Task by Task: The Workflows We're Handing to AI — One Decision at a Time | Lens Four by Sean Martin | Read by TAPE9

RSA C2026 conference, where insights drive action, solutions emerge, and community connections

spark innovation.

Be there March 23rd through March 26th, secure your spot at rsaconference.com forward slash

ITSPMAG26.

Task by Task.

The workflows were handing to AI one decision at a time by Sean Martin, lens4 at SeanMartin.com.

I look at the intersection of business, technology, and messaging regularly through three lenses.

How organizations operate and run their programs.

How innovation and market forces are reshaping what's possible and how the language and narrative

around technology shapes what gets funded, prioritized, and trusted.

This week, all three lenses are pointing at the same thing, and the picture is clearer

than most people are comfortable admitting.

Nobody decided to remove the human from the workflow.

That's the part worth sitting with.

In boardrooms, in budget reviews, in vendor evaluations, nobody stood up and said, let's

build a business process with no humans in the loop.

What happened instead was a series of smaller decisions, each of them reasonable, each of

them local, each of them defensible.

An HR team bought a screening tool to handle application volume, a legal department licensed

an AI drafting platform to reduce outside counsel spend.

A finance team deployed automated invoice processing to close faster.

None of those decisions on their own looks like giving up control, but map them together

task by task across a single workflow and something significant emerges.

The human is already optional across most of the process.

That's what I want to examine this week.

Not whether AI should take on more of the work.

That debate is largely settled in the data.

But whether organizations have consciously mapped what they've actually handed over and

what that means for how businesses operate, compete, and carry accountability going forward.

Let's start with the business operations lens.

Are we delegating efficiently or giving up control?

The honest answer is both.

And most organizations can't tell the difference yet.

Let me trace two workflows that most businesses run every week, not as edge cases, not as experiments,

but as normal operating processes with deployed tools and real outcomes.

The first is the hiring workflow, a job requisition opens.

What happens next used to require a recruiters judgment at every step?

Here's what the same process looks like with current tools.

The first task is resume screening.

Unilever reported saving over 1 million pounds annually after deploying AI screening tools

across its hiring pipeline.

McDonald's rolled out paradoxes conversational AI called Olivia across thousands of locations

to handle applicant screening and scheduling.

This move from application to interview without a human recruiter touching the file.

AI tools now rank and filter applicants in seconds against criteria, a human set once

and that now runs autonomously at scale.

The second task is interview scheduling, a large US financial services firm using good

time, reduced time to fill by weeks by automating calendar coordination alone.

The moment a candidate cleared screening, an interview invite went out within hours.

No recruiter coordination required.

The third task is first round interviewing and assessment.

Higher view used by JP Morgan, Goldman Sachs, Amazon, Microsoft, Emirates Airlines,

and hundreds of others, conducts asynchronous first round interviews with no human present.

The candidate records answers to preset questions on their own schedule.

The AI analyzes speech, language, and behavioral indicators and returns a ranked score.

No recruiter watches the recording until after the AI has already filtered and ranked the

pool.

Emirates Airlines reduced its hiring cycle from 60 days to 7 using this approach.

The human interviewer enters at round 2, but by then, the AI has already determined

who gets that meeting.

The fourth task is offer generation and outreach.

Networking platforms including Lindy and recruiter flows agent mode draft, personalize, and

send offer communications and follow-up sequences autonomously.

The offer letter is written before a recruiter opens their inbox.

The fifth task is onboarding initiation.

End-to-end workflow automation, deployable today through platforms like N8N, covers the

full pipeline from CV submission through assessment, scheduling, and status tracking,

without human intervention at any step.

Five tasks.

Five separate vendor decisions.

Each one made independently, each one with its own ROI story.

And together, a process where a candidate can move from application to offer without

a single human making an active decision along the way.

Now let's run the same analysis across a legal department's standard contracting process.

The first task is matter intake and triage.

Quickbox AI handles incoming legal requests through intelligent chatbots that capture context,

ask clarifying questions, and route matters to the right team automatically.

No paralegal spending the morning clearing an email queue.

The second task is legal research.

Harvey AI, now embedded in AMLaw, 100 firms, surfaces relevant case law, statutes, and

precedent across large document sets in minutes.

Alexis Plus AI provides contextual legal reasoning on demand.

What used to be a junior associate's full day is now a prompt.

The third task is contract drafting.

Spellbook drafts contracts inside Microsoft Word.

Legal on users report NDA reviews dropping from two hours to 30 minutes.

One managing partner reported a 40% increase in billing capacity, not from doing better

work, but because AI wrote the first draft on every matter.

The fourth task is contract review and redlining.

Luminance identifies anomalies and flags deviations from playbooks across thousands of contracts

simultaneously.

Kira extracts specific clauses at scale.

Across the category, AI contract review tools are reducing review time by 75 to 85%.

A task that defined legal practice for decades is compressing toward minutes.

The fifth task is approval routing and post execution management.

Contract Pade handles routing, obligation tracking, and compliance monitoring after signature.

Ironclad manages the full contract life cycle, renewals, explorations, obligation triggers

without a paralegal maintaining a spreadsheet.

Again, five tasks, five products, five separate procurement decisions, and end-to-end, a contracting

workflow where a contract can move from request to executed agreement without a lawyer

authoring a single original clause.

And this pattern runs across the business, not just in these two functions.

Finance has it.

AI invoice processing platforms, handle capture, validation, approval routing, and payment

scheduling end-to-end, with one hospital association reporting a reduction in batch processing

time from 10 hours to minutes.

Server service has it.

Gartner projects that agentic AI will resolve 80% of common customer service issues without

human intervention by 2029, up from effectively zero in 2024.

Security operations has it.

And I've had direct conversations with the vendors building these tools.

Edward Wu, founder of Dropzone AI, told me ahead of Black Hat USA 2025.

He wants to be a tier one analyst forever.

Subo Guha of Stellar Cyber described a digital army of AI agents that filter 70 to 80%

of alerts before a human analyst sees them.

The pattern looks the same whether the workflow is closing a contract or closing a security

incident.

The business question this raises isn't whether the tools work, most of them do.

The question is whether organizations have a clear, deliberate answer to which tasks require

a human decision and why, because right now many organizations are answering that question

by default, one purchase at a time rather than by design.

Gartner puts a number on the trajectory, at least 15% of day-to-day work decisions will

be made autonomously through agentic AI by 2028, up from essentially zero in 2024.

That isn't a distant forecast, it's a projection from a baseline that is already moving inside

most mid to large enterprises today.

Now the innovation and market shifts lens.

What is the market building and how fast is it moving?

The market knows exactly what it's building, it's not naming it directly, but the architecture

is unmistakable.

Gartner predicts that 40% of enterprise applications will include integrated task-specific AI agents

by the end of 2026, up from less than 5% today.

Agent AI assistants that help people do their jobs, agents that do the job, within defined

parameters without waiting for a human to initiate each step.

By 2035, Gartner's best case scenario has a gentic AI driving approximately $450 billion

in enterprise software revenue, roughly 30% of the entire market.

Notice what the market is selling, task-specific, not workflow-replacing, not role-eliminating,

task-specific.

One task at a time, each one rationalized locally, each one with a discrete budget line

and an ROI model.

The cumulative effect, a workflow that no longer requires human participation, isn't what's

being sold, it's what's being assembled.

This is where the business opportunity gets genuinely interesting, and where the strategic

gap between leading and lagging organizations is opening up.

The companies deploying these tools aggressively are not doing so because they ran an experiment.

They're doing it because the economics are compelling in ways that compound over time.

Recruiter flow data shows recruiters saving six or more hours per week, a 33% productivity

increase per person.

Legal on-users report reducing outside counsel dependency by thousands of dollars per contract

cycle.

Individually, those numbers are meaningful.

Across a workforce, across a fiscal year, they represent a structural cost advantage

that competitors without these tools cannot match.

But the more consequential shift isn't cost reduction.

It's speed and scale.

A hiring process that moves at machine speed doesn't just save money.

It changes who gets the best candidates.

A legal team that can review, red line, and execute contracts in minutes rather than

days doesn't just reduce billable hours.

It changes how fast the business can move on deals.

The organizations that figured this out early are already operating at a different tempo

than those still treating AI as a pilot program.

Forester projects that distributed AI workflows will capture 45% of enterprise workload capacity

by 2026.

It's not adoption at the margin.

That's a structural shift in how work gets done.

The complication, and it's a real one, is that the vendor market is significantly ahead

of organizational readiness for what these tools actually do.

Gartner estimates that only around 130 of the thousands of companies now claiming to

offer agenteic AI are delivering genuine agenteic capability.

The rest are rebranding existing automation and RPA under a new label.

A genuine agenteic system reasons across tasks, adjusts based on outcomes, and handles

exceptions without a human rewriting the playbook.

A rebranded chatbot executes a fixed sequence and breaks at the edge case.

Buying the latter under the belief it's the former is how organizations end up with

expensive tools that create new workflow fragility instead of removing old bottlenecks.

The cybersecurity sector is already several steps ahead of most enterprise functions on

this curve, and the outcomes are real, not experimental.

As I wrote in the first lens 4 article, the 72 minute gap, organizations deploying agenteic

SOC automation are realizing documented, measurable budget savings.

Dropzone AI's Edward Wu described it plainly when we spoke at Black Hat USA 2025.

At roughly $36,000 per year, their platform ran 4,000 automated alert investigations.

A number that simply cannot be staffed at comparable cost.

Subo Guha of Stellar Cyber in two separate conversations with me at RSAC 2025 and Black

Hat 2025 described their digital army of AI agents filtering 70 to 80% of incoming alerts,

allowing analysts to focus on the fraction that require human judgment.

Both companies are emphatic that the value isn't hypothetical.

The savings are already in the operating budget.

The market is also generating the next layer of infrastructure, which is itself a leading

indicator of how far adoption has already gone.

When AI agent identity governance becomes a funded product category, and it has, it

means organizations have already deployed enough autonomous agents into production that

they've discovered they can't see what those agents are doing, or control what systems

they can reach.

Token security, named a finalist in the RSAC 2026 Innovation Sandbox, was built entirely

around this problem, governing AI agent identities with the same rigor applied to human users.

Continuous discovery, intent to wear access controls, life cycle management from deployment

through decommissioning.

Moderna has already scaled from 750 to more than 3,000 internal AI agents in a single year.

The governance market doesn't emerge until the adoption that requires governance is already

underway.

That tells you where the actual baseline is.

Here is the structural shift worth watching closely.

Right now, organizations are assembling workflows tasked by task through separate vendor decisions.

The next phase of the market eliminates that friction entirely, and the infrastructure

for it is already being built.

What's emerging is the agentic orchestration platform.

A single governed environment where workflows can be defined in plain language, purpose-built

agents can be selected, configured, guardrailed, and monitored, and the cumulative workflow

is visible as a designed hole rather than discovered after the fact as an accumulated

pile of vendor contracts.

In-text, which serves more than 7,000 organizations across 100 countries, announced its agentic

business orchestration platform in September 2025, explicitly positioning it as a single

governed layer, unifying legacy systems, manual processes, and AI agents.

Their incoming agent designer feature enables IT leaders and business technologists to build,

evaluate, and orchestrate specialized agents in a low-code environment without writing code

and without leaving the governance framework.

IDC's Maureen Fleming framed it directly.

Agentic business orchestration represents a shift toward coordinating people, systems,

and AI agents in governed ways that ensure automation and AI deliver measurable results

at scale.

Microsoft is moving in the same direction at enterprise scale.

Co-Pilot Studio, already connected to more than 1,400 systems, allows agents to be built

in natural language, configured, monitored, and governed from a single interface.

Every agent now gets a Microsoft Entra agent, ID, an identity credential that enables governance

across the fleet.

Microsoft's own framing for 2026 is pointed.

The transition is from AI that helps people do work faster to AI that handles work on

behalf of the organization, with humans escalating into exceptions rather than executing by default.

The pattern is the same whether you're watching Nintex, Microsoft, Salesforce, Agent Force,

or ServiceNow's Agentic Capabilities.

The market is converging on a platform model where the workflow is defined up front

and plain language.

Agents are scoped to specific tasks with explicit permissions.

Guard rails are set before deployment rather than bolted on after.

Human oversight points are designed in rather than assumed, and the full workflow is audible

and measurable as a system.

The organizations that get ahead of this transition will enter it with clear workflow maps and

define accountability structures.

The ones that don't will find themselves importing their accumulated default choices into

the new architecture and inheriting all the governance gaps that came with them.

Now the language and messaging lens.

Why does everyone say augment when the direction is replaced?

Because augment gets funded, replace gets scrutinized, and the actual outcome is somewhere

neither word honestly describes.

There is a phrase that appears in virtually every vendor pitch, analyst briefing, and

enterprise communication about AI and automation.

We augment human capabilities.

We don't replace them.

It surfaces in hiring tech.

It surfaces in legal AI.

It surfaces in financial automation and customer service platforms.

It is, at this point, essentially obligatory in the category.

The phrase is doing real work.

It manages three audiences simultaneously.

Employees watching their job functions shift, procurement committees answering to boards

who want to see AI investment justified, and regulators watching closely how AI is being

deployed in consequential decisions.

Augment, not replace, threads all three needles cleanly.

It implies human oversight remains intact.

Accountability structures are unchanged, and the organization is being measured and responsible.

But walk the data back against that framing, and it doesn't hold up.

Swimlane projects AI will resolve or escalate over 90% of Tier 1 security alerts by 2026.

Not assist with them.

Resolve them.

Gardner projects autonomous AI handling 80% of customer service issues without human involvement

by 2029.

The legal contract review tools marketing 75% to 85% time reduction aren't augmenting

lawyers.

They're doing the task and asking the lawyer to review the output.

The hiring platforms aren't helping recruiters screen faster.

They're screening and asking the recruiter to validate the ranking.

When the AI handles 80% of the task and the human handles exceptions after the fact, that's

not augmentation in any meaningful operational sense.

That's oversight of an autonomous system.

The distinction has direct implications for where accountability lives, what skills the

organization needs to maintain, and what happens when the output is wrong.

I explored a version of this tension from an unexpected angle on the Music Evolves Podcast

in a conversation with Chandler Lawn, AI Innovation and Law Fellow at the University of Texas

School of Law, Drew Thurlow, adjunct professor at Berkeley College of Music, and Pooja

Parto-Navid, partner at Sayfarth Shaw.

We were talking about AI-generated music and who owns the output, but the underlying

question was the same one running through every enterprise workflow.

When the system produces the thing that used to require a human, what does the human's

role actually become?

The music industry is a few years ahead of most enterprise functions on this curve.

Universal Music Group and Warner Music Group both reached landmark settlements with AI

music platforms in late 2025.

The answers they're landing on involve drawing explicit lines around what requires human

creative judgment and what can be systematically produced.

Enterprise operations will need to draw the same kinds of lines, probably with less drama,

but with the same underlying logic.

The language gap has a practical consequence beyond messaging.

When leadership describes every AI deployment as augmentation, it becomes difficult to

have honest internal conversations about what the organization has actually delegated,

where the accountability gaps are, and what happens when a consequential decision turns

out to be wrong.

That conversation is easier to have before the workflow is fully assembled than after.

Gartner's prediction that over 40% of agentic AI projects will be canceled by end of

2027 is worth reading through this lens.

It's not because the technology fails.

It's because organizations bought capability without building the governance, accountability

structures, and organizational clarity to run it responsibly.

The language that got the tool funded made those harder conversations easier to avoid

at purchase time.

They don't stay avoided.

At Black Hat USA 2025, Marco Chipelli and I talked after walking the floor about exactly

this.

When every vendor claims the same positioning, the actual distinctions disappear from the

buyer's view.

In our post-show episode, we called it the marketing milkshake problem.

Every vendor's message going into the same promotional blender and coming out tasting

the same, regardless of what the underlying technology actually does.

The agent-washing problem isn't just a market integrity issue.

It's a decision-quality issue for every organization trying to figure out what they're actually

acquiring and what decision authority they're actually transferring.

And now, the fourth lens.

When did you decide to hand over control and who was in the room when you did?

Here is what I keep coming back to when I look at all three lenses together.

And it's the thing I find myself saying in conversations that rarely makes it into polished

conference presentations.

We are already past the point of no return.

The human optional workflow is not the exception being cautiously piloted.

It is the operational default for hiring, contracting, finance, customer service, and security

operations in organizations that made five individually rational procurement decisions

and never looked at what those decisions assembled.

That's not naivety.

I want to be clear about that.

The organizations deploying these tools are not confused about what they're buying.

What they haven't done and what the vendors selling to them have never required them to do

is map the cumulative shape of those decisions before committing to them.

And I don't think that's an accident.

When I look at the language the vendor market has built around this transition, augment,

not replace, human in the loop, AI assisted, I don't read it as imprecise.

I read it as precise in exactly the right direction.

These are companies staffed with product managers, lawyers, and communications teams who understand

exactly what their tools do when deployed at scale.

And not replace, threads every needle it needs to thread, employee relations, procurement

approval, regulatory scrutiny, board optics.

It's not a description, it's a strategy, and it has worked because organizations bought

the framing along with the capability and now have workflows they couldn't honestly

describe as augmented with a straight face.

So where does accountability land when an AI assembled workflow produces a bad outcome?

Right now?

That is not hyperbole.

The procurement signer approved a task specific tool with its own contained ROI case.

The vendor sold a product that performs as specified.

The workflow that those tools assembled collectively, that's in a gap between contracts between

org chart lines, between the legal definitions anyone drafted when they wrote the terms of service.

Nobody owns the workflow, everybody owns a task.

That should be alarming.

Not because bad outcomes are inevitable, but because the accountability structure that

would catch and correct a bad outcome before it becomes a crisis.

That structure doesn't exist yet in most organizations.

The efficiency gains are real and already in the budget.

The accountability architecture is still theoretical.

What I'm watching closely is whether the agentic orchestration platform changes this dynamic

or accelerates it.

My honest read, both, depending on the organization.

A small group of mature deliberate organizations, the ones who were already doing workflow mapping

before procurement, who already had security and legal at the design table, will use nintex,

co-pilot studio, and platforms like them to do exactly what those platforms were designed

to enable.

Define the workflow first, configure the agents inside it, set the guard rails before deployment,

and maintain a complete audit trail of what was delegated and why.

For those organizations, the platform genuinely forces the design conversation, because you

cannot configure guard rails without deciding what you're guarding.

For everyone else, the platform will make accumulation faster and cheaper.

The same five irrational decisions, each locally rational, collectively unexamined, will

just be easier to make in one place.

Here's the structural reality I think most organizations are not yet reckoning with.

The auditors haven't arrived yet.

The regulatory frameworks that will eventually require organizations to account for autonomous

workflow decisions, who authorize them under what criteria, with what human oversight, and

how exceptions are handled, those frameworks are being drafted right now.

GDPR took years to land on AI.

The EU AI Act is already in motion.

The US regulatory posture is slower but not absent.

The window between we accumulated this workflow through procurement, and we need to demonstrate

we designed it with intention is open, but it is not going to stay open.

The organizations that use that window to map what they've built, establish where accountability

sits, and make explicit decisions about what requires human judgment, not because the

AI can't do it, but because the organization has determined that accountability requires

a person will be positioned to operate without disruption when the framework's arrive.

The ones that don't will discover that the workflow they built by default is not the workflow

they would have chosen under scrutiny.

The vendors knew what they were building, the buyers in most cases, didn't ask the right

questions, the auditors haven't arrived yet.

That window is closing.

If this analysis is useful to you, the full article with all references, data points,

and links to every podcast conversation mentioned, is at seanmarton.com.

Search for lens4, or find me directly at seanmarton.com.

And if these are the kinds of conversations you want more of, the redefining cybersecurity

podcast is where I explore them in depth every week with the people building, buying, and

breaking these systems.

You can find it wherever you listen to podcasts, or at redefiningcibersecuritypodcast.com.

Thanks for listening.