Artificial intelligence (AI) has been described as a set of technologies that gives computers
vision and lets them understand written and spoken language; as machines capable of
performing functions usually associated with human minds; as the simulation of human
intelligence; as an unfathomably large potential boon to human productivity; and as the
possible doom of humankind.
In short, AI is at the center of several raging business and societal debates—but the terms
of those debates have shifted dramatically. For more than a century, AI had captured the
imaginations of a small group of philosophers, science-fiction fans, mathematicians, and
computer scientists. Now, three years past the debut of ChatGPT, businesses of all sizes are
actively implementing AI solutions, from chatbots and predictive analytics to autonomous
agents that can complete complex multistep tasks.
For small to medium-sized businesses (SMBs), however, the questions have become practical and
urgent: Which AI tools should we implement first? How do we integrate AI into our existing
workflows? And how do we scale AI capabilities as they become more sophisticated? These are
questions born not of curiosity but of competitive necessity.
This article addresses those practical questions with practical answers. It explains the
highlights of AI’s history, what it can do for SMBs, how it works, and the business benefits
it can bring.
What Is Artificial Intelligence?
AI is a set of technologies and practices meant to create computer-based systems capable of
performing tasks that previously relied on human intelligence. These tasks range from
recognizing patterns and making predictions to understanding language and solving complex
problems.
But what they all have in common is that they require making decisions based on information.
Therefore, all AI systems include data and algorithms that process and, sometimes, act on
the data. Some AI systems, such as generative AI chatbots, are trained on so much data that
it becomes a newsworthy talking point. But the multitude of AI systems that are more
narrowly focused on individual business tasks need only enough data to address the job at
hand—which may still be a large volume of data by everyday standards.
AI algorithms are so numerous and varied that they are tricky to discuss at a high level. But
they all share the ability to recognize patterns in data, make decisions or predictions
based on that information, and, in many cases, learn from the quality of their decisions. It
is how each algorithm works that makes the primary differences among many of the AI
technologies and approaches you’ve likely heard of, such as machine learning (ML), deep
learning, natural language processing (NLP), computer vision, and the neural networks that
underlie most of them. These technologies enable machines to understand—and generate—speech,
recognize images, and make autonomous, problem-solving decisions.
Any discussion about AI systems must also include the human factor. People create and
fine-tune the software that embodies the algorithms, design the hardware to run the
software, judge the quality of AI systems’ output, and, often, provide feedback that helps
the systems improve.
Key Takeaways
- AI can enable computers to learn, solve problems, and make decisions.
- Integrating AI capabilities into business technology solutions can help companies
automate more operations, gain better insights from their data, and improve customer
experiences. - The future of AI is being shaped by several advancements and the increasing importance
of human-AI collaboration, both of which present opportunities and challenges for
businesses. - Agentic AI systems are being developed that can autonomously manage entire business
workflows. - As AI moves forward, businesses that effectively harness its capabilities and adapt to
the changing landscape will be better positioned to thrive than those that fall behind.
Artificial Intelligence Explained
Why should businesses, particularly SMBs, care about AI? The reason is because a multitude of
narrowly focused AI algorithms have been embedded in, or added to, practical business
applications that can help improve efficiency, reduce costs, and drive growth. Business
software with AI capabilities can help SMBs automate more repetitive tasks, gain valuable
insights from data, and enhance customer experiences in ways that, before AI, were open only
to bigger companies with deeper pockets.
For example, AI-powered customer service chatbots and virtual assistants can handle basic
customer inquiries 24/7, freeing up human staff to focus on more complex issues. They can
also learn from customer interactions to provide more personalized and efficient support
over time. Today’s AI-enhanced chatbots are far more sophisticated than the rules-based
systems that frustrated customers in earlier years; they’re capable of understanding
context, handling complex queries, and even detecting customer sentiment.
In marketing and sales, AI algorithms can analyze customer data to identify patterns and
preferences, enabling businesses to create targeted marketing campaigns and personalized
product recommendations that can lead to higher conversion rates and increased customer
loyalty. And that, of course, means more revenue.
Internally, AI can help companies improve automation in data entry and inventory
management tasks, for example, reducing the risk of human error and saving
time. It can also assist with financial management and accounting functions, such as demand
forecasting, budgeting and planning, and producing financial statements.
Increasingly, AI agents—systems that can autonomously plan and execute multistep tasks—are
being deployed to handle complete workflows, from processing invoices to onboarding new
employees.
How Does AI Work?
SMBs owners and managers should understand two important aspects about how AI works. One is
about how AI works in the big picture—where does its ability to analyze company information
and help businesses make better-informed decisions come from? The other is how they are
likely to experience AI, in action, in their own organizations.
How AI Works: The Big Picture
AI works by processing data, identifying patterns in the data, using the patterns to make
decisions or predictions, getting feedback on the quality of its choices—and iterating that
process hundreds, thousands, or millions of times. Let’s break that into steps:
- Data input: AI systems generally need training or a combination of
careful instruction and data access before they can be released into the world. Either
way, that means large datasets. The data can come from a combination of different
sources, such as internet-connected sensors, company databases, and user interactions.
And it can have multiple modalities, such as text, images, audio, and video. - Initial processing: Whatever its type, real-world data is usually
messy. It must be preprocessed to remove irrelevant and redundant information and
transformed into a format that the AI system can understand and analyze. This usually
involves techniques like data cleaning and normalization. - Algorithm writing/selection: The heart of any AI system is its
algorithms—the mathematical models and instructions that tell the system how to process
and learn from the data. Historically, there have been many different types of AI
algorithms, but today most AI systems use one or more of the various approaches to ML,
including deep learning and neural networks. ML algorithms also include ones that train
the AI system (discussed later in this article). Each approach—and, of course, each
individual algorithm—has its own strengths and weaknesses that can make it better or
worse at any given task. - Training: This involves feeding the data into the AI system (which
may include dozens of algorithms) and allowing it to learn and adjust its internal
parameters to better fit the patterns and relationships within the data. - Testing and validation: After training, the AI system is tested on
data it has never seen before to evaluate its performance and accuracy. This helps to
determine that the system has not simply memorized the training data but can generalize
to new situations. If the system’s performance is not satisfactory, it may need to be
retrained with more data or its algorithm(s) may need to be revised. - Deployment: The AI system can now be deployed into a production
environment to make predictions or decisions based on real-world data.
Throughout the entire process, human oversight and intervention are crucial. Data scientists
and AI experts are involved in selecting and preparing the data, choosing the appropriate
algorithms and fine-tuning the system’s performance. They also monitor the system’s outputs
for accuracy.
Bear in mind that all of this is still rapidly evolving. As AI technologies continue to
advance, new approaches and architectures are emerging that can handle more complex tasks
and larger datasets. For example, the AI “transformer” architecture was introduced in a June
2017 academic paper and, based on that, the generative pretrained transformer (GPT) model
was first described a year later in a June 2018 paper. Since then, GPTs have pushed the
boundaries of what AI can do in terms of understanding and generating language, making
ChatGPT possible only four years and five months later, in November 2022.
Despite these ongoing advancements, AI systems are still limited by the quality of the data
they are trained on, the quality of the data they are given to act on in business
applications, and the potential inherent biases and assumptions built into data and
algorithms.
How AI Works for SMBs
In practice, SMBs experience AI in two ways: through the enhanced capabilities and
efficiencies it brings to their everyday tools and processes and through standalone AI
software, such as ChatGPT, Claude, and other models, the public versions of which have been
rapidly adopted by businesses. For example, Verizon’s “2025 Mobile Security Index” report
found that 93% of responding organizations report that employees have incorporated
generative AI tools on mobile devices into their daily workflows. However, the report also
found that only half of business organizations have formal guidelines in place to govern the
safe use of GenAI.
AI algorithms are usually integrated into various business applications that organizations
are familiar with and, often, already using. For example, AI may already be integrated in
relatively subtle ways into software-as-a-service (SaaS)
applications, such as customer relationship
management (CRM), marketing
automation, and accounting software, since many SaaS providers have begun to
incorporate AI capabilities. A CRM system might use AI algorithms to analyze customer data
and provide personalized recommendations for sales and marketing strategies. A business
manager simply interacts with the CRM interface, while the AI works in the background to
process data and generate insights.
Other products that SMBs may use, or may want to consider using, make more ambitious use of
AI capabilities, including recent advances in GenAI. Companies are still figuring out how to
incorporate these capabilities, so many different approaches are emerging.
Weak AI vs. Strong AI vs. Superintelligence
One of the ways that philosophers and computer scientists have long categorized AI is “weak”
or “strong.” Weak AI is everywhere, referring to the scope of what an AI system can do, not
the quality with which it performs its tasks. All AI systems used in businesses today are
narrowly focused on specific functions or capabilities, rather than on trying to replicate
general human intelligence. Even large language models (LLMs) are considered weak/narrow,
despite their wide-ranging knowledge, because they are limited to language. Their abilities
cannot be extrapolated to visual understanding, motor control, or complex decision-making,
and they don’t learn and adapt the way human intelligence can.
No instances of strong AI, also called artificial general intelligence (AGI), are known to
exist—which is ironic in that AGI has been discussed consistently in science fiction and
news media, garnering the most societal debate since the emergence of GenAI. An AGI would,
theoretically, be equivalent in intelligence to the smartest humans. Debate continues in
scientific communities about how close to AGI some AI systems may come.
Beyond AGI lies an even more theoretical concept: artificial superintelligence (ASI), also
called superintelligence. ASI refers to a hypothetical AI system that would vastly surpass
human cognitive abilities across all domains—from scientific reasoning and creative
problem-solving to emotional intelligence and strategic thinking. Superintelligence is
firmly in the realm of speculation.
For SMBs, understanding the distinction between weak AI, AGI, and superintelligence is
important context. The business applications available today, which are all forms of weak
AI, are already powerful enough to transform operations. The key is to focus on implementing
proven, narrowly focused AI solutions that can deliver measurable value now, while staying
informed about how the broader AI landscape evolves.
What Can AI Do?
People tend to anthropomorphize—that is, project human attributes onto—just about everything,
including AI. So many people think of AI as “thinking.” But it does no such thing. What does
AI actually do? Here are eight key features, which can be incorporated into business
applications in ways that lead to potentially significant benefits—and, sometimes, to the
uncanny sense that the software can actually think.
- Analyze data: If data is the new oil, then AI can be a major
refinery. AI algorithms can process and analyze vast amounts of structured and
unstructured data much faster and more accurately than humans. This enables them to
discover hidden patterns and trends that can inform business
intelligence and company decision-making processes. AI-powered data
analysis can be applied to virtually any field. - Automate processes: From data entry and document processing to
inventory management, warehousing, and accounting, AI
can automate
repetitive
and time-consuming tasks, freeing up human resources to focus on work that adds
significant business value. AI-powered automation can improve efficiency, accuracy, and
productivity across various industries. - Detect objects and patterns: Humans are excellent
pattern-recognizers, but AI can do it with far more data than we can keep in our
heads—and many times faster. AI algorithms can identify and recognize objects, patterns,
and anomalies across visual, textual, and numerical data. AI-powered computer-vision and
image-recognition technologies can accurately identify and classify objects, faces, and
patterns in images and videos, which is why it is so useful in security and
surveillance, medical imaging, and autonomous vehicle applications. Beyond visual data,
AI algorithms can be used in business
analytics to detect patterns and anomalies in large datasets. It can help
identify fraudulent transactions in financial data, recognize spam or malicious content
in emails, and discover trends in customer-behavior data. - Personalize recommendations: AI can analyze user behavior and/or
purchase data and provide personalized recommendations and experiences. This capability
is widely used in ecommerce, streaming services, and content platforms to improve user
engagement and satisfaction. - Translate languages: AI-powered language translation tools can
instantly translate text and speech from one language to another, with increasing
accuracy. This can help managers and executives communicate across borders and cultures,
whether they are traveling abroad or doing business with international partners. - Generate text and images: AI tools that can generate human-like
text and realistic images based on written prompts and examples took the world by storm.
From art and design to content creation and beyond, some business analysts believe GenAI
has the potential to add trillions of dollars to the global economy annually by
increasing knowledge worker productivity. Tools like ChatGPT and Claude for text
generation, Perplexity for research, and Midjourney and DALL-E for image generation have
shown GenAI’s potential. - Summarize data and text: AI can automatically summarize large
volumes of data and text, extracting key points and insights. In a world drowning in
information, this capability can be a lifeline. It makes it far easier for researchers
and business managers, for example, to cut through the details of large documents or
datasets and get to the heart of what matters to their organizations. - Converse in natural language: AI algorithms can make computer
systems understand speech. This capability lies at the core of tools like AI-powered
chatbots and virtual assistants. But, over time, all kinds of information technology
systems can benefit from integrated algorithms that let people interact with them in
natural language.
Types of AI
There are many ways to categorize AI systems. The weak versus strong AI discussion above is
one. But AI systems can also be differentiated by training method, capability level, and the
underlying algorithm’s core approach. Complicating the matter is that over the course of an
80-year history, various avenues of AI research appeared to reach dead ends, only to be
“rediscovered” years, or even decades, later—often with different names.
Here is a useful way to categorize AI systems based on the types widely in discussion now:
-
Decision trees
are a prime example of a pre-deep-learning/neural network approach
to AI
algorithms.
Tree models make decisions based on a series of questions; so-called “random
forests” are
collections of decision trees working together that are more accurate than
individual trees.
Symbolic regression, genetic algorithms, and Bayesian networks are other examples of
different algorithmic approaches to the challenge of building machines that learn.
These AI
approaches, and many more, continue to operate in products today. -
Machine learning
is a subset of AI at the same level as all the approaches in the
first
category, but
it’s the
big one—at least for now. ML encompasses a multitude of algorithms and statistical
models
that enable computers to improve their performance on a task through experience,
without
being explicitly programmed. The next two categories, below, are specialized
applications of
ML. And so are many AI systems that are trained to perform narrow, well-defined
functions
using large, structured datasets (i.e., data that is labeled and usually organized
in rows
and columns). ML techniques are used in most modern AI systems. -
Deep learning
is a subset of ML that can take advantageous use of neural
network
architectures with
more than two “hidden” layers of artificial neurons. Depending on the complexity of
the use
case, deep learning neural networks can have up to hundreds of layers. Although AI
systems
using pre-deep-learning approaches, such as decision trees, could perform image
recognition
and process natural language, newer AI systems using deep-learning ML techniques and
running
on neural network architectures outperform them. Self-driving cars, however, are an
example
of a use case that became possible only through deep learning. -
Generative AI
uses a subset of deep-learning ML technologies and deep neural
networks to
construct AI
models that, once trained, can rapidly create content in response to text prompts.
Different
GenAI tools can produce new audio, image, and video content, but it is the
text-oriented
conversational AI of LLMs that has generated the most excitement. GenAI models
represent a
significant advance in AI because they exhibit many AI capabilities that begin to
bridge the
gap between weak and strong AI systems, including natural language understanding and
generation, knowledge synthesis, problem-solving across multiple domains of
expertise, and
complex reasoning. Consequently, people can converse with and learn from advanced
GenAI
models in pretty much the same way they do with humans.
The Relationships Among AI, ML, and Deep Learning
Machine learning is a subset of AI that enables systems to learn from data. Deep
learning is an even more specialized subset of ML that can make use of multilayered
neural networks. These AI approaches are generating the most excitement at the
moment,
although other approaches, such as decision trees and symbolic AI, exist alongside
ML as
alternative methods for creating intelligent systems.
Approaches to AI Model Training
SMBs are unlikely to work directly on training AI models. But it is nonetheless important to
understand AI training because, without it, an AI system would be virtually useless.
Training is where AI models learn to perform specific tasks by absorbing information from
examples—that is, data, and lots of it. The quality and quantity of data used in training,
as well as the choice of training approach—different training methods are suited to
different types of AI models, tasks, and data—significantly affect the resulting AI system’s
performance. For these reasons, selecting the appropriate training approach is key to
building effective AI applications.
But why choose only one? The most practical applications of ML tend to use a combination of
techniques instead of relying on one approach. There are five main approaches to training AI
models:
- Supervised learning: In this method, AI systems are trained using
labeled data, meaning that both input data and the correct output are provided. The AI
learns to map inputs to outputs based on the examples it is shown. Supervised learning
is commonly used for tasks such as image classification, sentiment analysis, and
predictive modeling, where a target variable or outcome to predict is clear. - Unsupervised learning: AI systems trained with unsupervised
learning are given unlabeled data and must identify patterns, structures, or
relationships on their own. The AI learns to group similar data points together or
detect anomalies without being explicitly told what to look for. This approach is often
used for tasks like customer segmentation, anomaly detection, and data compression,
where the goal is to discover hidden data patterns or insights. - Reinforcement learning: In reinforcement learning, AI systems
learn through trial and error, receiving “rewards” or “punishments” based on their
actions. The AI learns to make decisions that maximize its cumulative reward over time.
One technique, known as reinforcement learning through human feedback (RLFH), has
recently come to public prominence because it played a crucial role in the development
of GenAI. In RLFH, human feedback helps the AI create a reward model that represents
human preferences and values, which then become part of the model’s output when it is
put to work on its assigned task(s). Reinforcement-learning techniques are commonly used
in game-playing AI and robotics applications; recommendation systems, such as those used
by Netflix and Amazon; and certain self-driving car technologies. - Semi-supervised learning: This is a hybrid that combines elements
of supervised and unsupervised learning. In this technique, AI systems are trained using
a small amount of labeled data along with a larger amount of unlabeled data. The AI
learns to generalize from the labeled examples and to leverage the structure in the
unlabeled data to improve its performance. This is particularly useful when labeled data
is scarce or expensive to obtain, as it allows the AI to learn from a combination of
labeled and unlabeled examples. It’s usually used in combination with one or more of the
other training models for applications in robotics, text and image classification,
recommendation systems, autonomous vehicles, and more. - Transfer learning: With transfer learning, AI systems use knowledge
from one training task to accelerate learning on a different but related task.
Developers start with a model that has been trained on a large dataset—such as one that
recognizes objects in millions of photographs—and then retrain only the final layers of
the model to adapt it for a more specific purpose, such as identifying manufacturing
defects or classifying medical images. This reduces both the amount of training data
needed and the time required to train for the new task. Transfer learning is used in
computer vision applications—such as quality-control systems and product recognition—and
in NLP tasks, like customer sentiment analysis and document classification.
Benefits of AI
The list of benefits that AI systems can bring to a business is long, varied, and still
growing. Keep in mind that these advantages stem directly from AI’s ability to recognize
patterns, make decisions or predictions, and learn by reviewing their performance—all of
which humans do as well or better. But because of their computational power, AI systems can
reach these conclusions much faster than humans and do so while analyzing many times more
data.
- Improves accuracy: AI systems process and analyze vast amounts of
data with a high degree of precision, reducing the risk of errors and inconsistencies.
For example, AI-powered medical tools can analyze patient data and imaging results to
provide more accurate diagnoses, while AI-based fraud detection systems can identify
suspicious transactions with greater reliability than human analysts. - Increases efficiency: By automating repetitive and time-consuming
tasks, AI can help businesses and individuals work more efficiently and productively.
For instance, AI-based document processing tools can extract relevant information from
large volumes of text, saving time and effort. - Enhances decision-making: AI’s ability to analyze many times more
data than people can lead to better-informed decisions. AI systems, particularly in
fields like healthcare, finance, and logistics, are assisting in decision-making through
advanced predictive modeling and data analysis. - Offers high availability and scalability: These generally are
benefits of the underlying IT infrastructure that supports an AI system rather than of
the AI itself. Nonetheless, because AI systems for SMBs are almost always deployed as
cloud-based software, it is the case that they can operate 24/7 and are always available
when needed. This is particularly valuable in industries such as healthcare, where AI
embedded in monitoring systems can continuously track patient vital signs and alert
medical staff to potential issues, or in customer service, where AI chatbots can provide
around-the-clock assistance. On the scalability side, the same cloud infrastructure
makes AI systems easy to scale up or down to accommodate changing demands and workloads. - Personalizes results: AI algorithms can analyze customer data and
preferences to provide highly personalized experiences and recommendations. This is
evident in applications such as streaming services, where AI algorithms suggest content
based on a user’s viewing history, or in ecommerce, where AI-powered product
recommendations are tailored to individual shopping behaviors and interests. - Reduces repetitive tasks: Business applications with embedded AI
capabilities can automate mundane and repetitive tasks, allowing humans to focus on more
creative or strategic value-added activities. AI-capable apps can handle data entry,
invoicing, and other routine administrative tasks, while content moderation systems with
AI can automatically flag and/or remove inappropriate material from online platforms. - Converses with humans: NLP capabilities can enable any business
system to engage in natural, conversational interactions with workers, providing
information, assistance, and support. This can improve accessibility and make new forms
of human-machine collaboration possible. For example, workers in assembly plants can
already get real-time guidance from AI systems via augmented reality headsets, and
AI-powered virtual health assistants are interacting with patients via voice and text
messages to help them stay on track with their medication regimens. - Creates computer code: Generative AI systems can assist in writing
and optimizing software code, boosting developer productivity and reducing errors.
AI-powered code completion tools can suggest relevant code snippets and functions as
developers type, and AI-based code optimization systems can automatically refactor and
streamline existing codebases. - Accelerates innovation: AI can speed up research and development
processes, leading to faster innovation. Because they can process large volumes of
scientific data rapidly, AI systems are accelerating discovery in fields from
pharmaceuticals and material science to astrophysics. Similarly, AI can accelerate
creative processes by collaborating with human workers to generate ideas, designs,
music, art, and even literature. - Refines risk mitigation: AI’s predictive capabilities can help
identify and mitigate potential risks before they become problems. For example, an AI
system could analyze data related to weather, geopolitics, and transportation routes to
predict potential supply chain disruptions. Business managers could identify alternative
suppliers, routes, etc., in advance. - Optimizes predictive maintenance: In industries that use heavy
machinery, such as manufacturing, AI can predict when machinery or equipment will fail
or require maintenance, helping to prevent breakdowns before they happen. This reduces
downtime and improves operational efficiency. - Improves accessibility: AI can make services and information more
accessible to people with disabilities. A website’s map of bus routes, for instance,
could contain AI-generated text aligned with the images that describe the routes in
detail. That could be paired with NLP to read the route descriptions out loud for
visually impaired people. - Leads to better healthcare outcomes: In medicine, AI can lead to
earlier disease detection and more personalized treatment plans.
Examples of AI Technologies
AI research and evolution have produced many different, specialized technologies, each with
its own distinct applications and potential to reshape industries. The following nine AI
technologies are some of the most impactful and widely adopted.
-
Computer vision:
Computer vision enables machines to understand visual information
from
the
world around them. It is used in facial recognition, object detection, and image
classification applications. In retail stores, for example, it’s used in automated
checkout
systems and inventory management (for example, by deducting an item from inventory
as it
is
purchased). It’s also a crucial component for autonomous vehicles. In security, it
powers
surveillance systems; in healthcare, it aids in medical imaging analysis and disease
diagnosis. -
Weather modeling:
AI algorithms analyze vast amounts of meteorological data,
including
satellite imagery, radar, and historical weather patterns, to generate accurate and
detailed
weather forecasts. Beyond meteorologists, weather models are used by farmers to
optimize
crop planting and harvesting, by utilities to anticipate energy demand, and by
emergency
services organizations to better prepare for and respond to severe weather events.
Some
large businesses incorporate weather data into their demand forecasting analyses, a
practice
likely to become available to SMBs, too, as the technology becomes less expensive
and
easier
to use. -
Autonomous vehicles:
Self-driving cars, trucks, drones, and other autonomous
vehicles
rely
on a combination of AI technologies, including computer vision, sensor fusion
(integrating
data from multiple sensors to create a more comprehensive and accurate understanding
of
the
surrounding environment), and decision-making algorithms to navigate roads (and
skies)
safely without human intervention. This technology is being developed and tested by
automotive manufacturers, technology companies, and transportation services. The
potential
benefits include reduced traffic accidents, increased mobility for elderly and
disabled
individuals, and better traffic flow in cities. -
Fraud detection:
In fraud detection systems, AI algorithms analyze patterns of user
behavior
to find anomalies that suggest fraudulent activities and then try to prevent the
associated
action, either on their own or by alerting human agents, depending on the situation.
This
technology is widely used in banking, insurance, and ecommerce. It helps financial
institutions protect customers’ assets, reduces business losses due to fraud, and
helps
organizations comply with industry regulations. In ecommerce, AI-powered fraud
detection
can
reduce chargebacks and enhance trust in online transactions. -
Speech recognition:
Speech recognition technology uses AI algorithms to convert
spoken
language into written text. It’s often included as an initial step in an NLP
application
but
shouldn’t be confused with that broader technology (see below). To avoid such
confusion,
it’s often called speech-to-text technology. By either name, it is used in
applications
like
dictation, virtual assistants, and customer service automation. In
telecommunications,
it
powers voice-activated dialing and customer support. In healthcare, it enables
voice-to-text
transcription of medical notes and hands-free documentation. In automobiles, speech
recognition is a part of voice-controlled navigation and entertainment systems. -
Natural language processing:
NLP is an AI technology that enables computers to
understand,
interpret, and generate human language. While speech recognition focuses on the
acoustic-to-text conversion, NLP is meant to achieve—and output—a deeper
understanding
of
language content, regardless of whether it originated as speech or text. It’s used
in
applications such as sentiment analysis, text summarization, and automated
translation
from
one language to another. In marketing, NLP helps companies analyze customer feedback
and
social media mentions. In media, NLP assists in content recommendation systems and
automated
news aggregation. In finance, it powers the analysis of financial reports and market
sentiment for investment decisions. -
Virtual assistants:
Virtual assistants are AI-powered software agents that can
understand
natural language commands and perform tasks on behalf of users. They are fairly
sophisticated, able to handle complex, multistep tasks that require contextual
understanding, and they can learn from their interactions to improve future
performance.
They are used in smartphones, smart home devices, and enterprise software. In
healthcare,
for example, they can engage in natural conversations with patients, collect symptom
information, and assist doctors in making diagnoses. In education, virtual
assistants
can
offer personalized learning experiences and answer student queries. In corporate
settings,
they can manage schedules, set reminders, and control conference systems. Virtual
assistants
can even personalize their interactions, adapting to individual user preferences
over
time. -
Chatbots:
Think of chatbots as virtual assistants’ younger cousins. They are
simpler
AI-based conversational interfaces that can interact with users through text or
voice,
answering easy questions, providing information, and completing tasks. They excel in
scenarios with predictable patterns of customer interaction and repetitive tasks. In
customer service, for example, chatbots handle routine inquiries, such as checking
the
status of an order or explaining the differences among standard service plan
offerings.
In
ecommerce, they can guide customers through the purchasing process and present
personalized
recommendations (that were determined by a separate AI system). Banking chatbots
assist
with
account inquiries and transaction details. Telecommunications companies employ
chatbots
for
troubleshooting common issues, account management, and service inquiries. -
AI agents:
Think of AI agents as the ultimate virtual assistant. But instead
of
responding
to individual commands or prompts, AI agents combine reasoning, memory, and
autonomous
decision-making to take actions that aim to achieve specific objectives with little
or
no
human intervention. Agents can complete multistep processes end-to-end, such as
drafting
and
sending marketing emails, updating a CRM, monitoring email open rates, and refining
future
campaigns. Businesses are embedding AI agents into applications for lead
qualification,
order management, expense approvals, and customer support. As adoption grows, these
systems
are evolving from simple task-handlers into collaborative agents that work together
to
automate increasingly complex workflows.
Generative AI
GenAI, of course, is the groundbreaking subset of AI that can create new, original content
rather than only analyze or act on existing data. Unlike other AI technologies designed for
specific tasks, like image recognition, language translation, or decision-making, GenAI can
produce virtually any kind of text, image, music, speech, or other type of content that is
like what a human might create, and do it all in response to text prompts.
At the heart of GenAI are LLMs and deep-learning algorithms that do the heavy lifting of
enabling machines to understand and generate human-like content. GenAI models are trained on
really large amounts of data so that they can learn the patterns, styles, and structures of
different types of content and then use that knowledge to generate new, coherent outputs
based on user prompts or other input parameters.
Market researchers, including McKinsey & Company, have said that GenAI has the potential
to enhance human productivity to the tune of trillions of dollars per year in the coming
years through enhanced creativity and collaboration. Rather than replacing human workers,
GenAI can serve as a powerful tool to augment and accelerate human capabilities. For
example, writers are using GenAI to brainstorm ideas, overcome writer’s block, and produce
drafts that they can then refine and edit. Designers are using it to create multiple
variations of a design concept, explore new styles, and automate repetitive tasks.
Researchers are employing it to summarize large volumes of text, generate hypotheses, and
identify patterns in complex datasets.
As GenAI technologies continue to advance, they have the capacity to transform the way people
work, learn, and create. At the same time, however, GenAI has raised questions about
intellectual property rights, the role of human creativity, and the potential for misuse.
Agentic AI
Agentic AI is an evolved use of GenAI that has captured the imaginations of businesspeople
since discussion began in early 2024. Multiple research firms have declared agentic AI to be
the #1 strategic technology for 2025. The excitement stems from the fact that, unlike GenAI
tools, which react to human prompts, agentic AI tools can make proactive decisions without
human intervention. For example, a business could, at least theoretically, build an agentic
AI system, tell it a business goal, and let the system independently plan, execute, and
adapt until it achieved that goal.
Since early 2024, many organizations have turned that theory into reality. For example, a
bank studied by McKinsey was in the process of modernizing 400 legacy applications when it
decided to switch from a human developer approach to a multiagent approach, which put the
human developers in supervisory roles. Doing so cut the time and effort for the transition
by 50%. Separately, a market research firm deployed a multiagent approach for its data
quality-control operations and experienced a 60% productivity gain. It expects to save $3
million annually. Though such multiagent approaches are popular, it’s also possible to
achieve quality results by having a single agent review and revise its own work from
multiple perspectives.
AI researcher Andrew Ng is credited with popularizing “agentic AI” as both a concept and its
label through a series of 2024 articles and talks that described its potential. Ng’s
thinking starts with the “zero-shot” nature of conventional GenAI tools, such as chatbots.
By this he means that the user gets one chance to write a prompt that evinces a usable
result from the GenAI tool. To overcome the zero-shot limitation, AI researchers developed
“chain of thought” prompting, which, for example, lets a GenAI tool produce a first result,
then critique it, fix the issues, and only then share the result with the user. Ng’s idea
was to apply a kind of parallel processing, similar to that used when microprocessors are
banded together into a larger computational engine. By having multiple AI agents performing
portions of a task independently and then sharing their results, an agentic AI system can
rapidly produce sufficient high-quality information to make an independent choice.
“Tool use” is another critical aspect of Ng’s agentic AI vision. It’s the idea that an AI
agent can manipulate specialized software tools, which could include other agents, to
achieve specific objectives. A tool might, for example, search the web; another might
operate a computer—a skill that opens up a vast potential for agentic autonomy.
Though the major AI technology companies are racing to simplify agentic systems, they are, at
present, challenging to implement. Current agentic systems may use five or more distinct AI
technologies and an orchestration layer that coordinates the agents’ activities. Plus, they
must be integrated with a business’s existing systems so that they can access necessary
data. Finally, one implementation element that no amount of technology simplification can
eliminate is business process reinvention. To gain the most from agentic systems, businesses
must be willing to rethink their processes as well as the ways that their human employees
interact with AI systems.
AI Use Cases
AI technologies are being applied across a wide range of industries, sometimes simply
enhancing the efficiency of existing operations and other times transforming how businesses
operate and the way they deliver value to customers. Here are some of the most prominent AI
use cases in eight important industries.
-
Retail
Ever since Amazon pioneered the use of AI-powered recommendation engines roughly 20
years ago
to analyze customer data and provide personalized product suggestions, their use has
become
widespread in the retail industry.
The
recommendations that come from AI analysis of customers’ purchase history and
browsing
behavior can not only help increase sales, but customer engagement, too. AI
algorithms can
also predict future demand for products by analyzing historical sales data, weather
patterns, and other relevant factors, helping retailers optimize inventory
management and
reduce waste. Additionally, AI-powered computer-vision technologies enable automated
checkout systems that allow customers to shop without waiting in line
(theoretically), while
also reducing labor costs for retailers. With coming agentic systems, these discrete
functions could evolve into a series of autonomous agents that can manage complete
customer
journeys—from personalized product suggestions to inventory optimization and pricing
adjustments. -
Healthcare
In healthcare, AI systems are powering faster
and more
accurate diagnoses and treatments. AI algorithms can analyze medical images, such as
X-rays
and MRIs, to detect abnormalities and assist clinicians in diagnosing diseases. AI
can also
accelerate new drug discovery by analyzing biomedical data, identifying potential
drug
candidates, and predicting the efficacy and safety of new medications—all of which
should
reduce the time and cost for pharmaceutical companies to bring new drugs to market.
Furthermore, AI can analyze patient data, including genetic information and medical
history,
to develop personalized treatment plans that can lead to improved patient outcomes. -
Finance
In the finance sector, AI-powered systems analyze monetary transactions in real time,
identifying patterns and anomalies that suggest fraudulent activity and, thus,
helping
financial institutions prevent losses and protect customers. Emerging agentic AI
systems in
finance could execute multistep processes, such as loan application reviews,
coordinating
document verification, credit checks, and approval workflows for routine cases. AI
algorithms can also analyze market data, news sentiment, and other factors to make
split-second trading decisions, optimizing portfolio performance and reducing risk.
Likewise, AI is able to analyze alternative data sources, such as social media
activity and
mobile phone usage, to assess credit applicants’ creditworthiness. Again, this
reduces risk
for the lenders, but, in this case, it also broadens accessibility to credit markets
for
people with limited credit histories. -
Logistics
AI is helping the logistics industry improve efficiency and reduce costs. AI
algorithms can
analyze traffic patterns, weather conditions, and other factors to refine delivery
routes,
reducing fuel consumption and improving on-time performance. AI can also analyze
sensor data
from vehicles and equipment to predict when maintenance is needed, reducing downtime
and
extending asset life. Furthermore, AI-powered robotics and computer-vision
technologies can
automate warehouse operations, such as picking and packing, improving efficiency and
accuracy while reducing labor costs. -
Media
Media companies are using AI algorithms to analyze user preferences and engagement
data and
provide personalized content recommendations. This approach has been shown to
increase
customer retention. AI-powered tools are also assisting news organizations in
generating
articles, summaries, and even videos, enabling them to scale content production and
reach
new audiences. As it does for other industries, AI can analyze customer data to
identify
distinct audience segments, which is beneficial for targeted advertising and
personalized
content delivery. -
Cybersecurity
AI is playing an increasingly important role in cybersecurity by enabling faster
threat
detection and response. AI algorithms can analyze network traffic and system logs to
identify potential security threats, so that cyber defenses—whether also automated
or
initiated by human analysts—can respond faster, reducing the risk of a successful
data
breach. They’re especially useful when it comes to so-called zero-day threats,
which, by
definition, have never been seen before. AI can also learn normal user behavior
patterns and
then detect deviations from those norms, which may indicate insider threats or
compromised
accounts. Furthermore, AI can prioritize and automate the deployment of security
patches
based on their vulnerability level and potential impact on the business, reducing
the window
of exposure to the most dangerous cyber threats. -
Manufacturing
For manufacturers, AI is
driving
improvements in efficiency, quality, and productivity. AI agents can analyze sensor
data
from production equipment to predict maintenance needs, automatically schedule
downtime,
order replacement parts, and coordinate technician assignments—handling the entire
workflow
from start to finish. AI-powered computer-vision systems can inspect products for
defects,
improving quality control and reducing the need for manual inspections. AI also can
analyze
historical production data, sales trends, and external factors to predict future
demand for
products, helping manufacturers strengthen production planning and inventory
management. -
Energy
AI is helping the energy sector optimize operations, reduce costs, and improve
sustainability. AI can analyze sensor data from power-generation equipment to
predict when
maintenance is needed, reducing downtime and extending asset life. AI algorithms can
also
analyze historical consumption data, weather patterns, and other relevant factors to
predict
future energy demand, so that utilities can prepare for the necessary power
generation and
distribution. Moreover, AI can analyze real-time data from smart meters and other
Internet
of Things devices to optimize power flow and reduce transmission losses, improving
grid
efficiency, and reliability.
Key Dates in AI Development
Across a half century from the 1950s to the early 2000s, AI made slow and uneven progress.
The AI community often made great promises but was unable to deliver, leading to cynicism
about the technology. And apparent breakthroughs, like human-competitive chess-playing
programs, didn’t generalize well to practical business problems. That said, by 2000 AI had
practical value in a wide range of applications. Mostly, these involved training a computer
program on a fairly narrow task using what is now considered medium-sized data, such as
10,000 to 100,000 examples. This level of AI is built into many products already in
widespread use.
Starting in the early 2000s, one specific approach to AI started to advance rapidly: neural
networks. These are multilayered networks of artificial neurons encoded in software. To
envision them, imagine the familiar spreadsheet but in three dimensions because the
artificial neurons are stacked in layers similar to how real neurons are stacked in the
brain. They also mimic the way that the connections between brain neurons have different
strengths. Neural networks are a very old technology, dating back to the 1950s; during their
rapid progress in the 2000s, some of their practitioners rebranded them as “deep learning.”
Here are the key dates that tell the story of how AI emerged into the world:
- 1943, Artificial neuron: Warren McCulloch and Walter Pitts publish
“A Logical Calculus of the Ideas Immanent in Nervous Activity,” introducing the concept
of artificial neurons. - 1950, Turing Test: In his seminal paper “Computing Machinery and
Intelligence,” Alan Turing proposes “the imitation game,” which later became known as
the Turing Test, as a way to determine whether a machine can think. In the game, a human
tries to distinguish between a computer and a human based solely on their responses to
questions. - 1955, AI coined: The term “artificial intelligence” is first used
in the title of a grant proposal to support the Dartmouth Summer Research Project on
Artificial Intelligence, which took place the following year. The proposal was written
by four people who would become legends in computer science and AI: John McCarthy, then
an assistant professor of mathematics at Dartmouth; Marvin Minsky, a Harvard math
professor; Nathaniel Rochester, head of information research at IBM; and Claude Shannon,
the Bell Labs mathematician who founded information theory. The event marked the birth
of AI as a field of study and came to be known as “the Dartmouth Conference.” - 1958, Perceptron: Cornell Aeronautical Laboratory research
psychologist Frank Rosenblatt develops the Perceptron, an early artificial neural
network capable of learning and recognizing simple patterns. It had a single hidden
layer between its input and output layers. - 1959, MIT AI Project: McCarthy and Minsky, who both moved to the
Massachusetts Institute of Technology the prior year, co-found the MIT Artificial
Intelligence Project. - 1960, LISP: McCarthy publishes his design of LISP in the April
edition of Communications of the ACM (the Association for Computing
Machinery). It quickly becomes the most popular programming language for AI research and
applications. - 1962, Backpropagation: Rosenblatt introduces the concept of
“back-propagating error correction.” Decades later, the concept becomes crucial to ML
and generative AI. - 1963, Stanford Artificial Intelligence Lab (SAIL): McCarthy, who
moved to Stanford University in 1962, founds SAIL; it is still in operation. Today, MIT
and Stanford remain among the top U.S. universities for AI research. - 1966, ELIZA: MIT computer science professor Joseph Weizenbaum
debuts ELIZA, considered to be the first chatbot, which simulates conversations a person
might have with a psychotherapist. Although it sounds impressively human, ELIZA is
entirely rules-based; it parses a user’s input into keywords and then chooses a matching
response from a preprogrammed library. - 1969, Neural nets fall from favor: Minsky, along with Seymour
Papert, both of whom are now co-directors of the MIT Artificial Intelligence Laboratory
(a successor to MIT’s AI “Project”), publish Perceptron. The authors argue
that neural networks like the Perceptron are a dead end and that the future of AI lies
in symbolic systems. This fuels a long-running controversy among AI researchers and
discourages research in neural nets. - 1970, Backpropogation rediscovered: Finnish mathematician and
computer scientist Seppo Linnainmaa reintroduces the AI research community to the idea
of backpropagation, which he describes as the “reverse mode of automatic
differentiation” in his master’s thesis. - 1974, Expert systems emerge: Standford publishes a paper on Mycin,
one of the earliest expert AI systems, which encodes physicians’ knowledge about
antimicrobial therapies and recommends treatment for infectious diseases. - 1978–1986, XCON savings boosts expert systems: XCON (for eXpert
CONfigurer), another early AI expert system, is written in 1978 by Carnegie Mellon
University professor John P. McDermott. Its goal is to help the university configure
Digital Equipment Corporation’s (DEC) VAX computers, which was a tricky challenge in
those days. DEC began using the program internally in 1980; by 1986 it was estimated to
be saving the company $25 million ($72 million in 2024 dollars) annually, mainly from
reduced configuration errors. XCON’s success was one reason expert systems came to
dominate AI research in the 1980s. - 1986, Backpropagation brings back neural nets: The article
“Learning representations by back-propagating errors,” by David Rumelhart, Geoffrey
Hinton, and Ronald Williams, is published in Nature, applying
backpropagation algorithms to multilayer neural networks. Over the next decades, the
combination of backpropagation and neural nets becomes the basis of machine learning,
leading to multiple AI breakthroughs—including GenAI. - 1980s, RNNs: Recurrent neural networks—a neural net architecture
that incorporates feedback mechanisms like those thought to occur in the human
brain—emerge. Though the inherent concepts have been around since the early 1900s, the
1980s brought working RNN models that are still in use. - 1988, Statistical language translation: IBM researchers introduce
statistical machine translation (SMT). Based on information theory and using Bayes’
Theorem, SMT makes a major improvement over the rules-based language translators of the
time. Although it did not use neural nets, SMT’s use of large datasets and probabilistic
algorithms laid a foundation for those techniques to reemerge in modern language
translation systems that are built on neural nets. - 1990s, CNNs: Convolutional neural networks grew out of attempts to
refine RNNs. CNNs specialize in grid-like data and are, therefore, great at spatial data
representations—they can generate pictures. Today’s popular text-to-image GenAI apps use
CNNs as one of multiple neural net models. - 1997, Deep Blue beats Kasparov: IBM’s Deep Blue chess-playing
computer defeats world champion Garry Kasparov in a six-game match, marking a
significant milestone in AI’s ability to compete with humans in complex tasks. - 2005, DARPA Grand Challenge: Stanley, an autonomous vehicle
developed by Stanford University’s Racing Team, wins the DARPA Grand Challenge,
successfully navigating a 132-mile desert course without human assistance. - 2011, Watson wins Jeopardy!: IBM’s Watson defeats two human
champions in a televised game of Jeopardy!. Although Watson did not use deep learning
and neural networks—it had a one-off software architecture specialized for
question-answering—it showcased the power of NLP and led to surging interest in AI. - 2011, Siri: Apple introduces Siri, a virtual assistant that uses
speech recognition and NLP to interact with users and perform simple tasks on iOS
devices. - 2016, AlphaGo: Google DeepMind’s AlphaGo AI system defeats world
champion Lee Sedol 4-1 in a five-game match of Go, demonstrating the potential of deep
learning and reinforcement learning in tackling complex problems. - 2017, The Transformer breakthrough: Transformer, a deep-learning
neural network architecture whose “self-attention” mechanism eliminates the need for
recurrence in neural nets, is introduced in the paper “Attention Is All You Need,”
authored by eight employees and former employees of Google Brain and Google Research.
The transformer breakthrough is that it can process sequential data, such as text, in a
massively parallel fashion without losing its understanding of the meaning in the
sequences. The parallel processing of sequential data revolutionized NLP and powered the
creation of today’s LLMs. - 2018, The GenAI breakthrough: In June 2018, four OpenAI
researchers publish “Improving Language Understanding by Generative Pre-Training,” which
describes how they combined generative pretraining with a transformer model to create
the LLM that is known today as GPT-1. - 2020, GPT-3: OpenAI releases GPT-3, then the largest and most
powerful LLM. - 2022, ChatGPT: In November, OpenAI launches ChatGPT, based on
GPT-3.5. This highly capable conversational AI chatbot captures public attention and
sparks discussions about the potential and implications of advanced LLMs and GenAI. - 2023, GPT-4: OpenAI releases GPT-4 in March, representing a major leap
in reasoning capabilities and the first model to pass challenging professional exams,
such as the bar, at human-expert levels. - 2024, Claude 3 family: Anthropic releases Claude 3 (Opus, Sonnet,
Haiku) in March, with Claude 3 Opus matching or exceeding GPT-4 on most benchmarks. This
begins a period in which new models coming to market consistently leapfrog each other. - 2024, Agentic AI emerges: At his BUILD 2024 keynote in June, Andrew Ng
introduces agentic AI to describe autonomous systems that independently plan and execute
multistep workflows. Ng’s writings and talks give shape and definition to the nascent
field. By year’s end, market researchers declare agentic AI as the top strategic
technology trend for 2025. - 2024, Reasoning models: OpenAI releases its o1 reasoning model in
September, followed by o1-pro in December. These models use extended “thinking time”
before responding—sometimes reasoning for minutes rather than seconds—achieving
PhD-level performance on physics, chemistry, and math benchmarks. This is only the first
of what becomes a fundamental architectural shift toward deliberate reasoning. - 2024, Claude computer use: In October, Anthropic releases Claude’s
computer use capability in public beta. The system can control desktop computers—moving
cursors, clicking buttons, typing text—to complete complex tasks across multiple
applications autonomously, marking a major step toward AI agents that interact with
software like humans do. - 2025, Claude Sonnet 4.5: Anthropic releases Claude Sonnet 4.5 in
September, advancing coding capabilities and multistep reasoning. The model demonstrates
significant improvements in computer use tasks and becomes widely adopted for agentic
applications.
The Future of AI
AI technologies have been developing at a breathtakingly fast pace—partly because they can
contribute to their own development—and are likely to continue doing so for the foreseeable
future. The trajectory of that development appears to be moving toward increasingly
autonomous, specialized, and deeply integrated AI systems. For businesses, planning for AI
is becoming a strong strategic imperative. But it’s not just about adoption. It’s about
thoughtfully deploying AI by redesigning business functions and operations around AI
capabilities.
In the next year or so, businesses’ main activity will likely center on transitioning AI
experiments and pilots to full production systems. As always in such technology transitions,
a percentage won’t succeed. In the case of agentic AI, for example, the complexity required
to coordinate multiple specialized agents, provide consistent performance, integrate with
legacy systems, and maintain appropriate human oversight is likely to exceed many
organizations’ expectations. Organizations that succeed will be those that approach AI
deployment with realistic timelines, strong governance frameworks, and a willingness to
iterate based on real-world performance.
During that same time frame, reasoning models that deliberate before responding—sometimes
“thinking” for minutes to solve complex problems—will likely become mainstream,
complementing the more familiar quick-response models. Context windows will continue
expanding from thousands to hundreds of thousands of tokens, enabling AI to analyze entire
documents, codebases, or datasets in a single interaction. Perhaps most significantly, AI
systems will begin routinely controlling desktop computers directly, manipulating software
interfaces the way humans do rather than requiring custom integrations for every
application. Anthropic already has such a capability in beta test.
For SMBs, this period will bring great opportunity. Cloud-based platforms from major vendors
will begin to make enterprise-grade AI accessible at SMB price points, and the ecosystem of
prebuilt agents and templates will help reduce SMB implementation barriers.
By the late 2020s, the AI landscape may be characterized by a spectrum of specialized
solutions rather than general-purpose tools, much like today’s enterprise software market.
Organizations will assemble teams of AI agents from marketplaces and vendor catalogs,
selecting specialized capabilities for legal research, financial analysis, customer service,
software development, supply chain optimization, and dozens of other functions. Just as
businesses today license different SaaS applications for different needs, they’ll deploy
different AI agents with distinct expertise.
During this period, the most successful organizations will likely be those that redesign
their workflows from scratch around AI capabilities, rather than merely automating existing
processes. New professional roles may emerge and proliferate. Think: Agent managers who
supervise squads of AI systems rather than human teams, focusing on goal-setting,
performance monitoring, and exception handling; AI integration specialists who design the
workflows that coordinate multiple agents toward business objectives; and ethics and
compliance officers who confirm that agent behavior aligns with company values and evolving
regulations.
Looking toward the 2030s, AI capabilities that seem remarkable today will be commonplace. AI
embodied in robotics could finally achieve the reliability and cost-effectiveness needed for
widespread deployment in physical industries like manufacturing, logistics, and agriculture.
Entirely new AI architectures beyond today’s transformer-based models may emerge,
potentially offering capabilities we can barely conceptualize today. What seems certain is
that the AI systems of the 2030s will be substantially more capable than today’s
technology—able to handle increasingly complex reasoning, operate autonomously across longer
time horizons, and collaborate with humans in ways that feel more like partnerships than
tool use.
The 2030s will bring a much higher bar for rate of progress—organizations that fall behind
the progress curve will go out of business much faster.
The Future of AI’s Implications for Business Leaders
For business decision-makers, several strategic principles emerge from this future trajectory
of AI:
- Speed matters, but thoughtfulness matters more: Organizations rushing
to deploy AI without clear use cases, governance frameworks, or change management processes will likely
join the ranks with canceled projects. Those that move deliberately—identifying
high-value applications, establishing baseline metrics, and building organizational
capability—will capture sustainable advantages. - Process redesign trumps automation: The greatest returns come from
reimagining workflows around AI capabilities rather than using AI to speed up existing
processes. - Human-AI collaboration is the endgame: Despite headlines about AI
replacing jobs, the most successful implementations amplify human capabilities rather
than eliminate human involvement. Workers become managers of AI systems, curators of
outputs, handlers of exceptions, and providers of the judgment and creativity that
remain distinctly human. - Data infrastructure is foundational: Organizations with clean,
well-organized, accessible data will extract far more value from AI than those with
fragmented, siloed, or low-quality information.
The future of AI isn’t about machines replacing humans: It’s about fundamentally new ways of
working where human intelligence and AI combine to solve problems neither could address
alone. Organizations that embrace this collaborative model—investing in both technology and
the human capabilities to leverage it—will be best positioned to thrive in the decades
ahead.
Exceed Your Productivity Goals With NetSuite AI
Businesses navigating the rapidly evolving landscape of AI can consider NetSuite’s
cloud-based enterprise resource planning (ERP)
system, which offers a powerful set of AI capabilities that can help organizations
of all sizes boost their productivity and gain a competitive edge. NetSuite’s embedded AI capabilities can
help businesses automate a wide variety of repetitive tasks and gain more valuable insights
from their data than previously possible, which equips business managers to make
better-informed decisions. NetSuite does this by adding AI functions to its proven,
user-friendly ERP with a unified central database.
NetSuite’s AI capabilities span business functions from invoice processing to financial
management. For example, NetSuite’s intelligent financial management tools can scan invoices
using AI-based object and character recognition, automatically categorize expenses,
continuously analyze financial data to detect anomalies and recommend next steps, and
provide predictive insights into cash flow and budgeting. NetSuite’s AI-powered demand
forecasting and inventory management features can help businesses optimize their stock
levels, reduce waste, and improve order fulfillment. By integrating these AI capabilities
into a comprehensive ERP solution, NetSuite enables businesses to streamline their
operations, enhance their agility, and unlock new opportunities for innovation and growth,
all while keeping pace with the rapid advancements in AI technology.
AI is a transformative technology that is rapidly reshaping the business landscape. AI’s
ability to analyze data, automate processes, and generate insights can help businesses
improve efficiency and drive innovation and growth. As AI rapidly evolves, it’s essential
for organizations to stay up-to-date about its potential benefits—and challenges—so they can
develop strategies to integrate AI into their planning and operations.
Artificial Intelligence FAQs
What is AI mainly used for?
AI is mainly used for automating tasks, analyzing data, and making predictions to help
businesses and individuals make better decisions and solve complex problems.
What is the purpose of AI?
AI research and products aim to create intelligent machines that can perform tasks that
typically require human intelligence, such as learning, problem-solving, and
decision-making.
Where is AI used today?
AI is used in virtually all industries, including healthcare (diagnosis and drug discovery),
finance (fraud detection and algorithmic trading), transportation (autonomous vehicles),
manufacturing (predictive maintenance), customer service (chatbots), and more.
Can AI replace humans?
While AI can automate many tasks and augment human capabilities, it is unlikely to replace
humans. AI is best suited for specific, well-defined tasks, while humans excel at
creativity, empathy, and general intelligence. The most successful applications of AI are
likely to involve collaboration between humans and machines.
What are people using AI for?
People are using AI for a wide range of purposes. These include automating repetitive tasks
and processes, analyzing large volumes of data to identify patterns and insights, making
predictions and forecasts based on historical data, personalizing experiences and
recommendations for customers, improving decision-making and problem-solving, enhancing
creativity, generating new ideas in fields like art and design, augmenting human
capabilities, and improving workplace productivity.
How is AI being used by businesses?
Business use of AI is limited only by imagination. In operations, AI automates repetitive
tasks, such as invoice processing, inventory management, and customer support. In marketing
and sales, algorithms analyze customer data to predict demand, personalize recommendations,
and optimize campaigns. Finance teams use AI for fraud detection, forecasting, and anomaly
spotting, while manufacturers apply it to predictive maintenance and quality control. AI
agents have become able to handle multistep workflows—from onboarding employees to managing
orders—largely autonomously. By combining automation with data-driven insight, AI is helping
organizations of all sizes reduce costs, innovate faster, and deliver better customer
experiences.
What percentage of businesses use AI?
Nearly all businesses use AI in one form or another. For example, Verizon’s “2025 Mobile
Security Index” survey found that 93% of 762 responding organizations worldwide say their
employees are using generative AI tools on their mobile devices.
