It’s impossible to overestimate the importance of incorporating artificial intelligence to the corporate world. Computational technology, in particular artificial intelligence and its various subsets, is being actively applied across industries, from government and military, to healthcare, finance, education, and entertainment.
In 2020, as reported by McKinsey, most organizations adopted AI-solutions in at least one business function. These numbers are forecasted to increase in the coming years, sensibly popularizing advanced technologies among companies.
- About 37% of businesses have already implemented AI
- Approximately 90% of business have invested in AI
- The sector will hit $500 billion by 2024
- The industry might reach over astonishing $15 trillion by 2030
Considering the positive dynamics, we expect business leaders to continue to implement artificial intelligence. But however, despite the great potential and opportunities, artificial intelligence comes with various problems in terms of integration.
So, what should one consider before going with the trend?
What is artificial intelligence?
In brief, artificial intelligence is a complex field, which combines computer science along with other subfields (machine and deep learning, natural language processing, artificial neural networks) to perform specific tasks:
Computer intelligence is utilized to interpret enormous datasets in order to automate information gathering, processing, categorizing, and more.
At the very moment, both startups and organizations are using AI-based solutions to handle daily processes. International corporations to mention include IBM, Google, Microsoft, Amazon, Apple, and Meta.
Where do we use artificial intelligence?
Artificial intelligence is rapidly taking over most verticals:
- Healthcare & pharmaceuticals
- Retail & distribution
- Transportation & logistics
- Safety & surveillance
And these are only the largest to mention.
Advanced technology radically changes every aspect of the human life.
AI-based services are already significantly fueling the next digital revolution blasting across different industries. AI-driven applications include cybersecurity, business process automation (BPA), Internet of Things (IoT), intelligent virtual assistants (IVA), Cognitive Computing, Big Data, and other.
Artificial intelligence in the IT world
From coding to deployment, artificial intelligence is transforming the ways software development is handled. Mathematical algorithms significantly accelerate everyday workflows providing for smarter resource-allocation, faster time-to-market, and streamlined error management.
Computer intelligence automates routines, which require human intelligence, like reasoning and generalizing. Computational technology might improve business analysis, project planning, programming, implementation, quality assurance, and enhance user experience.
- Precise estimates
- Speed and scale optimization
- Better decision-making
- Issue identification and resolving
What’s more, AI-based solutions are transforming nor only software development, but various other sectors. Whether it’s data transferring and updates, marketing analysis, trend forecasting, fraud detection, or chatbots, over time we will become even more dependent on those AI-driven tools.
AI development: Key value for businesses
It is quite common that the software development significantly overflows pre-defined budget and deadlines. Machine learning helps obtain accurate estimates by processing historical datasets for the software developers to get context understanding.
Converting gathered business requirements into on-demand tech solutions requires sensible human resources. By applying machine learning, technical specialists might enable non-technical professionals to transform project requirements into thought-out project plans, reducing expenses and time-to-market.
Proofreading documentation and debugging is an intense component of a software developers’ daily routine. With the help of machine learning, software developers can get relevant recommendations, code examples, and templates, therefore boosting overall productivity.
Malfunction determination, approach and strategy shift, and further risk elimination can be quite challenging. By applying machine learning, business leaders can get more insight and re-consider operational processes across departments.
AI development: Core challenges to consider
Dataset quality and availability are necessities when speaking about gaining valuable information and insights. Dataset determination is critical to power data-based decision-making, which improves customer experience, brand loyalty, supply flows, and more.
Data quality and availability requires thought-out dataset selection with focus on identifying trusted sources. Even perfectly trained algorithms can’t segregate inappropriate or inaccurate information, which brings us to the conclusion that the initial stages are determining the success of further data management.
Artificial intelligence is dependent on previously provided datasets, which determine successive conclusions. Entirely relying on defined training datasets, mathematical algorithms observe models, which are then applied to people and objects.
The main issue with artificial intelligence being used for decision-making is that provided information is biased. For example, using complex math algorithms to automate risk assessment in the legal system might generate incorrect conclusions associated with common prejudices against certain social groups.
The required computing power is one of the main factors keeping companies from adopting AI-based solutions. They demand increasing numbers of cores and graphics processing units, and not every company can afford such resources.
To utilize AI-driven tools, one requires a supercomputer’s computing power, and supercomputers aren’t cheap. And given the inflow of unprecedented data amounts, even utilizing cloud computing is not always helpful.
Artificial intelligence is an efficient alternative to most traditional systems and can be applied across industries. But apart from inspired technology enthusiasts, college students, and researchers, there aren’t many people truly aware of the great potential machine intelligence brings to the table.
There are small and medium enterprises, which might sensibly benefit from implementing artificial intelligence, for example, by scheduling their work, managing resources, analyzing trends, forecasting demand, and more. But they aren’t aware of popular service providers like Amazon Web Services, Google Cloud, Microsoft Azure, and other useful opportunities in the tech domain.
Data privacy and security
Computer algorithms perform countless complex operations every day, collecting and processing information. The information, being later gathered and automatically categorized by these complex algorithms, is generated by people habitually surfing the Internet, transferring money, making appointments, and more.
The problem about handling large datasets is that it creates potential threats for everyone sharing information. For example, without appropriate cyber protection, medical institutions might lose personal and medical data to criminals who can take advantage of the stolen information.
With mature international organizations, including IBM, Google, Apple, and Meta, being accused of unethical data exploitation, some countries actively implement stringent limitations associated with data dissemination. In consequence, many companies are facing the problem of finding data sources, which causes algorithm bias.
One solution that’s gaining great popularity is the audacious concept of generating synthetic data:
- Synthetical data isn’t derived from events, so it doesn’t violate ethical rules
- Synthetical data simply matches example datasets by extracting necessary properties from the primary datasets
- Artificial datasets enhance robustness of the AI model
- Artificial datasets ensure privacy and security
Strategic leaders might enjoy improved tracing, higher sales, lower risks, and more, by adopting AI solutions. But these business benefits can be only accessed through implementing suitable infrastructure.
Replacing outdated legacy systems is expensive, that’s why many leaders reject implementing AI tools at all. Despite the numerous advantages, most still keep relying on their traditional infrastructure, applications, tools, and devices.
Strategic companies are seeing artificial intelligence as a promising opportunity to power digital reformation. Yet still, many projects keep failing due to the challenges around operationalizing.
The reasons why scaling artificial intelligence is troublesome fall under four topics:
Your team must customize the selected AI model to suit industry-specific needs, collected data, and budget. What’s more, the trained AI model must be further optimized to align with key performance indicators.
Heterogeneous and ambiguous datasets must be properly collected, processed, categorized, and managed. Even more, smaller and noisier datasets must be accurately processed by implementing advanced techniques to eliminate the blockage to getting pilot products to production.
Most startups and enterprises starting their ambitious journeys of adopting computer intelligence, face issues finding professionals with required statistical knowledge, practical experience, and necessary technical skills. Strategic-thinking organizations have brought a holistic ecosystem approach to finding suitable candidates enjoying augmentation, which brings faster pilot-to-production, and more.
Technical and non-technical professionals are worried their specialties might become completely irrelevant. That means, emphasizing tight human-machine collaboration is foundational to save more jobs and improve public opinion.
Why implement AI-driven solutions?
For entrepreneurs that consider digital transformation, investment relevance and return are determinative. Responsible decision-makers need numbers.
The reasons for approaching digital transformation include resource-allocation (time, cost) and productivity. And under the condition of overcoming potential obstacles, these factors significantly drive expeditious growth and profit.
- Automate processes of reconciliation or verification of compliance with standards
- Optimize workloads
- Minimize potential human error
- Maximize productivity and efficiency by improving daily routines and analyzing customer satisfaction
AI-strengthened solutions can have the form of diverse smart tools:
- Spam filters
- Analysis programs for monitoring customer feedback, trends, demand, and more
- Intelligent chatbots
- Virtual assistants
Artificial intelligence solves numerous pressing problems, including planning, production issues, and delivery. Computational advances significantly optimize document processing, documentation maintenance, and other operational routines.
Machine intelligence, in particular machine and deep learning, also accelerates insight-driven decision-making. By processing large datasets and increasing overall accuracy, smart tools help form efficient strategies.
What does it take?
To design and deploy AI-based software, IT teams require datasets — in brief, training data for implementation. If there are no existing datasets, the involved IT team must allocate resources to gather appropriate data.
At the first stages, software developers:
- Choose a suitable algorithm
- Choose the learning type (supervised, unsupervised, or reinforcement)
- Ensure the training dataset is clear, adequate, organized, and consistent
- Place labels
After this, the team can proceed with determining:
- Learning platform
- Programming language
But what peculiar features should be necessarily considered at the next stages?
There are many platforms, which provide software developers with easy-to-navigate, comprehensive tools. They combine mathematical algorithms simplifying decision-making, supportive documentation, and data.
Software engineers commonly prefer:
- Amazon Web Services (AWS)
Amazon Web Services (AWS) help professionals in creating, training, testing, and deploying complex models. The products include tools for computing, storage, databases, networking & delivery, analytics, and security.
- Google Cloud
Google Cloud provides specialists with resources and tools for creating and implementing AI-based products. It’s a code-based environment, assisting engineers with projects from initial idea evaluation to launch.
- Microsoft Azure
Microsoft Azure provides specialists with applications and agents, knowledge mining, and other handy tools. The platform assists with pattern identification, sentiment analysis, and key phrases extraction.
Software developers commonly use:
This language smoothly integrates with various data structures, provides unique mathematical algorithms beyond standard programming practices, and allows expanding knowledge and experience through libraries like Scikit and Pandas.
This language helps approach exception handling, multi-threaded applications, and other relevant problems like arrays, lists, structures, and more.
This language allows implementing complex logic simultaneously preserving expeditious performance; software developers turn to this language for applications including animation.
APIs and frameworks
Evans Data’ provides insights on the artificial intelligence and machine learning development’ core challenges. Their study sheds light on modern-day AI and ML development, covering demographics, business decisions, domains, practices, frameworks, models, accelerators, containerization, hardware optimization, cloud services, platform adoption, tools and technology adoption, and security.
The 2022 artificial intelligence and machine learning survey highlights the following findings:
- Over 55% of developers rely on language and speech APIs
According to the study, conversational assistants have now become part of mainstream software development. This means, software engineers are using:
- Entity analysis to label specific information within documents (emails, chats, social media)
- Sentiment analysis to process gathered information and provide valuable insights
- Almost 40% of developers state that operational management is the core challenge when implementing artificial intelligence
- The second biggest challenge is building AI products portable across deployment environments
- The third greatest challenge is choosing the right AI framework
AI frameworks are libraries that provide building blocks to architect, train, validate, and deploy AI models. These include Scikit Learn, TensorFlow, Theano, Caffe, Keras, MxNet, PyTorch, and other AI frameworks, commonly utilized to build AI-based applications.
Artificial intelligence in the coming years
In the past times, we implemented traditional approaches to trace emerging trends and predict rising demand. But today, by leveraging computational technology, we can unprecedentedly transform customary approaches to deliver innovative solutions.
With corporations like IBM, Google, Microsoft, Amazon, Apple, and Meta investing millions to implement modern advances, in particular artificial intelligence and its various subsets, big things are going to happen. Despite the potential obstacles keeping companies from adopting artificial intelligence in their daily processes, we see great opportunities in the coming days.
We foresee the popularization of several promising trends:
- AI democratization enabling corporations to eliminate the challenges of today’s talent shortage
- AI creativity producing music, images, videos, and even complex code
- Ethical and explainable technology making information more accessible
- Augmented collaboration making people work alongside with machines
- Sustainable AI making businesses reduce their carbon footprints
- Surveillance AI using advanced biometric authentication detecting threads
In particular, an increased everyday adoption of modern-day intelligent assistants in the following domains:
- Healthcare (imaging, diagnostics, medical research, medical training, hospital care, public health)
- Retail (in-store assistance, cahier-less checkout, price adjustment and prediction, visual search)
- Finance (risk assessment and management, fraud detection, credit decisions, personalized banking)
- Education (intelligent tutoring, virtual classrooms, voice assistance, smart content)
No doubt, modern technology radically changes traditional programming, used approaches, and techniques. But still, today’s innovations cannot replace conventional programming, as it can’t provide valuable outcomes without thorough human surveillance.
Critical components (data management, product interfaces, and security) will still rely on common software. But all the same, regular software can be sensibly improved in terms of planning, coding, testing, and delivery by applying advanced techniques.
By leveraging AI capabilities, we can sensibly streamline various processes:
- Workflow planning
- Performance monitoring
- Human resources
- Marketing campaigns
- Sales optimization
- Accounting automation
- Fraud detection
- Risk management
- Customer behavior tracking
- Customer response prediction
And there are multiple AI subsets that bring valuable opportunities to businesses across industries:
- Predictive analytics (custom models for efficient pricing strategies, churn prediction, fraud detection)
- Computer vision (image and video recognition)
- Data science (data mining and visualization, probabilistic programming, and more)
- Data capture & extraction (document processing for reduced human error)
Artificial intelligence is not an outlying futuristic concept, but rather something already noticeably present across different major industries, including government and military, public health, and other large domains. We are already implementing smart assistance in our everyday lives and business, whether society realizes this or not.
The world relentlessly revolutionizes many sectors by applying artificial intelligence, but the ways innovations are created and implemented must be better understood to resolve potential challenges mentioned above. Smart machines are still perceived controversial when speaking about politics, global economy, and society, but bring endless potential.
What is to expect in the near future if we continue investigating and cultivating computational technologies? We believe the breadth of all the possibilities won’t be revealed soon, but we are impatiently looking forward to seeing advanced technology evolve further.
Sure thing, there are major implications, including policies, investment troubles, ethical conflicts, and more. But with the right transition approach and emphasizing on transparency, artificial intelligence might become the most influential invention in history.