2026 TRENDS REPORT: DATA & AI 2026 Trends Report: Data & AI 1

2026 TRENDS REPORT: DATA & AI Report Overview Introduction: The (Great)
Disconnect The widening gap between AI ambitions and operational reality
across industries Top Data & AI Trends for 2026 1. AI success in 2026
will be driven by data infrastructure, not new models Why modern data
infrastructure, not the latest AI model, delivers the highest ROI for
enterprises 2. Organizations are moving from broad experimentation to
specific, high-value use cases The shift from hype to focus: proven,
measurable applications replacing unfocused experimentation 3. AI is
evolving from proof-of-concept to enterprise-wide deployment AI moves
from testing and pilots to strategic, production-ready adoption across
industries 4. Companies are rethinking short-term, tech-first AI
strategies that fail to scale How organizations are learning from early
mistakes 5. Semantic modeling, conversational intelligence, and
governance are becoming critical differentiators The hidden enablers
defining scalability, trust, and responsible AI adoption 6. Enterprises
are prioritizing data lifecycle management, modernization, and human
capability Strategic priorities shaping the next 18 months of AI-driven
transformation 7. High-performing companies are aligning data, people,
and purpose to scale AI responsibly What successful organizations
understand about themselves, their data, and their people
Industry-Specific Trends for 2026 Sector forecasts for airlines, retail,
media, healthcare, and technology, and how AI is reshaping operations,
innovation, and talent How to Prepare for 2026 Key actions to build
readiness and avoid common pitfalls Conclusion: The Base Determines the
Future The three paths ahead and why strong foundations decide which one
you take 2

2026 TRENDS REPORT: DATA & AI

2026 Trends Report: Data & AI Expert insights from DataArt on where AI
actually works, the common failures holding companies back, and the
priorities that will define success over the next 18 months.

Introduction: The (Great) Disconnect

This report synthesizes findings from comprehensive interviews with
DataArt's senior data, AI, and technology leaders conducted in September
and October 2025. Throughout this report, you'll find direct insights
from these experts -- practitioners who architect data platforms, deploy
AI solutions, and guide enterprise transformation daily. Their
perspectives reflect real-world experience with both AI's genuine
capabilities and its most common failure points, cutting through the
hype to identify what's actually working in AI implementation and what's
not. A fundamental gap exists between what organizations expect from AI
and what it actually takes to deliver. Decision-makers pursue
transformational wins at minimal cost while neglecting the foundation
work that makes those wins possible. This divide manifests across
industries. Companies announce AI initiatives in press releases while
finance teams manually copy data between systems. Executives champion
data-driven decision-making, then override analytics when results
conflict with intuition. Technology departments operate as offshore
service providers rather than strategic partners, creating fragmented
understanding and slow interfaces that stifle innovation. The cultural
sector, for instance, reveals this mismatch quite vividly. Organizations
enthusiastically adopt AI and extended reality tools for visitor
engagement, yet 82% of cultural institutions surveyed lack the data
governance frameworks and staff skills for production deployment. The
music industry recognizes data as a key asset but remains crippled by
fragmentation and a lack of metadata standardization. Financial services
tells a similar story. GenAI dominates conversations about productivity,
potential layoffs, and competitive advantages. Yet actual implementation
primarily occurs within technology teams and advanced analytics groups.
Core business functions in finance, risk, and fund management still rely
heavily on manually populated Excel files pulled from legacy databases
dating to the 1990s or early 2000s. What passes for AI adoption in most
enterprises amounts to employees using ChatGPT for search and email
generation. Real adoption means companies use AI to automate processes,
enable new capabilities, and serve their specific business and client
needs. The distinction between transformation and modernization gets
lost. Many

While companies claim to be embracing cutting-edge innovation, many of
their workflows remain untouched by it. It's not about adopting GenAI,
but more about modernizing the everyday tools." 3

2026 TRENDS REPORT: DATA & AI

companies talk about transformation when they are actually doing
modernization. They claim to use technology to reinvent their business
through AI, data platforms, and automation. In practice, most effort
goes into fixing legacy systems, cleaning data, or integrating tools
that do not talk to each other. That work is necessary, but it is not
transformation. Investments that companies have made in their cloud
computing platforms are driving the most business results right now.
This is because of the maturity and scale of where these projects can
impact businesses -- a scale that AI, at this point, is not yet able to
match. True transformation happens when technology actually changes how
decisions are made, how people work, and how customers experience the
brand. That requires a deeper cultural shift, and not just new tech
stacks.

Top Data & AI Trends for 2026 1 AI success in 2026 will be driven by
data infrastructure, not new models If 2025 was the year of AI
experimentation, 2026 will be the year of foundational reckoning. The
highest-ROI technology investment right now is data infrastructure, not
the latest AI model. Building proper pipelines, establishing a AI
Adoption Layers clear company-wide approach to data management, and
making data highly available and as close to real-time as possible
represents the best ROI initiative for any company. From there,
implementing analytics and AI solutions can create tremendous financial
benefits.

I'm a strong believer that improving data management is core to
everything our clients do. You can't go far if your enterprise data is
siloed, difficult to modify, hard to access, governed in a way that
becomes a bottleneck, and locked into narrow use cases."

AI Adoption Layers

GenAI Pilots & POCs Where Companies Focus

Process Automation & Analytics Spotty Implementation

Data Platforms & Governance Critical Foundation, often Missing

4

2026 TRENDS REPORT: DATA & AI

The most impactful investments focus on decentralized data platforms,
particularly data mesh architectures and modern stacks from providers
like Snowflake, Databricks, and major cloud platforms. These platforms
offer fast, accessible, API-driven architectures that unlock agility,
democratize data access, and lay out the groundwork for GenAI
integration. Investments are strategic enablers of innovation, speed,
and competitive advantage. Companies that invested in cloud computing
platforms three to five years ago see those investments paying dividends
now at a scale AI cannot yet match. Data platforms enabling self-service
analytics with clear guardrails remain the priority. Technology already
exists, so companies need to implement it before chasing more exotic
capabilities. Data governance deserves special attention. Multiple
organizations have implemented tools like Microsoft Copilot internally
without proper access permissions and governance, causing accidental
sharing of sensitive information across staff. This is not really an AI
issue, but rather a failure in data permissions and governance. Strong
governance frameworks need to be the priority in preparation for AI. The
pattern repeats across industries. Airlines need data-sharing platforms
that enable collaboration with third-party entities. In media and
entertainment, investment in integrating AI with strong data management
delivers tangible results, including hyper-personalized customer
experiences, advanced analytics for content and audience trends, and new
monetization opportunities.

Though everyone talks AI, clients still have significant issues with the
basics of data management and governance. Data projects focused on
Sales, Service, Marketing, and Operations enablement remain the
foundation." Data is the fuel that AI runs on, and companies need to
focus on their data and get the intrinsic value within it through AI."

2 Organizations are moving from broad experimentation to specific,
high-value use cases

AI creates genuine business value in precise, constrained applications.
It remains experimental in broad, undefined use cases. The clearest wins
come from intelligent automation. The shortest path to real business
value for most organizations is AI agents trained to handle manual
processes occurring entirely in digital systems. AI agents are being
used successfully in areas like revenue cycle management. Intelligent
automation using AI agents has clear business value. Developer
productivity tools have achieved near-universal adoption. Tools like
Cursor are now standard. Strong traction exists in unstructured document
processing: extraction, summarization, and limited cross-checking are
becoming routine and reliable. Multiple areas show AI delivering
measurable impact: efficiency gains, process automation, document
automation, customer support, education, content creation, and software
engineering. AI-first solutions are mature enough to deliver business
results across most industries. Software development represents both
AI's biggest success and its biggest complexity. Efficiency gains from
coding copilots vary significantly based on developer experience and
project context. Using coding copilots can show mixed results when
engineers lack maturity or senior-level experience.

AI can dramatically boost software development, but most teams struggle
to use it effectively. Sometimes, marketing hype exceeds what AI can
actually deliver, so teams must be selective about where they apply it.
But the right approach brings huge benefits." 5

2026 TRENDS REPORT: DATA & AI

AI remains largely experimental in decision-making and alpha generation,
especially in regulated industries. In highly regulated financial
services, where trust, transparency, and compliance are paramount, AI's
role in these areas remains exploratory. Healthcare offers a glimpse of
what is coming. Recently, the FDA gave limited approval for the use of
AI agents to improve real-time navigation accuracy during lung biopsies.
This is expected to gain broader clearance in 2026. As more real-world
applications of AI improve healthcare, regulatory bodies worldwide will
improve regulatory guidance on AI-based medical applications, clearing
the path for more innovation. So, what is it that separates success from
failure? Specificity. Companies that start with concrete problems see
returns. In retail, AI delivers clear ROI in demand forecasting, dynamic
pricing, customer service automation, and supply chain optimization.
These areas produce tangible outcomes within months, not years.
Out-of-the-box AI solutions for common business problems deliver more
consistent value than custom-built solutions because they are focused,
accessible, and immediately useful. They are relatively inexpensive and
serve as testbeds and education tools for the workforce. For
differentiating capabilities unique to your business model, custom
solutions become necessary. Chatbots seem like yesterday's news. Real
value lies in solving specific problems.

It's still mostly experimental when companies chase generic AI
transformation without a clear business case. Building chatbots with no
defined use, pilots without ownership, or large language model
experiments disconnected from operations."

3 AI is evolving from proof-of-concept to enterprise-wide deployment AI
adoption in 2026 will look fundamentally different from today's
experimental approach. The shift from proof-of-concept to production has

MlMoaluotarduitxrdIinxsIfhnrsoafhrwsoatwrsinutrigcnutgcuAtruAIervIeavlauleuebybyusuesecacsaese
InIvnevsetmstmenetns ts

AlmAalma GenGeeranteiorantion

BUSINESS IMPACT BUSINESS IMPACT

InteIlnlitgeellnigt ent AutAomutaotmioantion DocDuomceunmt ent
ProcPerossciensgsing

CusCtoumsteormer SerSviecrevice

DecDiseiocins-ioMna-Mkinagking AI AI

DevDeelovmeelormc erc TooTlsools

??

T E C HT EMC HATMUAR ITTUYR I T Y 6

2026 TRENDS REPORT: DATA & AI

already begun. Recent years saw extensive POC experimentation, partly
because the landscape and models evolved quickly, and humans needed time
to adapt. People and organizations embraced small-scale experimentation
but delayed strategic bets and decisions. This initial adaptation is
largely over. AI starts to show true benefits in certain clusters of use
cases for most organizations. This may not be 10x gains, but even 10-20%
is material. General-purpose AI tools will give way to specialized,
agentic applications. By 2026, a growing segment of leading enterprises
will shift from general-purpose tools to more specialized, agentic AI
applications. Enterprise-grade agents in complex environments that
demand mature governance will become the focus for these frontrunners.
Once GenAI is embedded across the organization, writing agents for
business process automation becomes a logical and powerful next step.
That is where the most momentum is expected in 2026: AI moving from
passive assistance to active orchestration of workflows. The AI hype
cycle shows signs of cresting, though experts disagree on timing. Some
believe the bubble is showing signs of bursting as businesses realize
the hype is not delivering immediate transformation. Others counter that
this pattern has another two to three years before reaching a
correction. AI will not feel like a separate initiative. By 2026, AI
adoption will move from pilots to MVP and production among companies
that have built proper foundations. Most companies are still
experimenting, but this is changing for the prepared. AI will be
embedded into everyday workflows through tools, platforms, and
domain-specific agents. We will see less talk about AI projects and more
about AI-augmented teams, where planners, engineers, and marketers all
use AI assistants and agents as part of their daily work. The focus will
shift from building models to governing, integrating, and measuring
value at scale. AI Adoption Evolution AI Adoption Evolution

More and more companies will be shifting to more strategic approaches
for AI adoption, addressing infrastructure weaknesses and the pathway to
production, as well as adjusting organizations to look at AI as a major
catalyst in their business and organizational development strategy." The
surprise will be that AI won't feel futuristic. It'll feel ordinary and
essential, just like Excel once did."

2024 2024

202 202

3202 Phase 3202 Phase

202 202

202 202

Broad EBxropaedriments Experiments

Strategic Pilots &B&SturFFialoodteuuingnngddicaaPttiiioolonnts Building

Production ADADOPrHHeeorcppeedhnnlluooettcyysttmmiroaeentnniott n&&
Orchestration

AIaugmente BBOOAMeeIappccaieeoonurrmmsaagtrttxxmiieooaennmnsste
Mainstream

Mature An
CAEMBEACmmuccaaasrrbbpptooiuneeaassreddssbbesddiifllAiieettiinddeess
Businesf

7

2026 TRENDS REPORT: DATA & AI

Several sectors will see dramatic operational shifts. By 2026, AI will
run more of retail operations than people realize, quietly behind the
scenes. From pricing and assortment to marketing and supply chain, a
large part of day-to-day decision-making will be handled by autonomous
or semi-autonomous agents. Teams will not disappear, but their focus
will shift from execution to judgment and creativity. Healthcare will
see regulatory progress. The FDA approval for AI agents to improve
real-time navigation accuracy during lung biopsies is expected to gain
broader clearance in 2026. One contrarian prediction stands out about AI
PCs: while most organizations focus exclusively on cloud-based AI
implementations, adoption of AI-capable personal computers for
engineering and other internal use cases will grow. Today, many
companies use cloud-hosted AI models because they have cutting-edge
capabilities, and cloud has the hardware to run the models. Yet this
comes at a cost and creates compliance and data security concerns for
many organizations and use cases. Personal computers for AI are starting
to emerge and will offload some use cases to locally hosted open-source
models, which are evolving and becoming good enough for many purposes.
The industry will mature, with new standards, protocols, tools, and
services entering the market. The industry will move from experimenting
to proper engineering.

Smart companies take a different approach: they start by enabling broad
access to AI across the organization, encouraging experimentation,
sharing use cases, building prompt libraries, and embedding AI into
everyday workflows. The more diverse the users, the richer the ideas for
innovation. Technology alone can't drive meaningful AI adoption. It
takes a cultural shift, one that brings AI into the hands of business
users, not just developers."

AI Ininianive Success Facnors

Business Case Defnee?

Yes

FAIL Dana Qualiny Suffcienn?

Yes

FAIL Governance in Place?

Yes

FAIL Proceee no Pilon

Proceee no Pilon

8

2026 TRENDS REPORT: DATA & AI

Rapid experimentation will become the norm, especially in competitive,
complex, regulated industries. Those companies that embrace this change
will be ahead of their competitors. 4 Companies are rethinking
short-term, tech-first AI strategies that fail to scale These are the
five critical mistakes holding companies back: Mistake One: Technology
First, Problem Later The most pervasive error is treating AI as a
technology initiative and expecting tech teams to invent use cases in
isolation. Companies need to stop saying they should use AI widely and
instead look for business challenges, then determine which technologies
can solve them. Mistake Two: Building on Quicksand Companies want to be
data-driven, but in many cases, data quality is poor. Management
understands this and makes data-driven decisions only when they
correlate with their intuition and desires. Mistake Three: Overstating
Capability, Underdelivering Results The biggest issue today is
businesses stating they have a clear and cohesive data strategy when
they do not. In most cases, they have only covered one piece of the data
strategy. It is like saying we have a business strategy and only look at
HR. That is only one element of the overall business strategy. Just
because employees use ChatGPT does not mean customers will view services
as AI-enabled. Mistake Four: Tactical Thinking, Strategic Neglect
Organizations pursue a series of disconnected use cases without
strategic thinking. Everyone claims to be doing AI and agents now, as
these powerful capabilities have become far more accessible. Yet most of
this happens as experimentation and proofs of concept. The pathway to
production is still being figured out. Failure to connect projects with
ROI from the outset is common. Simply hiring a data scientist and
acquiring data is fine, but not durable after initial project releases.
Going tactically for a series of use cases without thinking
strategically about how the production pathway should be enabled is
problematic. Companies need an AI strategy considering a
three-to-fiveyear horizon, value mapping, required business model
adjustments, required technology, and organizational capabilities.
Mistake Five: Misunderstanding AI's Nature The most fundamental error is
treating AI as a precise mechanism working

Stop trying to implement AI with bad data. Create a proper innovation
hub that helps prioritize and truly understand which use cases should be
disregarded altogether, and which should be investigated,
conceptualized, and eventually scaled up." Most companies have gaps in
core capabilities, such as modern, flexible, powerful, and simplified
data platforms, which would allow them to move with AI and agent
adoption at a good speed. Also, the practices and technologies to manage
the risk and governance side of AI and agents are only emerging. This is
the biggest hurdle on the path to production outside of predominantly
internal low risk use cases." 9

2026 TRENDS REPORT: DATA & AI

with logic. AI is probability-based (a 90% result is a good result), and
generative AI does not understand logic at all. When everyone launches
100 AI projects and most fail, companies need to commit. Pick one and
make it work. Do not rush into using AI for everything just because it
is in the news. 5 Semantic modeling, conversational intelligence, and
governance are becoming critical differentiators While everyone chases
large language models and generative AI, several critical technologies
and approaches remain undervalued despite their future importance. Data
Foundation Technologies Semantic Modeling and Knowledge Graphs:
Simplification and unification of data platforms, elimination of data
silos while ensuring consistent data governance, lineage, metadata,
security, and quality layers for the entire data estate are critical.
Systematic approaches to knowledge management, knowledge graphs, and
semantic models serve as key enablers for data-driven LLM and agentic
use cases. AI and Agentic Capabilities Within Data Platforms: Major data
and analytics platforms like Snowflake, Databricks, AWS, GCP, and Azure
have been front and center of developing integrated AI and agentic
capabilities. Much AI and agentic experimentation happens at the
workflow level, but is not well integrated with the data and knowledge
companies have. For many

Many companies underestimate the effort required to work with AI and the
instability caused by its inherent lack of consistency, particularly in
business-critical processes. It cannot simply be implemented and
expected to deliver instant results."

Why AI Projects Fall Why AI Projects Fall

30% 30%

25% 25%

20% 20%

15% 15%

10% 10%

TechTn eicchal nical ImgIle mm gleent maetint oa ntion IssuIesssues

Poor Business C PoaosreBuesf inneitsison Case efnition

Insuffcient Q Insuuaff litcyient Qualit y

ata ata

Lack of LGaocvkeorn f ance G Fra om veerw na onrkcse Frameworks

Inaeuate Resources/ Inaeuate Commitment Resources/ Commitment

10

2026 TRENDS REPORT: DATA & AI

AI and agentic use cases, especially those requiring the use of data and
analytics or performing agentic transactions, these integrated platforms
will be the strong choice. Conversational Intelligence and Natural
Language Query: Conversational intelligence will help businesses with
reporting and enable business users to easily access data.
Implementation of data warehouses with natural language querying across
all areas of an organization would be beneficial to all clients. AI
Governance and Monitoring Tools AI Governance, Monitoring, and
Observability: These capabilities remain undervalued despite their
critical importance for production AI deployments. AI for Business
Process Management: AI for BPM exists but is underused. Implementing it
properly requires extensive data cleansing and preparation.
Next-Generation AI Capabilities Reasoning Generative Models: Training
generative models by receiving sequences of actions and reasoning is
experimental, but it represents the future. SDLC Optimization and AI
Agents Frameworks: It appears unusual that we do not have a centralized
development of something like an AI agent farm framework to automate
SDLC. SDLC is pretty standard in many aspects. It is predefined and
well-known. Having AI agents in the right places could automate
significant portions and potentially reduce team sizes. For now, it
reflects executive beliefs, but technology is catching up rapidly.
Organizations should think about their own models for some tasks: what
routines can you automate with AI? How hard would it be to fine-tune one
of the existing models for that purpose? 6 Enterprises are prioritizing
data lifecycle management, modernization, and human capability The
advice from DataArt experts for the next 18 months is remarkably
consistent across five priority areas. Priority One: Invest in Data
Across the Entire Lifecycle Data represents the single most important
investment area. This includes acquisition, storage, governance,
analytics, and access. How a company manages its data directly
influences everything it does. Whether adopting AI, building agents, or
improving self-service capabilities, all of it becomes dramatically
easier and more scalable with a modern, well-integrated data stack. Get
data strategy aligned to business strategy, then focus on creating an

Projects like automated software development lifecycle (SDLC) agents
remain largely driven by enthusiasts, with limited or nonexistent client
demand. AI fluency is not yet tracked as a distinct skill in project
management, indicating that the industry is still unprepared for the era
of AI-enhanced software development."

11

2026 TRENDS REPORT: DATA & AI

effective, efficient, and economic data platform that enables future AI
deliverables. A combination of modernization and new solutions to grow a
business is needed. Sometimes the answer is AI, but it will not always
be AI. There needs to be a thoughtful view of overall investments.
Priority Two: Modernize Legacy Platforms Now Spend more money on
modernizing legacy data platforms. The longer companies wait, the harder
it will be. Simplification of data architecture and data platforms using
integrated modern cloud and open-source technologies is essential.
Laying out strong groundwork in data management, governance,
engineering, and operations, as well as data interoperability between
platforms, parties, and technologies, enables AI and agentic
technologies initiatives systematically. Priority Three: Train People,
Not Just Models Start training people, not just models. Most companies
should start treating AI adoption as an organizational change, not a
tech project. They need to invest in data quality, cross-functional
skills, and internal AI literacy. Help every employee understand how to
work with AI, not around it. Management does not fully understand AI
technology, and as a result, the use and integration of AI solutions in
business is significantly reduced. The reality is that AI developers
spend their time convincing management of the feasibility of using
particular AI solutions. The AI competency gap between management and
developers has grown significantly. Priority Four: Create Real,
Actionable AI Plans Have a real plan for AI. Not just a socialized idea,
but a real, actionable plan with clear use cases, resource commitments,
and measurable outcomes. Proper upfront planning of large transformation
projects focused on data, back office, and front office applications is
critical. Executives enable initiatives, but most clients do not focus
on proper planning for implementation or ROI that these solutions will
produce. The airline workshop model provides a template: interview each
department and create a list of AI projects that would solve their
biggest pain points. Structure all business processes as much as
possible to gain experience and develop a vision of which processes can
be automated. Develop the ability to properly build application
architecture by eliminating unnecessary details. Identify only those
business processes that can be truly automated and deliver the desired
results. Priority Five: Enable Structured Experimentation Create a
proper innovation hub that can help the organization prioritize and
truly understand which use cases should be disregarded altogether, and
which should be investigated, conceptualized, and eventually scaled up.

It's difficult to overemphasize the importance of data products and data
democratization. Organizations succeed when employees can explore and
analyze data on demand and when foundational data models are complete,
easy to understand, and designed for broad usability."

12

Integrated Modern Platforms

2026 TRENDS REPORT: DATA & AI AI/ML Capabilities

Data Patforms

Agen2 Orchestration

Clean up data problems. Think about buying missing relevant data and
setting up a proper data ontology. Start with out-of-the-box AI
solutions. Educate your people. Create a good environment, including
security, scalability, and compliance, for your company to move with AI.
Facilitate AI adoption among engineering teams. The use of AI models and
tools in engineering, including data engineering and other data-related
tasks, brings meaningful productivity enhancements. The right approach
for AI adoption in software development should be adopted in all
companies. Invest in innovation (AI, data) and cybersecurity. Both
should advance. Otherwise, the IT footprint will be highly
disproportional, creating more risks than value. 7 High-performing
companies are aligning data, people, and purpose to scale AI responsibly

Companies need to choose: take a risky and difficult path to experiment
with new tools and be early adopters or just follow. Being a follower is
riskier as it limits the ability of a company to adapt to the rapidly
changing market."

DataArt experts completed this sentence: "The companies that will thrive
in 2026 are the ones that understand..." Self-Awareness at
Organizational Scale The companies that will thrive in 2026 are the ones
that understand themselves, and the deeper that understanding goes, the
more they'll thrive. That means breaking down the traditional divide
between technology and business to improve internal feedback loops and
accelerate automation. It also means democratizing access to data and
equipping employees with advanced tools to explore, analyze, and act on
it. Self-awareness at the organizational level, knowing how decisions
are made, how value is created, and how technology can amplify it, will
be the defining trait of successful companies. 13

2026 TRENDS REPORT: DATA & AI

Additional Critical Understanding Points The companies that will do best
in 2026 are the ones that understand: · The value of data and data as a
product · How to interpret data to help drive business success and
identify un- tapped opportunities · AI end to end: not just value from
use cases, but data infrastructure, data governance, employee data
literacy, and what real value can be derived from AI · How to become
AI-first in core business processes · That business is simple: being
better than competitors in serving clients · That their people need to
evolve as fast as their technology · That AI is not replacement of a
human but valuable augmentation of properly educated human · How to
treat AI not as a tool but as a core business strategy, embedding
real-time, AI-driven decisions into core workflows to improve efficiency
and accuracy, backed by strong data governance and tightly aligned with
customer needs · Responsible use of AI that works alongside people to
drive faster, smarter decisions · How to apply AI aligned with their
goals and strengths · The possibilities and limitations of generative AI
solutions · It is a generational change, not just hype · That skilled
engineers working with AI can achieve what once took entire teams.

The companies that will thrive are those that know how to interpret data
to drive business success and uncover new opportunities. Success depends
not on the amount of data collected, but on the ability to understand
what it truly means."

Path to AI Success

Scale Successful Use Cases

Pilot iith Clear retrics Foundation Assessment

Use Case Prioritization by ROI Department-by-department Pain Point
Identifcation 14

2026 TRENDS REPORT: DATA & AI

Industry-Specific Predictions for 2026

Several experts offered predictions that might surprise people about
where their industries are heading. Airlines: Rapid Experimentation
Becomes Mandatory Rapid experimentation will need to be the norm because
of the highly competitive, complex, regulated nature of airlines. Those
companies that embrace this change will be ahead of their competitors.
Retail: AI Becomes Invisible By 2026, AI will run more of retail
operations than people realize, quietly behind the scenes. From pricing
and assortment to marketing and supply chain, a large part of day-to-day
decision-making will be handled by autonomous or semi-autonomous agents.
Teams will not disappear, but their focus will shift from execution to
judgment and creativity. Media: Reversing the Isolation Trend In media
there is interest in trying to reverse the trend toward isolation in
media consumption. Streaming famously killed the watch and listen
parties where people would all go to friends' houses to watch episodes
or listen to the latest albums, and there are signs of the desire to
recreate this real-world social experience. Technology Sector: Return of
Engineer Demand There is a renewed demand for skilled engineers.
Predictions that AI would replace business process outsourcing proved
unfounded, and the same applies to software development. AI's ability to
generate code will not eliminate the need for engineers. Healthcare:
Regulatory Clarity Enables Innovation The FDA approval for AI agents to
improve real-time navigation accuracy during lung biopsies is expected
to gain broader clearance in 2026. As we see more real-world
applications of AI improving healthcare, we can expect regulatory bodies
around the world to improve regulatory guidance on AIbased medical
applications, clearing the path for more innovation.

Sustainable transformation depends on people and culture as much as
technology. The greatest barrier to adoption is rarely the tools
themselves but the lack of the right mindset, skills, and trust. Without
these, even the best platforms fail to deliver lasting change."

15

2026 TRENDS REPORT: DATA & AI

Other Notable Predictions · Broad adoption of agentic AI will occur in
organizations with mature data practices and well defined use cases ·
Quantum computing will start to make noise · AI PC adoption for
engineering and other internal use cases will grow as personal computers
for AI emerge and offload some use cases locally hosted open-source
models · People will talk about AI less as pragmatic approaches win over
hype · AI is an enabler tool, not a silver bullet solution. Without the
fundamentals (business goals, business capabilities, insight, action,
recalibration) that have existed throughout business history, it is just
a faster means of throwing good money after bad · We likely
underestimate the social impact of the data and AI transformation ·
Human-like robots will increase their presence in the service industry,
opening new platforms for application development · Some predict a
correction in AI and tech. It is not sustainable that one sector
completely outgrows the rest of the economy into the future forever. At
some point fast-growing areas must slow. That does not mean they must
crash, but they must slow down relative to their own recent growth and
relative to other sectors. 2026 could be the year that AI growth starts
to slow 2026 Predictions by Industry

Healthcare Retail Airlines Tech Media

AI-driven personaliQed treatments become standard in large hospital
systems Stores adopt generative AI for dynamic pricing and real-time
product curation Predictive AI drastically reduces delays through
dynamic re-routing Agentic AI systems autonomously manage cloud
infrastructure and optimiQe cod Generative content becomes 50% of media
output 16

2026 TRENDS REPORT: DATA & AI

How to Prepare for 2026

Some widely adopted approaches need immediate reconsideration. Stop
Separating Technology from Business Do not treat technology as the
silver bullet if your people and processes are broken. Technology will
just make bad happen faster and bigger. End Tick-Box Governance Tick-box
governance needs to go. Governance needs to deliver value and become an
enabler, not a blocker. Most strategies fail because they treat
technology like the answer and people like the problem. Stop
Over-Customizing Everything Too many organizations still rebuild what is
already available off the shelf, from ERPs to data pipelines, burning
time and budget. By 2026, it's about those who standardize, integrate,
and innovate on top rather than reinventing the wheel each time. Stop
Implementing AI Companions Instead of Core Integration AI companions
development means AI solutions are not really implemented into core
business processes. This is problematic. Organizations fail to actually
utilize AI in everyday processes to make things easier. Instead, AI
seems to be implemented for something more glamorous or public-facing to
say that they are using it. Reconsider the Mobile App Obsession Mobile
apps for every little thing are overused. Users do not need a separate
phone app for every event, theater ticket, or payment. Just make it
easier in the browser. Same for enterprise users. Give them one app for
most tasks (the browser) and give the app stores a break. Stop Using AI
Inappropriately in Software Development AI for software development
inappropriately or prematurely should be reconsidered. Building internal
platforms to consume AI agents with internal company expertise,
knowledge, and best practices when core capabilities are not ready is
problematic. Critical Insights for Implementation Several experts
offered additional insights valuable for organizations planning their AI
and data strategies. The Messaging Challenge The ability to speak to
business rather than technology outcomes is critical to winning more
business. Organizations are very good at technology

A common mistake in AI adoption is starting with the technology rather
than the problem. In the current hype cycle, many companies launch
pilots because it's trendy, without a clear business case, data
readiness, or a plan to scale. The more effective approach is to begin
with a specific, high-value use case--such as reducing churn, improving
demand forecasts, or automating routine workflows--prove ROI quickly,
and then scale." 17

2026 TRENDS REPORT: DATA & AI

delivery and less so at explaining to clients in business language what
they bring to the table in language that resonates with them. Messaging
needs to be aligned with the target audience. ROI Justification Remains
Critical For airlines and similar industries, the hardest part is
justifying and showing ROI for a project. While there is no shortage of
needs, sometimes it is hard to beat another project that needs funding.
Projects that can demonstrate an increase in revenue are usually first.
Cost savings are significant, but sometimes this metric is overlooked.
Improvements in customer experience are also seen as valuable. The
Crawl-Walk-Run Maxim Follow the maxim crawl, walk, run to achieve
lasting success and make sure your people are grounded in reality, not
hype Business Language Translation Organizations need help understanding
their internal processes and how they influence the business value they
provide to their customers and stakeholders. It is really hard to
combine the puzzle until you try to digitalize it, as it requires a good
understanding of the requirements and provides quick, real-world
feedback. So digitization is indeed one thing where all companies fall
behind. The second is agile. There are loads of pseudo-agile frameworks,
but very few companies are ready to embrace failing. It is still
primarily a failure-is-not-an-option culture, which is anti-agile.
Timing Considerations Organizations that have not yet begun investing in
foundational data capabilities will find it challenging to achieve
meaningful AI deployment by 2026. This suggests that the window for
building core infrastructure is narrowing, and early action is critical.
Conclusion: The Base Determines the Future The evidence from DataArt's
experts is unambiguous: 2026 will be defined by who built the strongest
foundations. After two years of experimental euphoria, the market is
approaching a moment of reckoning. Companies with robust data
infrastructure, mature governance frameworks, and AI-literate workforces
will accelerate into production deployment. Those still chasing
headlines while their core operations run on legacy systems will face a
painful reality check. The disparity between AI rhetoric and operational
reality cannot hold. Financial services firms discussing generative AI
while fund management runs on 1990s databases. Museums implementing
extended reality without staff

One widely adopted practice that companies should seriously reconsider
by 2026 is the persistent separation between technology teams and the
rest of the business. In many organizations, the tech department
operates remotely, sometimes entirely offshore or in a different state,
and is treated more like a service provider than a strategic partner.
The interface with technology is often slow, overly formalized, and
designed to tightly control spending and centralize decision-making.
This creates a fragmented understanding of business challenges, limits
essential automation, and fosters a complex, inefficient relationship
between tech and business units. This separation, both physical and
organizational, needs to go. Businesses should be closer to technology,
and technology should be embedded in the business."

18

2026 TRENDS REPORT: DATA & AI

digital skills. Retailers launching chatbot pilots while supply chains
remain manual. This gap will close, one way or another. Three Clear
Paths emerge for 2026: Path One: Foundation-First Success Companies that
invested in data platforms, governance, and organizational readiness
over the past 18 months will shift decisively from pilots to production.
They will deploy AI agents for business process automation, embed AI
into everyday workflows, and realize measurable productivity gains.
Their teams will use AI assistants as naturally as they use spreadsheets
today, making the technology feel extremely ordinary. Path Two:
Correction and Reset Companies that chased hype without the groundwork
will pull back after disappointing results. The high AI project failure
rate will force strategic recalibration. Some will use this reset
wisely, finally investing in data quality, governance, and skills.
Others will become cautionary tales and potentially acquisition targets.

Companies should reconsider the rush to implement AI innovation without
a solid base in data governance and staff readiness. Numerous examples
of ambitious AI initiatives in 2025 have shown that adopting AI as a
standalone innovation for innovation's sake is risky and often
ineffective. Ethical frameworks, transparency, human-AI collaboration,
and cultural alignment with mission-driven objectives must guide AI
deploy- ment."

The 2026 AI Maturity Model

HIGH

Fast Followers Experimenting with AI, limited by data

Leaders Fully integrated AI and data strategy

AI ADOPTION Progress

LOW

At Risk Disconnected data, minimal AI Progress

Foyatio ilyers Strong data, early AI phase

Weak

Data foundation Strength

STRONG

19

2026 TRENDS REPORT: DATA & AI

Path Three: Strategic Stagnation Companies that neither built nor
learned from failures will continue to make slow progress. Still talking
about bringing data together, still migrating to cloud, still slightly
starting with AI. Their technology footprint will grow increasingly
disproportional, creating more risk than value. The competitive gap will
widen. The distinguishing factor across these paths is consistent:
organizations that built strong data foundations, invested in people
alongside technology, and took strategic rather than tactical approaches
to AI will pull ahead decisively. People matter as much as platforms.
Next year, successful companies will be the ones that are ready to
adapt, experiment, and scale new ideas quickly. They will treat AI
adoption as organizational change requiring cross-functional skills and
internal AI literacy, not a technology project confined to IT
departments. The separation between technology and business must end.
Technology embedded in business operations, with fast feedback loops and
collaborative problem-solving, will outperform organizations where tech
departments operate as distant service providers. This organizational
integration matters more than any specific AI capability. Specificity
beats hype every time. Companies applying AI to concrete, measurable
problems (demand forecasting, revenue cycle management, document
processing, engineering productivity) see returns within months.
Companies chasing generic AI transformation without clear business cases
waste resources on disconnected experiments. The prediction that might
surprise some: AI will not feel revolutionary by 2026, but rather pretty
ordinary. Retail pricing agents make thousands of daily decisions.
Healthcare navigation systems guide procedures. Engineering tools
amplifying senior developer productivity. Marketing assistants help
teams work faster. The surprise will be that AI becomes as unremarkable
as Excel: embedded, essential, expected. One expert captured the
fundamental truth: business is simple: being better than your
competitors in serving clients. Everything else (AI, data platforms,
agents, automation) exists to serve this single purpose. Companies that
lose sight of this in their pursuit of technology for technology's sake
will fail despite technical sophistication. The window for
infrastructural investment is narrowing. Organizations not already
addressing data quality, governance, and organizational readiness will
struggle to catch up. But for organizations willing to do the
unglamorous work (cleaning data, modernizing platforms, training people,
aligning strategy, building governance), the opportunity remains
substantial. AI is a generational change, not a hype cycle. The question
is whether your organization will be ready when technology matures.

Moreover, legacy reliance on fragmented, siloed data and lack of
standardization, especially prominent in sectors like music and art,
must be addressed first to avoid undermining AI and digital
transformation efforts. Without prioritizing data governance,
transparency, and human oversight, AI projects risk bias,
misinformation, and loss of trust. Therefore, by 2026, companies should
seriously reconsider any AI or tech adoption strategy that skips core
data quality, ethical frameworks, and staff upskilling, and prioritize
efforts toward integrated, governance-first, and human-centered
approaches to AI and technology innovation."

20

2026 TRENDS REPORT: DATA & AI

About This Report

This report synthesizes insights from comprehensive interviews with
DataArt's senior data, AI, and technology experts across airline,
financial services, media, healthcare, retail, and cultural sectors.
Expert contributors include specialists in data architecture, AI
strategy, enterprise transformation, and industry-specific technology
implementation. Their collective experience spans decades of delivering
data and AI solutions to enterprise clients navigating digital
transformation.

Methodology: DataArt conducted structured interviews with technology
leaders and subject matter experts in September and October 2025, asking
identical questions about technology trends, implementation challenges,
and predictions for 2026. This report preserves the authentic voice and
specific insights from those conversations while organizing them into
actionable themes for enterprise decision-makers.

Acknowledgments: DataArt extends a sincere appreciation to Alexander
Makeyenkov, Alexei Miller, Alexey Utkin, Alistair Wandesforde, Andrey
Ivanov, Anna Serebryannikova, Daniel Piekarz, Denis Baranov, Dmitry
Butalov, Doron Fagelson, Ed Simmons, Georgina Gill, Ilya Aristov, Marcos
Mauro, Matt Ambrogi, Pavel Khrulev, Pavel Smirnov, Paul McDonald, Raman
Sidarenka, Russell Karp, Tim McMullen, Yuri Gubin, and Yury Kabrits for
their valuable contributions. Their insights and expertise played a key
role in shaping this report.

24 experts interviewed

About DataArt DataArt is a global software engineering firm that
delivers breakthrough data, analytics, and AI platforms for the world's
most demanding organizations. As the partner for progress in the digital
age, our world-class teams artfully design and engineer data-driven,
cloud-native solutions that generate immediate and enduring business
value. We combine global scale, deep technical expertise, and
progressive vision with advanced R&D Labs, frameworks, and accelerators
to solve our clients' toughest challenges. Since our founding in New
York City in 1997, DataArt has grown to bring together 6,000+ experts
across 40+ locations in the US, Europe, Latin America, India, and the
Middle East, with clients including major global brands like Priceline,
Ocado Technology, Legal & General, and Flutter Entertainment. Recognized
as a 2023 Newsweek Most Loved Global Workplace and 13 times as an
Inc. 5000 Fastest Growing Private Company, we are proud of our
reputation as a great place to work and partner with.

40+ standout quotes featured throughout the report 6 industries covered:
Airlines, Retail, Media, Healthcare, Technology, Financial Services

21