SUCCESS

Most AI tools promise to save time. Few explain exactly how. GetSolved.ai sits in a specific category – it’s a document verification and improvement platform aimed at people who already have text and need to check, refine, or verify it before it goes anywhere important. Not a writing generator. A pre-submission safety layer, as they describe it themselves.
This review covers what the tool actually does, where it works well, and where it falls short.
What The Platform Is
Users paste in text or upload a document, run it via available checks, and obtain feedback on AI patterns, originality, factual accuracy, grammar and clarity. The nature of GetSolved is that it’s a multi-detector platform.This procedure is for a unique use case: you have a draft and you need to ensure it’s clean before you submit it, publish it, or deliver it to a client.
It’s a platform between your initial draft and your final submission.” It is not a replacement for thinking or writing. It picks up on what you overlook by being too close to your own work.
The main tools available:

AI Detector – shows an overall AI score (percentage), sentence-level diagnostics, and a cross-platform risk check against major detectors
AI Humanizer – rewrites flagged AI-sounding passages with adjustable tone, formality, and humanization strength (scale of 5 to 10)
Plagiarism Checker – scans for matching content across web and academic sources
Grammar Checker – fixes grammar, punctuation, and tone with contextual explanations
Fact Checker – flags unsupported or potentially inaccurate claims in the document
Paraphraser – rewrites sections for clarity or to reduce similarity scores
Summarizer – condenses long documents into key points
AI Chat – conversational assistant for brainstorming or document interaction
Personal Expert Review – a live human editor reviews and refines your text before final submission

The AI Detector and Humanizer are the core features. Everything else supports them.
How the AI Detector Works
When you paste text, the detector returns an overall AI score (displayed as a percentage with a circular chart), a sentence-level breakdown highlighting which specific sentences read as AI-generated, and a cross-platform risk matrix that shows how the text would likely score across tools like GPTZero and Originality.ai simultaneously.
Score ranges: Authentic (0-19%), Uncertain (20-39%), AI-generated (40-100%).
One important caveat the platform acknowledges internally: the AI score is a marker for review, not a final verdict. False positives happen – formal academic writing, ESL writing, and highly structured professional text can sometimes return elevated AI scores even when written entirely by a human. The sentence-level analysis helps identify where exactly the concern sits, which makes it more useful than a single aggregate number.
You can download a full PDF report of the results. Students commonly use this as documented proof of originality when submitting work.
Workflow in Practice
The typical sequence most users follow:

Run the AI Detector first to get a baseline score and identify flagged sentences
Use the Humanizer on problem sections – choose tone (Academic, Business, Formal, Casual), formality level, and humanization strength
Compare source text vs. improved text using the built-in before/after view with “Show Changes” toggle
Run the Plagiarism Checker after humanizing (rewrites change similarity scores, so running it earlier gives inaccurate results)
Run Grammar Checker last, when the content is stable
Download the AI report or copy the final text back to your document

One limitation worth knowing: the tools are currently separate. There’s no single continuous pipeline that runs all checks sequentially on one document. Users move between tools manually, which adds steps. The platform has this workflow integration on its development roadmap, but it’s not available yet.
Accuracy: What It Gets Right and Where It Fails
The fact-checking function is the clearest differentiator from competitors. Most grammar and originality tools don’t attempt to verify factual claims at all. GetSolved’s Fact Checker flags statements that appear unsupported or potentially inaccurate, which matters for research-heavy documents where a single wrong statistic can undermine the whole piece.
That said, the fact-checker works best on general and academic claims. Highly technical, domain-specific assertions in fields like medicine or law may not have enough web coverage for the AI to verify reliably. Treat its output as a starting point, not a final clearance.
The Humanizer performs well with adjustable presets. Users who choose “Original Voice” (selected by over 41% of users on the platform) report the tool preserves their tone better than generic rewriters. The before/after comparison makes it easy to spot where the rewrite changed more than intended.
False positive risk is real, particularly for ESL writers and those producing highly formal text. If the detector flags a human-written passage, the sentence-level view usually reveals why – often it’s an “AI Clichés” pattern the system detected.
Pricing

Plan

Price

What You Get

7-Day Access

$2.00

Full tool access for one week – functions as a trial

Unlimited Monthly

$39.99 or $19.99/month

Unlimited word processing, all tools, no monthly cap

Unlimited Annual

$39.99 or $7.99/month

Lowest per-month price, billed annually

All plans include the AI Detector, Grammar Checker, Fact Checker, Plagiarism Checker, Paraphraser, Humanizer, and 24/7 support. The 7-day access at $2 functions as a low-friction entry point to test the platform before committing.
After the trial period ends, the subscription rolls into the monthly plan automatically. Cancellation must happen before the next billing cycle – the platform recommends contacting support directly for cancellations and refund requests rather than relying on self-service.
How It Compares to Alternatives

Feature

GetSolved

Grammarly

Turnitin

Originality.ai

AI Detection

Yes

Limited

No

Yes

Cross-platform risk check

Yes

No

No

No

Fact-checking

Yes

No

No

No

AI Humanizer

Yes

No

No

No (refused by design)

Plagiarism Check

Yes

Limited

Yes (stronger)

Yes

Free tier

Yes (demo)

Yes

No

No

Starting price

$2 trial

$12/month

Institutional only

$14.99/month

Grammarly is stronger for pure grammar correction and has broader integrations (browser extension, Google Docs, Word). Platform adds fact-checking and humanization on top, which covers a different workflow need.
Originality.ai is a well-regarded AI detector but deliberately does not offer a humanizer – they consider it contrary to their purpose. GetSolved takes the opposite position, treating detection and humanization as two parts of the same pre-submission process.
Turnitin remains the institutional standard in academic settings. Cross-platform check includes Turnitin-style detection logic as part of its risk matrix, but the outputs aren’t identical. If your institution uses Turnitin specifically, GetSolved gives you a risk preview, not a guaranteed result.
Who Gets the Most Out of It
The platform’s own usage data shows 72.6% of submitted texts are academic or educational. The next largest segment is professional and business writing at 17%. That split reflects who the tool is actually built for.
Most useful for:

Students running a final check before submitting research papers or essays
Researchers adapting AI-assisted drafts for journal submission (academic tone, terminology)
ESL writers who want contextual grammar feedback with explanations, not just corrections
Professionals checking work emails or reports for factual accuracy and grammar before distribution

Less useful for:

Short communications under 300 words – the overhead doesn’t justify it
Highly specialized technical content where fact-checking coverage is thin
Anyone who needs deep integrations (no Chrome extension, no Google Docs add-on, no API)

What’s Missing
The platform doesn’t yet have a continuous workflow workspace – you can’t run a document through detection, humanization, grammar check, and plagiarism check in one session without switching between tools manually. That friction adds up on longer documents.
There’s no browser extension, no Google Docs integration, and no API for business use. Mobile access is available through the responsive web app but there’s no dedicated mobile version.
Expert review availability also varies by language. The human review service works well for English but may reject requests in other languages if no specialist is available.
Final Verdict
Getsolved covers a gap that most writing tools ignore: checking not just how you wrote something but whether what you wrote is accurate, original, and will pass the detectors your audience uses. The fact-checker combined with multi-engine detection is genuinely useful for anyone producing research-heavy documents, and the humanizer gives you a way to address problems rather than just know they exist.
The main friction points – no integrated pipeline, no external integrations, manual switching between tools – are real limitations, not minor complaints. If your workflow depends on staying inside Google Docs or running bulk checks through an API, this isn’t the right fit yet.
For individual users who work with documents that need to hold up to scrutiny before submission, the $2 trial is a low-cost way to find out if it fits how you work.
The post Can GetSolved AI Make Your Workday More Productive? appeared first on Addicted 2 Success.

For most of business history, the process of earning trust began after a conversation started. But today, your business is often evaluated before you even interact with the person you’re trying to reach:

Email providers inspect your domain.
Telecom carriers analyze your calling patterns.
Search engines and social platforms evaluate your history.

Then, if your communication makes it through those systems, the recipient performs another assessment: is this person really who they claim to be?
That question is becoming harder to answer.
The Federal Trade Commission received more than one million reports of impersonation scams in 2025. Consumers reported losing $3.5 billion to them (nearly three times as much as they reported in 2020). Technology used to imitate legitimate organizations is becoming more accessible, while the channels businesses use to reach people are becoming more crowded.
AI is accelerating both sides of that problem. It can help bad actors create convincing messages, voices, images, and identities at extraordinary scale. It also enables legitimate businesses to fill feeds and inboxes with polished communication that lacks genuine relevance.
Audiences may not always know whether AI created something, but they can often recognize when an attempt at personalization feels manufactured or insincere. The result is declining consumer trust, along with increased pressure on businesses to change how they establish trust in the first place.
Businesses no longer earn trust only through what they say and do. They must also prove their identity, legitimacy, and relevance before they are given the opportunity to say anything at all.
Trust Has Moved Upstream
Consider what happens when your phone rings from an unfamiliar number.
Before answering, you may look at the caller ID, notice the area code, check for a warning label, or simply decide that an unexpected call is not worth the risk. You are evaluating the communication without knowing what the caller wants.
The same thing happens when an email arrives. You examine the sender, subject line, domain, formatting, and request before seriously considering the message itself.
These are sensible behaviors. Impersonation scams have become sophisticated, and AI makes it easy for anyone to create professional-looking content, so people have learned that presentation is no longer reliable evidence of legitimacy.
This creates a new challenge for honest businesses: your communication is being judged according to standards that are increasingly easy for bad actors to imitate.
But the risk here isn’t just that bad actors will successfully pass themselves off as legitimate businesses. It’s also that legitimate businesses may be mistakenly identified as bad actors.
Your intention may be legitimate. Your offer may be valuable. But none of that matters if the interaction looks suspicious, generic, or irrelevant before it begins.
Leaders therefore need to think about trust differently. It is no longer only a brand attribute or the product of a good customer experience. Increasingly, trust is also an operational capability.
Every Communication Must Pass Two Tests
Most business outreach now encounters two distinct trust tests.
The first is conducted by systems:

Email platforms use authentication, sender reputation, engagement, and complaint signals to determine where a message belongs.
Telecom providers and analytics engines examine calling behavior and other data to identify potentially unwanted calls.
Advertising and social platforms assess account quality, content, and policy compliance.

These systems exist to protect consumers from widespread fraud and abuse. But automated judgments are imperfect. Legitimate communications can be filtered, blocked, mislabeled, or deprioritized.
The second trust test is conducted by a person.
If the communication gets through, the recipient evaluates whether it is recognizable, relevant, and reasonable.

Do they know who is contacting them?
Does the message match the relationship?
Does the request make sense?
Does the business appear to understand their needs, or has it simply inserted personal details into a template?

Businesses often concentrate almost entirely on this second test. They refine subject lines, sales scripts, offers, prompts, and calls to action. Those things remain important, but they cannot compensate for failing the first test.
The best sales pitch in the world has no value when it lands in a spam folder. A thoughtful call cannot build a relationship if the recipient’s phone identifies it as “Spam Risk.”
That means before modern businesses can persuade, they must establish their right to be considered.
Make Your Business Easier to Verify
A business cannot eliminate skepticism, nor should it try. Healthy skepticism protects consumers. The goal is to make legitimate communication easier to distinguish from illegitimate communication.
That begins with identity consistency.
Your company name, calling numbers, email domains, websites, and public profiles should reinforce one another. A customer should not have to investigate whether the business contacting them is connected to the business they recognize.
It also requires context.
Unexpected requests naturally receive more scrutiny. Whenever possible, establish why the communication is happening. Let customers know what comes next, what number or address may contact them, and how they can independently verify the interaction.
Organizations must also monitor outcomes rather than assume their communications are reaching people as intended.
A sent email is not necessarily a delivered email. A completed dial is not necessarily a recognizable call.
Look at the evidence available to you: delivery, answer, response, complaint, conversion, and opt-out patterns. Investigate meaningful changes rather than immediately responding with more volume.
Performance declines may reflect a weak message. But they may also reveal problems with identity, targeting, reputation, frequency, data quality, or channel selection.
On the phone side, for example, telecom carriers and analytics providers evaluate calling activity to identify potentially unwanted calls. A legitimate business number can be mislabeled, causing a warning such as “Spam Risk” or “Scam Likely” to appear when the company calls. Unless the business is actively monitoring its numbers, it may not realize that its identity is being questioned before anyone answers.
No adjustment to a script can overcome that problem. The business must first understand how its calls appear and address the signals preventing the conversation from beginning.
Protect Trust by Respecting Attention
Verification helps a business gain access. Behavior determines whether it deserves continued access.
Much of the behavior that builds trust can’t be automated, including service recovery conversations and handling complicated objections. These situations call for a person who can actually listen and adjust, not a system executing a script.
Access is also not permanent. Someone who gave a business permission to communicate with them at one point may not want to be contacted later.
Instead of maximizing every contact attempt, businesses should make it easy to ask questions, verify information, and change communication preferences. This may seem counterintuitive, but it actually helps build trust by giving contacts confidence that interacting with your business will not become a future source of frustration.
The Leader’s Responsibility
It is tempting to assign communication trust to marketing, security, compliance, or IT. In reality, no single department controls it.
Marketing shapes promises. Sales determines outreach behavior. Operations manages data. Technology configures systems. Customer service maintains consumer confidence.
Leadership decides whether short-term activity targets matter more than the long-term reputation of the organization. This includes deciding how AI is used—will it improve the value of outreach, or merely the volume of it?
All of these are operational choices, but they are also trust choices.
Finally, leaders should understand how their businesses appear across communication channels, what systems stand between them and their customers, and what happens when those systems get something wrong.
In a world filled with manufactured identities, synthetic content, automated outreach, and endless claims, the companies that stand apart may not be the ones that communicate most frequently.
They will be the ones people can recognize, verify and believe.
The post Your Customers Are Evaluating You Before You Ever Speak appeared first on Addicted 2 Success.

War is changing fast, and Ukraine is rapidly adapting existing weapons to new and unexpected used. This flexibility may be essential in future wars.

Shakira’s “Dai Dai” brings the singer back to the U.K.’s Official Physical Singles chart after an absence of more than a decade and a half.

It’s not the purchase price that’s the biggest obstacle, but rather the continuing costs.

Warren Buffett’s Berkshire Hathaway has invested in plenty of dividend stocks over the years. Dividend stocks remain popular among investors as they allow you to create a passive income stream at a low cost. 

“Dividend stocks deliver regular payments to investors and can be an essential part of portfolios,” according to Charles Schwab’s investing education team. 

This is precisely what Chevron is doing for Berkshire Hathaway right now.

Between Chevron’s rising payout and Berkshire’s massive stake, the arrangement now sends more than half a billion dollars into Buffett’s coffers every year. 

Berkshire’s colossal Chevron dividend payday

Berkshire Hathaway owns 84,375,856 shares of Chevron. At Chevron’s current annual dividend of $7.12 per share, the stake generates roughly $601 million in cash every year, paid out in quarterly installments.

With CVX stock trading near $206 a share this week, Berkshire’s Chevron position is worth close to $17.3 billion. 

More Dividend Stocks:

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Berkshire trimmed part of its Chevron holding earlier in 2026, selling around $8 billion worth of stock.

Even after that sale, Chevron remains one of Buffett’s largest energy bets, sitting alongside Occidental Petroleum in Berkshire’s portfolio.

Chevron is a Dividend Aristocrat 

Chevron has raised its dividend for 39 straight years, growing the payout at roughly a 6% compound annual rate over the past 15 years, Chevron chairman and CEO Mike Wirth said at the company’s annual strategic conference.

Wirth told analysts:

“We increased our dividend during COVID when some of our peers cut their dividends.”

The CEO pointed to the dividend streak as proof of the company’s commitment to steady payouts through market cycles.

Related: BofA just turned on Exxon in favor of Chevron

That track record earns Chevron a spot among the S&P 500 dividend aristocrats, a group of companies that have raised dividends for at least 25 consecutive years.

In the second quarter of 2026, Chevron reported $19.7 billion of cash flow from operations excluding working capital and $15.4 billion of adjusted free cash flow.

By comparison, quarterly dividend expense was about $3.5 billion, indicating a payout ratio of 20%. 

Chevron also cut debt by more than $8 billion during the quarter and hit its $3 billion cost reduction target six months ahead of schedule.

Mike Wirth, CEO of Chevron is focused on dividend growthBloomberg/Getty Images

CVX stock dividend ratios investors should know

Annual dividend: $7.12 per share

Dividend yield: 3.47% 

Payout ratio: 20% of FCF

Consecutive years of dividend increases: 39

10-year dividend growth rate: 5.2%

Payment frequency: quarterly, with the next payment due Sept. 10, 2026

What Wall Street thinks about CVX stock

Analysts remain mostly upbeat on Chevron even after its 2026 rally. 

Barclays analyst Betty Jiang kept an Overweight rating on the shares, telling clients that Chevron’s guidance underscores significant free cash flow expansion with room for more growth in 2027. 

Morgan Stanley analyst Devin McDermott has also raised his price target while keeping an Overweight rating on the stock.

Carillon Tower Advisers, in its first quarter 2026 investor letter, said Chevron has significant exposure to spot commodity prices, a factor the firm sees as a key driver behind the stock’s performance this year.

Why dividend stocks matter for portfolios

Dividends have made up about one quarter of the S&P 500’s total returns over the past 50 years, according to research from American Century Investments. 

The firm notes that dividend-paying stocks can also help diversify a portfolio and reduce volatility, particularly when share prices stall out for long stretches.

Schwab makes a similar case with numbers. 

A hypothetical $10,000 investment in an S&P 500 index fund at the end of 1993 would have grown to more than $182,000 by the end of 2023 with dividends reinvested, compared with only $102,000 if those dividends were never reinvested.

That is the math working in Chevron’s favor. 

A $601 million yearly dividend check will not move the needle much for a conglomerate the size of Berkshire Hathaway. 

But it explains why a longtime dividend grower like Chevron still earns a spot in some of the most selective portfolios on Wall Street, including Buffett’s own.

Related: How much to invest in AT&T stock for $1,000 in annual dividends

After 45 years in business, a beloved restaurant chain has suddenly cut its footprint in half, closing locations after saying the cost of operating them was no longer sustainable.

The move leaves the longtime brand with just two restaurants and marks a major pullback for a company that built its reputation around breakfast, brunch, and baked goods, earning a loyal following among locals and tourists.

The closures come as restaurant operators across the country navigate higher costs and customers becoming more selective about dining out.

Founded in 1981 in New York City’s Manhattan neighborhood, the company began as a small bakery-kitchen before growing into a well-known restaurant brand. Its homemade preserves and homebaked goods helped establish a loyal following, eventually turning the business into a fixture of the city’s dining scene. The business is currently managed by RBM Restaurant Group, which also owns Docks Oyster Bar.

Sarabeth’s closes half of its restaurants

Sarabeth’s has closed two of its New York City restaurants, leaving the breakfast chain with only two locations.

The impacted restaurants include:

Sarabeth’s Park Avenue South: 381 Park Ave S.

Sarabeth’s Greenwich Village: 100 W Houston St.

The company attributed the closures to the financial challenges of operating the two locations.

“Unfortunately, despite everything that was working well, the economics of running these two locations are no longer sustainable,” Sarabeth’s wrote in a statement shared on Instagram.

The Park Avenue South restaurant opened in February 2013, while the Greenwich Village location opened in November 2024. Both locations have now permanently closed.

The company will continue operating its Upper West Side restaurant at 423 Amsterdam Ave and its Central Park South location at 40 Central Park S.

Sarabeth’s closes two restaurants in New York City.Alexander Spatari / Getty Images

Why Sarabeth’s is closing locations

Sarabeth’s restaurant closures come as operators continue to contend with higher costs and consumers becoming more selective about where they spend their money.

Food away from home increased 3.4% over the 12 months ending July 2026, according to the U.S. Bureau of Labor Statistics.

Menu prices have also continued to rise. According to the National Restaurant Association, menu prices rose 0.3% in July and were 3.4% higher than a year earlier.

Those elevated prices are creating a difficult environment for restaurants. Operators have to absorb increases in food, labor, rent, and other expenses while also trying to persuade customers that dining out is worth the higher price.

Restaurant traffic has also remained under pressure. Circana data showed restaurant traffic declined 0.3% in 2025 compared to the previous year, although traffic increased 0.5% during the fourth quarter.

That combination of higher operating costs and uneven consumer demand has made individual restaurant locations increasingly important to the industry’s financial performance.

For chains with multiple restaurants, locations that generate insufficient sales relative to their operating costs can become difficult to justify even when the broader brand remains viable.

Sarabeth’s decision to close two restaurants illustrates that dynamic. The company did not announce that it was shutting down entirely; instead, it is reducing its New York footprint while continuing to operate two of its longtime locations.

Rival breakfast chains close restaurants

Sarabeth’s is not the only breakfast-focused restaurant brand to pull back its footprint as operators navigate a challenging environment.

Several other breakfast and brunch chains have closed restaurants in recent months, underscoring the pressure facing operators across the segment.

Here’s some of my previous coverage of restaurant closures:

Maple Street Biscuit Company: Cracker Barrel sold the brand in July 2026, along with 35 restaurants, and closed its remaining 16 locations after previously shuttering more than a dozen.

Breakfast Republic: Closed three San Diego restaurants in August 2026.

Denny’s: Closed between 70 and 90 restaurants in 2025.

The shutdowns don’t necessarily signal that consumers have stopped eating out. Instead, they highlight how restaurant companies are increasingly evaluating individual locations based on their economics as costs rise and customer traffic remains uneven.

For Sarabeth’s, that means continuing with a smaller New York City footprint while preserving two locations that remain part of its long-running restaurant operation.

Related: Popular breakfast chain closes more restaurants

Cisco Systems gave investors plenty to like in its fiscal fourth-quarter report, but the market’s reaction showed how high expectations have become for one of 2026’s strongest AI infrastructure trades.

Cisco Systems (CSCO) reported record quarterly revenue and stronger-than-expected earnings, while management laid out another year of double-digit growth. The stock still fell sharply after the report and closed Aug. 18 at $112.90, leaving shares well below the $123.88 price used in Bank of America’s latest research note.

BofA analyst Tal Liani sees an opportunity in that disconnect. Liani reiterated a Buy rating and $150 price target on Cisco in a note given to TheStreet, arguing that broad networking demand and growing AI revenue could leave room for further upside.

The target now implies roughly 33% upside from Cisco’s Aug. 18 closing price.

Cisco’s networking business is gaining momentum

Cisco reported fiscal fourth-quarter revenue of $17.3 billion, up 18% from a year ago, while adjusted earnings reached $1.22 per share. Networking revenue jumped 28% to $9.8 billion as customers continued spending on data center and AI infrastructure.

Orders were even stronger. Total product orders rose 35%, while networking orders climbed 40%. Cisco said product orders still increased 25% when hyperscaler customers were excluded, giving investors another sign that demand is spreading beyond the largest cloud companies.

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Management expects that strength to carry into the new fiscal year. Cisco guided for first-quarter revenue of $18 billion to $18.2 billion and adjusted earnings of $1.32 to $1.34 per share, while full-year revenue is expected to rise to as much as $73.4 billion.

That broader strength sits at the center of BofA’s bullish view.

Liani noted that ex-hyperscaler order growth accelerated from 19% in the fiscal third quarter to 25% in the fourth quarter. Orders from Cisco’s four largest hyperscalers grew more than 100%, according to the note.

BofA believes those trends support Cisco’s decision to double its core growth outlook for fiscal 2027 to 10% from an earlier 5% estimate.

Cisco Systems reported record quarterly revenue and stronger-than-expected earnings, while management laid out another year of double-digit growth.SOPA Images via Getty Images

BofA thinks Cisco’s AI target could be conservative

Cisco booked $4 billion of AI infrastructure orders from hyperscalers during the fourth quarter, lifting full-year orders to $9.3 billion. The company generated about $4 billion in AI infrastructure revenue during fiscal 2026 and expects that figure to reach $7.5 billion in fiscal 2027.

BofA thinks that target could leave room for upside.

Related: Elon Musk’s $900 billion SpaceX stake is built on more than rockets

The bank expects stronger networking revenue as recent orders convert into sales and sees fiscal 2027 AI orders materially exceeding the $9 billion level reached this year. Improving momentum in Cisco’s security business could provide another boost as the company moves beyond pricing changes associated with Splunk.

BofA raised its fiscal 2027 adjusted earnings estimate to $5.08 from $4.77 and lifted its fiscal 2028 estimate to $5.47 from $5.21.

Cisco itself expects fiscal 2027 revenue of $72.2 billion to $73.4 billion, with adjusted earnings between $5.05 and $5.11 per share.

Cisco still has a margin problem to watch

The bullish demand outlook comes with a trade-off. Cisco’s adjusted gross margin slipped to 66.3% in the fourth quarter from 68.4% a year earlier as faster hardware growth changed the company’s sales mix.

BofA expects gross margin to fall to roughly 64.5% in fiscal 2027, about 150 basis points below the Street’s outlook. Strong hardware sales and a greater cloud mix could keep pressure on profitability, even as revenue accelerates.

Valuation also leaves less room for mistakes. Liani estimates Cisco trades near 25 times calendar 2027 enterprise value to free cash flow, well above its five-year average of roughly 16 times.

BofA still believes the demand cycle can outweigh those concerns. With orders accelerating inside and outside the hyperscaler market, Cisco may have more growth ahead than its fiscal 2027 targets currently suggest.

Related: Cisco stock flashes rare technical signal 

Rob Strechay, until recently managing director and principal analyst at theCUBE Research, has joined VentureBeat as our first Lead Analyst and a founding analyst of VentureBeat Research. His arrival is the next step in a deliberate move at VentureBeat toward deeper specialization: analysis built for the technical decision-makers — the directors, VPs, CIOs, and CTOs — who are evaluating, buying, and deploying enterprise AI.The enterprise AI stack is being rewritten in real time, and the decision-makers I talk with are starved for objective, defendable data. Rob Strechay has the mix of technical rigor and operating experience needed to dissect the architecture behind the next phase of enterprise AI deployment.The questions enterprise technology leaders are asking have changed. As organizations move past experimentation with generative AI toward production deployment, they want to know how to orchestrate multi-vendor environments, where the security gaps in their agentic pipelines sit, and how to fix the utilization problems draining their infrastructure budgets. Answering those questions requires more depth than news coverage alone provides, and that is the gap this research offering is built to fill.An analyst who has sat on every side of the tableStrechay brings nearly three decades of experience as a practitioner, product executive, and industry analyst. Before becoming an analyst, he was an executive at numerous startups, including Zerto; he joined Amazon Web Services to help build a new analytics service; and he held executive roles across enterprise infrastructure. He later served as a senior analyst at Enterprise Strategy Group and most recently as managing director and principal analyst at theCUBE Research and SiliconANGLE, where he hosted executive interviews and analyzed the evolution of cloud, data, and AI infrastructure.Strechay will initially focus his coverage on cloud infrastructure, advanced data infrastructure, platform engineering and DevOps orchestration and observability, and the intersection points where AI and enterprise security collide.Already at work: GPU utilization and the VB Pulse surveysStrechay has already been contributing to VentureBeat’s research. In May he published an analysis of enterprise GPU utilization, examining the compute waste sitting inside enterprise AI infrastructure, and he provided a substantive review of our AI Infrastructure & Compute survey before it went into the field.His infrastructure-level focus complements the research engine VentureBeat has built around its monthly VB Pulse surveys, which track five areas of enterprise AI adoption: agentic orchestration, agent reliability and evals, agentic security and identity, AI infrastructure and compute, and context layers, including retrieval-augmented generation (RAG). Our June report on agentic orchestration, drawn from a survey of 145 enterprises, found that two-thirds of those enterprises had hedged their AI model strategy rather than committing to a single provider — a posture whose value the June outage of Anthropic’s Claude models made plain.VB In Conversation: The first vehicleA core vehicle for this expanded research footprint will be a deepening of VentureBeat’s existing VB In Conversation video interview series, which Strechay will host. Rather than high-level industry overviews, the series will bring architectural blueprints, actual deployment barriers, and back-end infrastructure realities to light through in-depth technical interviews with the architects and product leaders behind leading enterprise AI systems — an unvarnished look at which tools perform under production-grade pressure.”VentureBeat has built an audience of enterprise builders and technology buyers that any analyst would want to serve,” Strechay said. “My goal is to use deep empirical metrics and VentureBeat’s proprietary tracking data to help enterprise buyers and the people building for them make sound platform and infrastructure decisions during the most disruptive transition enterprise technology has seen.”The expanded VB In Conversation series will appear on VentureBeat and on VentureBeat’s YouTube channel, alongside Rob’s written analysis on the site. Enterprise practitioners who want to take part in our monthly VB Pulse surveys, or arrange an analyst briefing with Rob, can reach the research team here.

The former morgue manager of the university’s medical school illegally took and sold body parts that had been donated for scientific research.