Agentic AI, automation lead 2026 trends in data economy :: WRAL.com
Every new year
comes with its own sense of excitement and energy. It’s the season of
resolutions and new beginnings. For companies, that usually means fresh
budgets, new initiatives and renewed optimism about what technology might
unlock next.
In the tech world,
the calendar always opens with the Consumer Electronics Show, which is the
granddaddy of all tech conferences where marketing teams spend millions hyping
the latest innovations promised to change the world. Some of those predictions
eventually prove true. Many do not. Metaverse, anyone?
As we begin 2026, I
thought it would be useful to step back from the hype and call out a handful of
technology, business and policy trends that I’m actively watching this year.
Not because they sound futuristic, but because they’re already starting to show
up in the real economy.
I’d love to hear
what you’re seeing as well, so consider this an open conversation.
Agentic AI moves from experiment to execution
For the past two
years, artificial intelligence has been framed as a tool that assists humans.
Copilots, chatbots and recommendation engines were designed to support
decision-making. A recent study I saw estimates that 95% of all generative AI
usage has been for improved individual productivity, versus creating new
business value or solving new business problems.
That framing will
begin to break in 2026. This is the year agentic AI moves from demonstration to
deployment. Systems will move beyond merely suggesting what should happen next
and begin executing actions themselves across workflows, data sources and organizational
boundaries. Not in flashy consumer apps, but deep inside operations and
especially in small and mid-sized companies that can move fast and try things.
>> What does ‘agentic’ AI mean? Tech’s newest buzzword is a mix of marketing fluff and real promise
Scheduling,
procurement, logistics, quality control and compliance checks are examples of
entire categories of middle-layer decision-making that are poised to disappear
into software. Not because executives declared it so, but because the economics
finally make sense and the AI-enhanced tools are mature to the point that
agentic algorithms can be system integrated in practical ways.
This is what
real-time automation looks like. And once organizations trust machines to act
on live data, there’s no going back.
>> When machines talk back: The rise of agentic AI in a world of physical devices
>> Tom Snyder: The problem and solution to agentic AI threat starts with people
[Aside – what
remains to be seen, which is a way-too-early 2027 question is whether automated
AI driven efficiency on the front end will unwittingly create massive future
technical debt that will be unsustainable. I’d suggest companies continue to
keep humans in the loop for system integration and maintenance. I’ll write more
on this in a future piece].
Operational data becomes the most valuable asset on the balance sheet
For years, we’ve
talked about data as “the new oil.” That metaphor never exactly fit. Oil gets
drilled, shipped, refined, piped, trucked and stored before finally being
burned. There is massive latency in the value chain. Operational data only has
value when it’s acted upon immediately.
In 2026, this
distinction becomes impossible to ignore. The most valuable data isn’t
historical, aggregated or cleaned up for dashboards. Pretty much all the
legacy data that has ever been digitized has already been trained into the
largest of the LLMs. The new world order is about live sensor data,
transactional data, machine data and behavioral signals that feed automated
systems in real time. It won’t matter so much what happened last year or even
yesterday. Automation thrives on what’s happening right now.
The real economic
battles shift accordingly. The fight will shift from who owns data and instead
to who has the right to execute against it. Who can trigger actions? Who
can automate decisions? Who bears responsibility when systems act autonomously?
These questions
will start showing up in contracts, partnerships and valuations long before
they appear in policy debates. And I would not be surprised to see real-time
data access begin to show up directly on the accounting balance sheet,
similarly to the estimated value of patents, trademarks and other IP.
Infrastructure is recast as economic enablement
For decades,
infrastructure was treated as a background concern. It was important, but
rarely thought of as strategic. But infrastructure for the Data Economy is
different, and we will see awareness bubble up this year.
In 2026,
infrastructure is no longer just roads and bridges. It’s power stability, water
reliability, transportation flow, broadband access, edge compute, IoT sensor
networks and data pipelines all functioning as a single economic system.
When electricity
falters, data centers fail. When sensors miss a reading, automation algorithms
lose resolution. When networks lag, AI systems stall. Infrastructure failures
now cascade directly into productivity losses.
>> Tom Snyder: AI power needs, available solar could transform energy landscape in southwest US
Regions that
understand this should stop competing on tax incentives and start competing on
reliability. The winners won’t be the flashiest ecosystems, but the ones that
quietly work, every minute of every day. Cities that invested in broadband
survived the transition from a labor economy to a knowledge economy while those
that didn’t fell into economic distress. Places that invest in data
infrastructure are going to separate from those who do not.
The automation divide replaces the digital divide
We’ve spent years
talking about the digital divide – who has access to technology and who
doesn’t. In 2026, a more consequential divide emerges.
It separates
organizations and regions that can automate decision loops from those that
can’t.
Small teams with
the right data, tools and workflows suddenly outperform much larger
incumbents. Legacy enterprises, buried under compliance, integration debt,
tooling debt and fragmented systems, struggle to move at all.
This divide isn’t
about job loss in the abstract. It’s about leverage. Firms and regions that can
deploy agentic systems gain disproportionate economic power through faster
cycles, lower costs, and better outcomes with fewer people.
I predict that
workforce and economic policy, meanwhile, will remain largely focused on
reskilling for yesterday’s roles, not preparing workers and institutions to
supervise, audit and collaborate with autonomous systems.
Innovation re-localizes
For years,
innovation narratives revolved around global platforms, coastal hubs and
venture-backed scale. I sense that this is changing. We already are seeing a ton of
entrepreneurial activity and success in non-traditional places (i.e. not NY,
SF, Boston, Seattle or LA). That was a 2025 trend that continues to grow.
A decade ago,
entrepreneurship wasn’t a household discussion, while today every college and
university and most high schools have invested into programs that inspire
students to consider creating a job, rather than simply finding one.
We need to look no
further than our own back yard to see that applied research commercialization
is accelerating outside traditional R1 universities. NCInnovation is building
the national model for locally sourced innovation right here in NC. Regionally anchored
accelerators regularly outperform global brands by staying close to customers,
markets and real operational problems. The national models that try to
force-fit every company to look the same, with a goal of scalable venture
investing (e.g. TechStars) are facing headwinds and are restructuring.
In 2026, we should
see economic development becoming less about hype and more about execution. We
are already seeing increased state-level funding of startups and entrepreneur
support organizations in our bordering states, and NC needs to join the trend or
risk falling behind.
The Data Economy
rewards proximity to data sources, infrastructure, and operational reality.
Innovation embeds itself into place. The most interesting breakthroughs
won’t always come from the biggest names, but from ecosystems that know how to
turn ideas into working systems.
Standards move faster than regulation
Despite years of
headlines, 2026 is not the year regulators suddenly “solve” AI, data
governance or automation. Instead, markets will continue to define how
emerging technology is adopted and applied. But we’re hitting an inflection
point that leads to increased standardization.
Procurement rules,
industry standards, insurance requirements and contractual norms typically
shape behavior faster than legislation ever could, and AI will be no different.
De facto standards emerge not because governments mandated them, but because organizations
needed common rules to operate at speed.
This doesn’t mean
regulation is irrelevant. It means that by the time formal frameworks arrive,
much of the economic behavior has already been set. In the Data Economy,
function beats regulatory uncertainty.
Data infrastructure becomes economic infrastructure
All of these forces
point to a larger historical pattern. For much of the 20th century, local and
regional economies were built around labor, especially manufacturing. When
globalization accelerated and labor-intensive industries moved offshore, many
cities failed to adapt. They were left behind in what we now call the Rust
Belt.
Other regions made
a different choice. They invested in broadband, education, and digital
connectivity. Digitally connected regions successfully transitioned from a
labor economy to a knowledge economy. High-wage industries followed. Disposable
income circulated locally. Arts, entertainment, restaurants, retail, and
quality-of-life industries flourished alongside them, because the knowledge
economy created the high wage jobs that create disposable income that gets
reinvested locally.
Trickle-down
economics has never delivered. Making rich people richer doesn’t really benefit
the place that they live. But what I’ll describe as trickle-around
economics, where widespread high-paying jobs circulate money locally
rather than concentrating it at the top, does work. An abundance of high-paying
middle class jobs spread money throughout local economies. Money that
“trickles-around” results in economic balance, quality of life and great places
to live.
High-wage
industries mattered during the globalization transition. They still do now.
We’re standing at another inflection point. How do we secure the middle-class
jobs of the Data Economy?
Many of the
traditional knowledge economy jobs that powered regional growth over the past
30 years are being replaced or dramatically augmented by AI. The question for
cities, counties, and states is not whether this shift happens, but whether
they are positioned for what comes next.
A new wave of value
is forming in the Data Economy. It depends on real-time data, automation,
resilient infrastructure, and systems that can operate continuously and
reliably. Places that invest in this new data infrastructure – compute,
connectivity, power, water, and execution capability – have a chance to lead.
Places that rely
solely on yesterday’s infrastructure, in other words the distributed internet
alone, risk falling into a new kind of Digital Rust Belt. And communities that
still lack broadband don’t stand a chance at economic health.
This is what
economic transitions look like in real time. They don’t arrive with
announcements. They show up as dependencies that are quiet at first, then
unavoidable.
History reminds us
that we only recognize new economic eras after they’ve already reshaped how we
live and work. The railroads. Electricity. Accounting standards. Each rewired
the economy long before the language caught up.
By that measure,
2026 won’t be remembered as a year of bold predictions. It will be remembered
as the year the Data Economy stopped being theoretical and started being
operational.
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