and innovation.
B2C – Customers experience faster resolution times, more consistent brand interactions, and improved personalization as processes are streamlined by AI.
Nonprofits – Lean organizations can automate administrative and outreach workflows, allowing more resources to be dedicated to mission-focused initiatives and stakeholder engagement.

Why It Matters Now
Fact: Autonomous AI agents enable enterprises to complete processes from start to finish without human handoffs.
Tactic: Identify one or two low-risk, high-value workflows—such as invoice processing or lead qualification—to pilot autonomous execution and measure efficiency gains.

Fact: Predictive planning features allow AI to anticipate needs and allocate resources accordingly.
Tactic: Integrate predictive models with CRM and ERP systems to improve forecasting accuracy and operational agility.

KPIs Impacted: Process automation rate, workflow completion time, operational cost reduction, customer resolution time, forecast accuracy, strategic initiative throughput.

Action Steps

  1. Select pilot workflows with clear success metrics and minimal compliance risk.
  2. Define measurable KPIs for agent performance and assess quarterly.
  3. Integrate autonomous agents into existing tech stacks for seamless execution.
  4. Establish governance protocols for exception handling and oversight.

“When AI takes over the execution layer, leaders can focus on steering strategy instead of managing steps.” – Chat GPT

References
GeekWire. (2024, October 21). Microsoft unveils new autonomous AI agents in advance of competing Salesforce rollout. Retrieved from https://www.geekwire.com/2024/microsoft-unveils-new-autonomous-ai-agents-in-advance-of-competing-salesforce-rollout/Disclosure:
This article is #AIgenerated with minimal human assistance. Sources are provided as found by AI systems and have not undergone full human fact-checking. Original articles by Basil Puglisi undergo comprehensive source verification.

TikTok Search, Canva Video AI, and HubSpot Marketplace: Converting Discovery Into Scalable Action

by Leave a Comment

TikTok keeps climbing as a search engine, Canva pushes its AI video editing beta into creative pipelines, and HubSpot revamps its App Marketplace with a wave of integrations. Each development lands in September, but together they map the way brands find audiences, create assets, and build performance systems. Discovery starts in TikTok’s search bar, where Gen Z types queries instead of keywords into Google. Creative assets scale faster in Canva’s AI video editor, which transforms campaign testing into a real-time loop. HubSpot closes the circuit by expanding integrations that feed CRM, marketing, and SEO execution with tighter data flows. The connection is visible in KPIs: content cycle time falls by 25 to 40 percent, campaign CTRs rise double digits from A/B testing variants, and search-driven visibility and conversions lift in the 15 to 30 percent range as integrations optimize the flow.

Factics prove how discovery converts into action. TikTok search rewards relevance and credibility, not just reach. The tactic is to seed content with expert-backed insights and trending hashtags so each clip answers a query as if it were a mini-FAQ. The measurable outcome is sustained discovery, with reply volumes climbing and search-driven traffic boosting sales by double digits when content aligns to popular question formats. Canva applies the same velocity logic to video. Its AI editing beta shortens production cycles by automating cuts, resizing, and transitions, allowing marketers to deploy multiple variants instead of one. The KPI is speed and performance. Campaigns using AI video editing deliver a 15 percent increase in CTR because creative versions match diverse audience segments. HubSpot’s marketplace expansion ties it together with more than 100 new integrations, including SEO and automation tools. The tactic is to connect CRM, search data, and campaign production in one place so every query or engagement event informs the next creative push. The outcome is clear: cost per acquisition declines while lead quality improves because every tool speaks the same data language.

“Young people are using TikTok as a search engine.” — The Washington Post

The narrative is alignment. TikTok turns into a discovery engine where authority is measured by clarity. Canva accelerates creative velocity so campaigns can keep pace with what TikTok search uncovers. HubSpot ensures the captured demand is nurtured, scored, and reactivated with integrations that keep SEO and automation connected. The KPIs compound across the funnel: discovery grows through TikTok search, engagement lifts with AI-edited video assets, and conversions climb through a CRM system that scales with integrations.

Best Practice Spotlights

CeraVe ranks in TikTok search.

CeraVe built a vast library of dermatologist-led TikToks designed to answer Gen Z’s most searched skincare questions. Queries like “best cleanser for acne” consistently surfaced CeraVe’s expert-backed content. The result: higher trust, surging engagement, and sales that established the brand as the category leader in TikTok search.

Canva AI video editing accelerates campaign testing.

A consumer tech company integrated Canva’s AI video editing beta into its campaign workflow, producing multiple creative variations from a single shoot. Production time dropped by 25 percent, and CTR across digital ads improved by 15 percent, proving that AI editing delivers both efficiency and performance.

Creative Consulting Concepts

B2B Scenario

Challenge: A SaaS firm generates leads but struggles to align content production with buyer research behavior.

Execution: Use TikTok search analysis to identify trending “how-to” queries, produce AI-edited video explainers in Canva, and route engagement signals into HubSpot workflows.

Expected Outcome (KPI): 20 percent faster lead qualification and 15 percent higher engagement from short-form content linked directly into CRM campaigns.

Pitfall: Over-indexing on TikTok trends risks off-brand messaging; governance must stay central.

B2C Scenario

Challenge: A lifestyle brand needs to stand out in a crowded market while scaling creative without ballooning costs.

Execution: Leverage TikTok as the discovery engine, feed creative prompts into Canva AI editing beta for rapid variant testing, and track campaign performance through HubSpot’s new integrations.

Expected Outcome (KPI): 30 percent higher engagement on TikTok search-driven campaigns, 12 percent increase in click-through from AI-edited videos, and lower cost per conversion through HubSpot’s automation.

Pitfall: Producing too many variants without structured testing can dilute creative learnings.

Non-Profit Scenario

Challenge: An environmental nonprofit wants to capture Gen Z attention but lacks the resources for constant video production.

Execution: Create TikTok search-ready content tied to questions like “how to reduce plastic waste,” repurpose raw clips with Canva AI video editing for multiple variants, and integrate results into HubSpot to trigger segmented donor communications.

Expected Outcome (KPI): 10 percent boost in donor sign-ups, 8 percent increase in repeat engagement, and better SEO visibility through HubSpot’s expanded marketplace tools.

Pitfall: Messaging overload in AI variants risks confusing supporters; simplicity drives clarity.

Closing Thought

TikTok as search drives discovery, Canva’s AI video editing scales engagement, and HubSpot’s expanded marketplace locks conversion into systemized growth — discovery, creativity, and integration aligning as one measurable engine.

References

Adobe. (2023, August 8). New Adobe research: The rise of TikTok as a search engine.
Search Engine Land. (2024, May 23). The state of TikTok SEO.
The Washington Post. (2024, March 5). Young people are using TikTok as a search engine. Here’s what they’re finding.
Canva. (2023, October 4). Canva unveils Magic Studio: The AI-powered design platform for the 99%.
Adweek. (2024, March 26). Canva expands its AI toolkit with new features for marketers.
TechCrunch. (2024, May 15). Canva launches AI video editing beta to simplify video creation.
HubSpot. (2024, May 21). HubSpot announces over 100 new and updated integrations and a re-imagined App Marketplace to help businesses grow better.
PR Newswire. (2024, June 12). Semrush launches SEO local for HubSpot on the HubSpot App Marketplace.
MarTech. (2024, May 21). HubSpot revamps its App Marketplace with over 100 new integrations.
Ad Age. (2024, May 20). How CeraVe became Gen Z’s favorite skincare brand.
Ad Age. (2024, July 28). AI video editing tools from Canva revolutionize campaign production.

AI in Workflow: Personalized Onboarding, Predictive Retention, and Accelerated Training #AIg

by Leave a Comment

What Happened
On January 9, 2024, industry-wide HR adoption of AI-powered onboarding reached a critical mass, with 68% of U.S. organizations now using AI to personalize onboarding workflows and integrate predictive analytics to flag at-risk hires. Prescott HR’s January 2024 brief highlights how these solutions are being used to accelerate employee ramp-up times, improve cultural fit, and provide role-specific learning pathways. This evolution moves AI in HR from pilot programs into a core operational standard, with reported retention improvements of 82% and a 40% faster time-to-productivity across surveyed companies.

Who’s Impacted
B2B – Enterprises benefit from reduced turnover costs, stronger workforce stability, and better role alignment. HR software vendors can differentiate by embedding predictive retention modules and adaptive learning systems that measure onboarding effectiveness in real time.
B2C – Employees receive tailored training sequences matching their role, skill level, and preferred learning style. Faster assimilation into company culture drives engagement, satisfaction, and early performance gains.
Nonprofits – Mission-driven organizations gain access to scalable onboarding without expanding HR headcount, freeing up resources for program delivery while sustaining staff quality and commitment.

Why It Matters Now
Fact: Prescott HR reports AI integration in onboarding leads to an 82% boost in retention rates.
Tactic: Deploy AI-driven engagement tracking during the first 90 days to proactively identify and address employee concerns before they result in turnover.

Fact: Predictive analytics cut time-to-productivity by 40%.
Tactic: Integrate early skill-gap analysis to assign targeted training modules, reducing ramp-up delays and increasing speed to competency.

Fact: AI-personalized onboarding aligns employee expectations with organizational goals.
Tactic: Continuously refresh AI training content libraries with role-specific scenarios and success benchmarks to maintain relevance.

KPIs Impacted: Retention rate, time-to-productivity, employee engagement score, onboarding completion rate.

Action Steps

  1. Deploy AI onboarding platforms that adapt training content based on role, skill set, and learning style.
  2. Integrate predictive analytics for early identification of at-risk hires.
  3. Track time-to-competency as a primary HR KPI and evaluate improvements quarterly.
  4. Maintain and update AI content libraries in line with shifting company goals and industry needs.

References
Prescott HR. (2024, January 9). AI in HR: Navigating the integration of artificial intelligence into human resources 2024. Retrieved from https://prescotthr.com/ai-hr-navigating-integration-artificial-intelligence-human-resources-2024/

Disclosure:
This article is #AIgenerated with minimal human assistance. Sources are provided as found by AI systems and have not undergone full human fact-checking. Original articles by Basil Puglisi undergo comprehensive source verification.