Tamas Hevizi

The hard part is getting AI to change how a business runs.

• Hevizi wants the questions shifted to whether the process got faster, cost went down, revenue improved, risk decreased, customer or employee experience materially improved, and whether a metric the CEO or CFO already tracks moved.
• He calls this "Boring AI": not flashy, but producing real results.
• He also notes that experimentation is not cheap because tokenomics can be punishing, so the scorecard should move from "Did we deploy AI?" to "What got better?"

The post sets up the contrast directly, "AI pilots are everywhere.

• AI business outcomes?
• Not so much," then replaces the deployment question with a short list of business-result questions.

Read original

They describe TotalAgility as the engine behind knowledge discovery for AI acting on completely unstructured content, orchestrating AI agents and document intelligence.

• InvoiceAgility covers accounts payable capture and e-invoicing.
• RPA removes manual, multi-step tasks and, in their framing, prepares data for AI retrieval systems and vector databases.
• Power PDF is positioned as an affordable all-in-one document toolkit.

Hevizi describes RPA as key to preparing data for AI RAGs and vector databases, and says InvoiceAgility is processing over $3 trillion in customer invoices at scale.

Read original

In the generative AI era, they argue, the question is not only how to orchestrate documents but whether they can be trusted. This turns their broader automation message into a verification and risk argument for regulated industries.

A short field note from the Düsseldorf summit names the roundtable topic and the fraud risk.

Hevizi shifts the argument about document handling from capability—can it read the document?—to assurance: should the document be believed?

Read original

It explores why CEOs feel pressure to lead with AI, why employees see it as a career catalyst, and how workslop, hype cycles and vanishing entry-level jobs are reshaping workplaces.

• The episode argues that AI is changing not just what people do but why they do it, and closes with a look at leadership and the human role.
• Tamas Hevizi and their brother and co-host Arpad Hevizi are presented as joint participants in the conversation.

The chapter list runs from “The CEO Pressure Cooker” and “The Rise of Workslop” through “Backward Problem-Solving: The Hammer Looking for a Nail,” “The Expert Paradox,” “The Entry-Level Crisis” and “Redefining Leadership and the Human Role.” Cited research includes an MIT report, covered by Fortune, that 95 percent of generative AI pilots at companies are failing, and HBR pieces on leadership skills in the AI age and AI-generated workslop.

Read original

Tamas Hevizi cites independent analyst recognition across automation categories and a customer base that skews toward the largest regulated institutions.

• The companion post counts a footprint in 140-plus countries and 850-plus partners, names the U.S.
• Government as the largest customer, and attributes long customer relationships to consistent delivery over 40 years.
• The message: legacy reliability is the platform for AI-era claims.

The posts list 8 of the top 10 global banks, 7 of the top 10 global insurers, 40 percent of the Fortune 100, 30 percent of the Forbes Global 2000, 9 of the top 10 global logistics providers, and 25,000-plus customers.

Alongside Hevizi’s newer posts, the 40-year credentials support the argument for AI outcomes rather than serving as a separate nostalgia message.

Read original · Original 2

The discussion covers Saudi Vision 2030, an expected 15 percent contribution from AI to GDP, government support that drew major cloud and software vendors to announce regional projects, and local startup growth. It also explores the divide between the Global North and Global South in AI and how government-led initiatives reshape the global technology landscape.

The episode cites over 215,000 attendees and $13.4 billion in investments at LEAP 2024, and points to Aramco’s METABRAIN model as an example of homegrown work rather than imported technology.

Read original

One short post carries the central thesis — outcomes over demos — and gives readers the vocabulary that recurs in their longer work.

Hevizi opens with a blunt contrast: “AI pilots are everywhere.

• AI business outcomes?
• Not so much.”
• They argue the hard part is not getting AI to work in a demo, a sandbox or a narrow pilot, but getting it to change how the business actually runs.
• They list the questions they want answered instead: did the process get faster, did cost go down, did revenue improve, did risk decrease, did customer or employee experience materially improve, did a metric the CEO or CFO already cares about move.
• They call this “Boring AI” — not flashy, but producing real results — note that experimentation is not cheap and “tokenomics can be punishing,” and say the scorecard should shift from “Did we deploy AI?” to “What got better?”

For brands planning a campaign

A thought-leadership plan on AI business outcomes can borrow the “what got better?” scorecard: a series of posts or a webinar built around a metric-by-metric checklist, with failing pilots as the hook.

Useful for judging whether their reasoning suits a campaign that needs depth rather than short posts.

The episode description says Tamas and Arpad Hevizi explore the psychological and career forces behind AI adoption, from the boardroom to the front line.

• It says the discussion covers why CEOs feel pressure to lead with AI, why employees see AI as a career catalyst, and how “workslop,” hype cycles and vanishing entry-level jobs are reshaping the workplace.
• The chapter list runs from “The CEO Pressure Cooker” (1:25) and “The Rise of Workslop” (4:00) through “Backward Problem-Solving: The Hammer Looking for a Nail” (7:45), “The Expert Paradox” (12:20), “The Entry-Level Crisis” (16:10) and “Redefining Leadership and the Human Role” (25:30).
• Cited research includes McKinsey work on the CEO in the agentic age and on the agentic organization, two HBR pieces on leadership skills and on generative AI in learning and development, an HBR piece on AI-generated “workslop,” and Fortune’s coverage of an MIT report that 95 percent of generative AI pilots at companies are failing.

For brands planning a campaign

The chapter structure offers a ready-made module and clip map — adoption pressure, workslop, entry-level hiring, the expert paradox — for a sponsored episode or multi-part series. The cited research gives a partner a reading list to anchor the series.

A short field note from Düsseldorf.

• Hevizi says they moderated a roundtable at the Gartner CIO Summit on a specific AI challenge — making unstructured documents AI-ready — and that the discussion also covered a fast-growing risk: AI-created document fraud across insurance, banking and government.
• They close with a question for the generative AI era: not just how to orchestrate documents, but whether they can be trusted.

For brands planning a campaign

Points to event-based content as a natural fit — roundtables, summit moderation, CIO-audience sessions — with document trust and fraud as topics a vendor could own. The post also offers a template for short field reporting from an event.

Writing in the first person as part of Tungsten, Hevizi walks through four products.

• TotalAgility “powers end-to-end process acceleration with AI-driven document intelligence, automated tasks and seamless orchestration for AI agents,” and they call it the engine behind Knowledge Discovery for AI working on completely unstructured content.
• InvoiceAgility handles invoice capture and an e-invoicing network and, according to Hevizi, is “processing over $3 Trillions in customer invoices at scale.”
• RPA eliminates manual, repetitive, multi-step tasks across systems and data sources and, in their framing, is “key to preparing data for AI RAGs and vector databases.”
• Power PDF is an affordable all-in-one toolkit to create, convert, edit, share and e-sign PDFs.
• The theme is customers accelerating key business processes.

For brands planning a campaign

For a competing vendor, this is a direct view of the product claims to position against. For a partner, it shows where Hevizi’s content sits in the stack and which use cases a campaign can attach to: accounts payable, unstructured content, and document-heavy PDF workflows.

The implied buyer is a large enterprise operations or procurement function in a regulated, document-heavy industry, setting the register for everything else Hevizi publishes.

Hevizi lists the scale claims associated with the company: 8 of the top 10 global banks, 7 of the top 10 global insurers, 40 percent of the Fortune 100, 30 percent of the Forbes Global 2000, 9 of the top 10 global logistics providers, and a base of more than 25,000 customers, alongside recognition from IDC, Everest Group and Zinnov.

• A companion anniversary post adds a footprint in 140-plus countries, 850-plus partners, and names the U.S.
• Government as the largest customer; it attributes “tenured, blue-chip customer relationships” to consistent delivery over 40 years.

For brands planning a campaign

A campaign seeking independent market commentary should say so: much of Hevizi’s publishing is company milestone messaging. A campaign aimed at banks, insurers, logistics and the public sector can use these figures as audience proof.

The challenge is not getting an AI model to work in a demo or sandbox; it is improving how the business runs.

• The questions shift from whether the model worked to whether the process got faster, costs fell, revenue improved, risk decreased, or customer and employee experience materially improved.
• They call this “Boring AI”—not flashy, but producing real results—and note that AI experimentation is not cheap because tokenomics can be punishing.
• The scorecard should shift from “Did we deploy AI?” to “What got better?”

Tamas Hevizi frames the topic as a scorecard change: replace deployment questions with questions about business results.

For brands planning a campaign

A thought-leadership plan for an intelligent automation company can focus on pilot-to-production metrics, cost and risk reduction, and process outcomes CFOs and CEOs already track. The topic lends itself to webinars, LinkedIn posts, and executive roundtables on moving beyond AI demos.

Examples: Source 1

Tamas Hevizi describes these as some of the most powerful tools for helping customers accelerate key business processes.

For brands planning a campaign

Content can link each product category to a buyer pain point: AP automation, document intelligence, RPA as AI data preparation, and PDF productivity without recurring costs. This gives a campaign concrete, product-led topics instead of generic AI commentary.

Examples: Source 1

The discussion also covered AI-created document fraud in insurance, banking, and government. They argue that in the age of GenAI, the question is not only how to orchestrate documents but whether they can be trusted.

For brands planning a campaign

A campaign can address document trust, fraud detection, and unstructured-data readiness for AI. The themes suit insurance, banking, and government audiences, where verification and risk are central buying concerns.

Examples: Source 1

Tamas Hevizi cites analyst recognition from IDC, Everest Group, and Zinnov across automation categories, along with customer figures: 8 of the top 10 global banks, 7 of the top 10 global insurers, 40% of the Fortune 100, 30% of the Forbes Global 2000, and 9 of the top 10 global logistics providers.

• Another post reports 140+ countries, 25,000+ customers, 850+ partners, and the U.S.
• Government as the largest customer.
• The posts also list platform strengths: embedded AI, NLP, and ML; RPA and intelligent document processing; a CIO-durable architecture; and faster ROI with lower total cost of ownership.

For brands planning a campaign

Partnership content can use these proof points to establish enterprise credibility, especially in regulated industries. Source and date the claims rather than repeat them as slogans.

Examples: Source 1 · Source 2

Topics include CEO pressure to lead with AI, employees seeing AI as a career catalyst, “workslop,” hype cycles, vanishing entry-level jobs, backward problem-solving where a hammer looks for a nail, the expert paradox, and the psychology of hype and hesitation. The episode argues that AI is changing not just what people do but why they do it, and reconsiders leadership and the human role.

For brands planning a campaign

For a thought-leadership plan, this supports content on change management, workforce, and leadership around AI adoption. It is less product-specific and better suited to executive conversations about culture, expertise, and entry-level talent.

Examples: Source 1

Tamas Hevizi and Arpad Hevizi discuss Saudi Vision 2030, AI’s expected 15% contribution to GDP, government support for AWS, Cisco, Dell, IBM, and ServiceNow, local startup culture, Aramco’s METABRAIN LLM, and the divide between the Global North and Global South in AI. The episode asks how government-led initiatives reshape the global tech landscape.

For brands planning a campaign

A campaign can use this material to explore emerging-market AI investment, public-sector digital transformation, and global technology shifts. It suits audiences interested in regional growth beyond North America and Europe.

Examples: Source 1

The LinkedIn posts often open with a sharp contrast—“AI pilots are everywhere.

• AI business outcomes?
• Not so much”—while the podcast episodes organize discussion into timed segments.
• One post also records their moderation of a Gartner CIO Summit roundtable, adding conference participation to the mix.

Tamas Hevizi alternates between product-proof posts and concept-driven podcast episodes.

For brands planning a campaign

A content plan can repurpose one idea across short LinkedIn posts, longer podcast episodes, and live event moderation. Contrast-led openings offer a repeatable structure for hooks, while chapter markers make podcast topics easy to excerpt.

Examples: Source 1 · Source 2 · Source 3 · Source 4 · Source 5 · Source 6 · Source 7

The podcast episodes add AI adoption psychology and regional investment. Together, they show a shift from legacy automation credentials to AI-era positioning, with enterprise scale and reliability as the anchor.

For brands planning a campaign

A campaign can pair anniversary credibility with current themes around AI outcomes: use the legacy and customer proof as trust signals, then pivot to practical AI results and document trust. This presents AI as a continuation of the company’s operational history, not a break from it.

Examples: Source 1 · Source 2 · Source 3 · Source 4

Other posts say “our global footprint” and “Tungsten Automation has won trust,” list customer counts, and tag #tungstenautomation. This appears to be an employment relationship; the posts do not state a title, ownership stake, or start date.

View example

Gartner is identified as the event host; no sponsorship or client relationship is stated.

View example

Robbins' posts about India, Indonesia, and Europe use the same field-note format as Hevizi's event roundtables and customer-scale claims.

Robbins writes from a networking, connectivity, and corporate CEO vantage point, focusing on country visits, government meetings, and Cisco partnership announcements. Hevizi writes about automation and document-intensive processes, centering AI business-result metrics, document trust, and automation products.

Read original · Original 2

Anbarasu focuses on manufacturing and Industry 5.0, with Körber's hardware-software-AI ecosystem and European sovereignty as recurring reference points. Hevizi focuses on enterprise process automation and document intelligence, with regulated-industry trust and CFO or CEO metrics as recurring reference points.

Read original · Original 2

Liu focuses on growth strategy, sales focus, customer segmentation, and international market entry, especially the Middle East. Hevizi focuses on automation technology execution, document workflows, and AI adoption inside large enterprises.

Read original · Original 2

The posts cover customer, partner, and government meetings in India, Indonesia, and Europe; AI centers of excellence; security operations; and large-scale skills commitments. That suits campaigns focused on enterprise AI adoption across markets, telco and service-provider networks, or public-sector digital programs.

The tradeoff is distance from specific automation outcomes.

• The posts focus on corporate visits, partnerships, and strategic announcements, not document processing, invoice capture, RPA, or AI scorecards for process improvement.
• For a campaign on enterprise automation outcomes, ask for examples that connect this broad infrastructure and security narrative to process-level results, or pair it with a more process-focused voice.

Examples: Source 1 · Source 2

The posts cover a Handelsblatt industrial AI panel and Körber’s Industry 5.0 positioning: resilience, human centricity, sustainability, and the integration of hardware, software, digital, and AI. This is a stronger match when the buyer wants to discuss applying AI to efficiency, productivity, resilience, and plant or process decisions, especially in Europe.

The tradeoff is category and buyer focus.

• The material centers on industrial and manufacturing transformation, not document intelligence, accounts payable, invoice networks, PDF workflows, or enterprise-wide automation platforms.
• It also speaks for a company brand.
• If the campaign needs independent process-automation commentary, confirm employer approval or choose a different contributor.

Examples: Source 1 · Source 2

The second post extends the discussion to international expansion, especially in the Middle East, covering buyer trust, relationships, and local decision-making. This fits a thought-leadership plan on go-to-market discipline or Gulf market entry.

The tradeoff is that the posts focus on B2B growth and market entry, not AI outcomes or enterprise automation technology.

• They can help an automation company decide which accounts and regions to prioritize, but offer few examples of AI deployments, document workflows, or process metrics.
• For a campaign promising practical AI outcome measurement, ask for deployment examples and metrics, or use this as a supporting perspective rather than the main voice.

Examples: Source 1 · Source 2