Antonio Grasso engagement report
@antgrasso - 351K followers on X
Measured over 15 original posts from a 30-day window, last computed on September 2, 2026.
Engagement
A typical post picks up 156 interactions against 351K followers, an engagement rate of 0.044%. Measured over 15 original posts, its engagement rate beats 50% of 6,874 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 2.9K times each, and 5.36% of those impressions turn into an interaction. That is about 0.829% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.3 post a day over the last 30 days, with activity on roughly 50% of days. Most posts go out around 13:00 UTC, and Friday is the busiest day of the week. Of the 15 posts sampled, 93% carry an image or video and 20% link out. The account's strongest tracked post pulled 227 interactions. Recurring topics include #digitaltransformation, #humancenteredai, #platformeconomy.
Measured over 15 original posts from a 30-day window, last computed on September 2, 2026. Recurring tags: #digitaltransformation, #humancenteredai, #platformeconomy.
Compared with accounts its own size
Antonio Grasso's engagement rate beats 50% of the tracked X accounts closest to it in follower count (6,874 accounts, accounts of similar size (decile 8 of 10)). A percentile is spread evenly by construction, so 50 really is the middle of that group and 90 really is its top tenth.
On engagement per impression rather than per follower it beats 91% of the same group. When those two numbers disagree, the gap is about how far its posts travel rather than how people react to them.
Where this sits in the catalog
At 0.044%, Antonio Grasso sits above the 25th percentile of the 66,258 accounts in this comparison. That places it in the below the median band, which runs 0.016% to 0.1%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.002% |
| 25th percentile | 0.016% |
| 50th percentile | 0.1% |
| 75th percentile | 0.499% |
| 90th percentile | 2.09% |
| 99th percentile | 119.6% |
This ruler is the whole measured catalog, not a size-matched group: it shows where the raw rate falls across every account we can measure, all of which are large. For a like-for-like comparison, read the size-band percentile above instead. See how the bands are built
Posting timing
This account posts most often around 13:00 UTC, and Friday is its busiest day of the week. The bars below are the catalog-wide pattern, with this account's own busiest slot marked. They do not show how this account performs at each hour: we keep one aggregate per account, not one per hour, so that measurement does not exist in our data.
Show engagement by hour posted, utc as a table
| Hour (UTC) | Vs author median | Posts |
|---|---|---|
| 00:00 UTC | -1% | 89K |
| 01:00 UTC | -2% | 90K |
| 02:00 UTC | -3% | 88K |
| 03:00 UTC | -4% | 94K |
| 04:00 UTC | -5% | 76K |
| 05:00 UTC | -4% | 75K |
| 06:00 UTC | -5% | 86K |
| 07:00 UTC | -5% | 93K |
| 08:00 UTC | -4% | 108K |
| 09:00 UTC | -4% | 124K |
| 10:00 UTC | -3% | 129K |
| 11:00 UTC | -3% | 141K |
| 12:00 UTC | -3% | 154K |
| 13:00 UTC | -3% | 167K |
| 14:00 UTC | -4% | 173K |
| 15:00 UTC | -2% | 176K |
| 16:00 UTC | -3% | 171K |
| 17:00 UTC | -3% | 159K |
| 18:00 UTC | -2% | 149K |
| 19:00 UTC | -2% | 141K |
| 20:00 UTC | -1% | 131K |
| 21:00 UTC | 0% | 116K |
| 22:00 UTC | -2% | 100K |
| 23:00 UTC | -1% | 90K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 393K |
| Monday | +1% | 483K |
| Tuesday | -2% | 520K |
| Wednesday | -3% | 472K |
| Thursday | -2% | 430K |
| Friday | -3% | 447K |
| Saturday | +2% | 393K |
Formats this account uses
Its own posting mix on the left, and what each of those formats does across every account we track on the right. Only formats where the effect clears our publish test appear here, so an empty row is a format we could not measure rather than one that does nothing.
| Format | This account | Catalog effect | 95% interval | Accounts behind it |
|---|---|---|---|---|
| Image or video | 93% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 20% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 93% of this account's sampled posts carry an image or video. Across the catalog, posts with an image or video run 111% above the same accounts' other posts.
- 20% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts.
- Its average post runs 903 characters, which falls in the over 280 characters band. Across the catalog, posts over 280 characters run 15% above the same accounts' other posts.
These are catalog-wide differences applied to this account's own posting mix, not a measurement of how each format performs for this account specifically. We keep one median per account, not one per format per account, so the second thing is not something this data can tell you.
Best tweets
- Aug 30, 2026
Digital transformation often fails because we look for technology before we look inside an organization and see how work really happens. The people doing the work can reveal friction that plans and process maps often miss until implementation begins. Microblog by @antgrasso Before redesigning a workflow, watch it. A new AI-enabled process may look efficient in a project plan. The people doing the work may know that customer exceptions arrive outside the system or that one handoff already creates delays. Those details can change the design. What happens if you ask the people who do the work before asking them to adapt to the new way of working? Observe how the work happens today. Listen where people expect friction. Their suggestions can help refine the redesign before it reaches a larger part of the organization. Then test it with them. Digital transformation is easier to scale when reality has already had a chance to challenge the design. Before changing how people work, look inside and listen to the people who already know where the friction lives.
- Aug 24, 2026
AgentOps is the discipline that keeps AI agents visible and under control once they begin working inside real business processes. An agent may perform well during a pilot and still behave differently in production. Data changes. Workflows are updated. Microblog by @antgrasso A permission that looked harmless during testing may create risk when the agent starts acting across live systems. Consider an AI agent that updates supplier records. Before deployment, the company defines which fields it can change. Its behavior is tested with realistic cases, including incomplete information and unexpected requests. Once the agent is active, the work continues. Teams need to see what it did and where review is required. When behavior moves outside accepted limits, they must be able to adjust the agent or stop it. This is the purpose of AgentOps: turning autonomous activity into something the organization can observe and manage with confidence. An AI agent should never be easier to deploy than to stop.
- Aug 29, 2026
Still expecting the CIO to say “mea culpa” when resilience fails? You may have once. Today, digital resilience is a board responsibility because disruption reaches operations and business continuity, far beyond the data center and IT function alone. Microblog by @antgrasso Why has the responsibility moved? Because a digital failure rarely stays inside IT. If a cloud outage stops online orders, restoring the infrastructure is a technology problem. Deciding which business services must return first is an executive leadership decision. If a cyberattack forces a factory to slow production, the CIO can coordinate recovery of the systems. But who decides how long operations can tolerate the disruption? Who decides what customers should be told and which business risks are acceptable? Executive leadership needs to make those decisions. Digital resilience now depends on knowing which operations the company cannot afford to lose and making sure technology recovery supports them. The CIO remains central, but cannot carry that responsibility alone. And resilience starts before the incident. Investment choices and continuity plans need agreement while everything is still working. Accountability must already be clear. When disruption arrives, the CIO can restore technology. Executive leadership must already know how the business will continue.
- Aug 23, 2026
Rarely is the right moment to adopt a technology marked by a clear date. It leaves signals, and leaders need to learn how to read them. Think of those signals as a trail of clues, not a perfect map. Microblog by @antgrasso about #TechStrategy and #DigitalTransformation A new technology may already offer a concrete business advantage, while the organization still lacks the capacity to absorb it. Another technology may look promising today but still be changing so quickly that waiting preserves valuable options. This is why timing cannot be reduced to asking whether a technology is mature. Leaders also need to understand what moving now could improve inside the business and what adopting too early could disrupt. Waiting deserves the same scrutiny: does it preserve flexibility, or does it slowly give away strategic ground? There will rarely be a moment when every signal points in the same direction. The decision is to recognize when enough of them do.
- Aug 22, 2026
If a platform does not serve people, what exactly is it scaling for? Human-centered design should shape the business model from day one. Growth changes the relationship between a platform and the people who depend on it. Microblog by @antgrasso about #PlatformEconomy A marketplace can expand quickly while sellers stop understanding why their visibility changes. A professional platform can improve engagement while users begin questioning how their personal data is handled. Those are design questions, not side issues. What happens when people no longer trust the rules behind access or recommendations? And what if moderation itself feels opaque? Leaders need to treat transparency and data rights as part of platform architecture. Fair access also needs to be visible enough that people can understand how decisions affect them. Community health matters for the same reason. If harmful behavior is rewarded because it drives activity, short-term growth can slowly damage the environment that made the platform useful in the first place. Human-centered design gives leaders a practical test for every platform decision: does this help people participate with confidence and dignity? A platform can scale technology very quickly, but its future depends on the people who choose to stay.
- Aug 27, 2026
Accessing an online newspaper and accessing a bank account do not carry the same identity risk. Security should rise with the consequences. It sounds obvious. Yet identity security is still frequently treated as something that happens mainly at login. Microblog by @antgrasso In financial services, the risk continues after access. A customer may authenticate correctly and then behave very differently during the session. A payment may suddenly fall outside an established pattern. Later, a recovery request could indicate that someone else is trying to take control of the account. Should all these moments receive the same level of security? Probably not. A low-risk action may require relatively simple assurance. Moving money or changing recovery details can justify stronger verification because the potential impact is much higher. AI-supported identity intelligence can help by connecting signals across the customer journey and bringing unusual changes to the attention of fraud or security teams before damage spreads. The decisions still need to remain traceable. When money or access is at stake, financial institutions need to understand why additional verification was requested and preserve evidence for review.
- Aug 21, 2026
A foundation model gives AI general capability, but your business context turns that capability into something useful in daily operations. So what actually makes a general-purpose model useful inside your company? Usually, it is the context around it. Microblog by @antgrasso The model may know a lot, but it does not know your internal policies or current business data. It also lacks the practical context of how your teams work. If RAG and MCP sound familiar, this is where they fit. Retrieval-Augmented Generation can bring relevant enterprise knowledge into the response, while Model Context Protocol can give AI standardized access to approved data sources and tools. Then the operational questions start. Can people trust the answer? And what happens when the business context changes? Testing before deployment helps expose weak points. Once the system is active, monitoring shows whether its behavior still matches business needs. Human review remains important where judgment has consequences. There is also a cost dimension. More context and repeated interactions consume more tokens, so organizations need to understand what useful personalization is really costing them. A foundation model gives capability. Your business gives it direction.
- Aug 31, 2026
The more AI helps you think, the less reason you have to stop thinking for yourself. Use its speed to sharpen your judgment and push your reasoning further. AI assistance should raise human thinking as technology takes on more work. Microblog by @antgrasso #HumanCenteredAI AI can summarize a report in seconds or surface a pattern you may have missed. It can even suggest what to do next. Useful? Absolutely. An AI assistant might recommend reducing investment in a customer segment because performance is falling. The pattern could be accurate, while a recent campaign change or a temporary market constraint tells a different story. AI accelerates the analysis. You still need to understand the context before acting. Question what you receive. Verify what matters. Own the decision you make. Those three steps help people grow with AI instead of simply following it. AI can raise the speed of analysis. Our judgment has to rise with it.
- Sep 1, 2026
You are happy with the benefits digital transformation brings to your company, but now you are discovering a “hidden tax”: digital friction. It appears when people repeat work, switch between systems, or wait for information, consuming time and capacity. Microblog @antgrasso Let me explain digital friction “papele papele,” as we say in Naples: plain and simple. An employee updates a customer record in the CRM, then copies the same information into another system because the two platforms do not communicate. Five minutes disappear. Multiply that by hundreds of people every day and the cost is no longer small. Or take an approval process. The request is already inside a digital workflow, but someone still exports a file and sends it by email for another check. The technology is there. The work is still moving twice. Digital friction often hides in repeated work or waiting between systems. The answer is not another transformation project. Start by watching how work actually moves and identify where people repeat actions or leave one system just to complete a task in another. Then remove the friction that adds no judgment. A process can be digital and still waste time. That waste is the “hidden tax” you keep paying until someone looks closely enough to see it.
- Aug 28, 2026
Want to scale AI across your company but worried about the consequences? Three simple rules can help: who owns it, what is shared, and how. Get those decisions right early, and scaling becomes easier to govern, fund, and deliver across the business. Microblog by @antgrasso Start by removing ambiguity. Who owns AI and funds the priorities that move beyond experimentation? What capabilities and controls should business teams share? How does a promising use case reach production without rebuilding the same setup each time? Ownership gives AI initiatives a place in the business, with responsibility for funding and direction. Shared platforms and governance let teams reuse approved capabilities instead of creating isolated solutions around every use case. Then delivery has to make the model operational. Teams need support to move from an idea to a process that can run reliably after launch. The questions are simple. The answers define whether AI can scale beyond a collection of pilots.
Ranked by total interactions across everything we have tracked for this account, which is a longer history than the 30-day window the rates above use. The multiple compares each post to this account's own median.
Recurring topics
The most frequent hashtags in the sampled posts. They describe what this account writes about; they are not a performance signal, and the catalog-wide breakdown on the hub shows how little hashtag count moves.
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Reading these numbers
A typical post picks up 156 interactions against 351K followers, an engagement rate of 0.044%. Measured over 15 original posts, its engagement rate beats 50% of 6,874 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 2.9K times each, and 5.36% of those impressions turn into an interaction. That is about 0.829% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.3 post a day over the last 30 days, with activity on roughly 50% of days. Most posts go out around 13:00 UTC, and Friday is the busiest day of the week. Of the 15 posts sampled, 93% carry an image or video and 20% link out. The account's strongest tracked post pulled 227 interactions. Recurring topics include #digitaltransformation, #humancenteredai, #platformeconomy.
- What is Antonio Grasso's engagement rate on X?
- Antonio Grasso (@antgrasso) has an engagement rate of 0.044%, based on the median interactions across 15 original posts from the last 30 days against 351,305 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.044%, Antonio Grasso sits above the 25th percentile of the 66,258 accounts in this comparison. Those comparison accounts are all large ones, because our scanning cadence is weighted towards big accounts, so this is a ranking among peers of similar scale rather than a ranking across X.
- Does @antgrasso have real engagement?
- Its engagement rate beats 50% of the tracked X accounts closest to it in follower count (6,874 accounts), which puts it in the middle of its size range group. Ranking inside a size band matters because engagement rate falls as accounts grow, so a raw rate would mostly re-measure the follower count. It is a starting point for a look at follower quality, not a verdict on it.
- When does @antgrasso post?
- Most posts go out around 13:00 UTC, and Friday is its busiest day, at roughly 1.3 posts per day across the measured window.