Top 10 AI and Data Science Trends to Watch in 2026
Artificial intelligence and data science are transforming industries and society at a breathtaking pace. Each year ushers in exciting new breakthroughs, tools, and applications that expand the realm of what‘s possible. As we look ahead to 2022, several key trends promise to define the AI and data landscape.
From the continued dominance of cloud giants to the democratization of AI development, increasing regulatory oversight, the quest for actionable insights, and more powerful language models, here are the top 10 trends that data science and AI professionals should have on their radar.
1. The Cloud Wars Heat Up
Cloud computing giants Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) are engaged in an intensifying battle to become the go-to provider of AI and data science solutions. The cloud‘s scalable storage and compute power are essential for building and deploying data-intensive AI workloads.
In 2022, expect these megavendors to release a steady drumbeat of new AI services and expand their partner ecosystems as they vie for market share. AWS‘s SageMaker platform, Azure‘s Cognitive Services, and GCP‘s Vertex AI will continue to add features that abstract away complexity for developers. At the same time, the risk of cloud lock-in will compel many organizations to pursue a multi-cloud strategy.
As IDC forecasts, worldwide spending on artificial intelligence, including software, hardware, and services, is expected to break the $500 billion mark in 2023, up from $341.8 billion in 2021. The cloud providers are angling to capture an outsized portion of this massive opportunity.
2. The Rise of the Citizen Data Scientist
Not long ago, AI development was the exclusive domain of experts with PhDs in computer science and statistics. But a new category of tools is putting the power of AI into the hands of non-specialists.
Low-code and no-code platforms like Google‘s AutoML, Microsoft‘s Azure ML, and startups such as Alteryx and DataRobot are using AI to help business users build AI models without needing to code. By automating tasks like data preprocessing, feature engineering, and model selection, these tools significantly lower the barrier to entry.
Gartner predicts that 70% of new applications developed by enterprises will use low-code or no-code technologies by 2025, up from less than 25% in 2020. The shortage of data science talent is a key driver of adoption, as organizations seek to enable business analysts and domain experts to extract value from data.
However, the goal is to augment rather than replace professional data scientists, who will still be needed for more complex and mission-critical use cases. Automated machine learning (AutoML) frameworks like Auto-Keras, Auto-PyTorch, and Auto-Sklearn are also gaining traction, enabling data scientists to be more productive by automating repetitive modeling tasks.
3. AI Gets Regulated
As AI systems become more pervasive and consequential in our lives, from screening job applicants to informing bail decisions, governments are stepping up efforts to regulate the technology. The aims are to protect citizens‘ rights, ensure fairness and transparency, and hold companies accountable for responsible AI development.
In 2022, we can expect to see more AI-specific laws and enforcement actions, particularly in the European Union which is taking a more assertive stance relative to the U.S. and China. Under consideration are rules requiring companies to assess AI systems for risks, conduct audits, provide explanations for automated decisions, and obtain prior authorization for high-risk applications like facial recognition.
Existing laws like GDPR are also being applied to curb AI harms related to privacy and discrimination. Self-regulation efforts by industry will need to adapt to the fast-evolving patchwork of regional AI regulations. Compliance will add costs and complexity, but proponents argue it‘s essential to preserving trust as AI becomes ubiquitous.
4. From Big Data to Big Insights
Having amassed massive troves of data, enterprises are shifting their focus from collection to extracting tangible value in the form of insights that enhance decision making. No longer satisfied with merely describing what happened in the past, business leaders want predictive and prescriptive analytics powered by AI.
The emphasis is increasingly on "decision intelligence" – timely, contextual insights seamlessly embedded into operational workflows. To deliver those high-impact insights, data science teams are doubling down on a few key priorities:
- Improving data literacy across the organization
- Collaborating closely with domain experts
- Ensuring a reliable data pipeline and strong DataOps practices
- Leveraging AI to automatically generate relevant features and models
- Focusing on interpretable, causal modeling techniques
- Designing compelling visualizations and narratives
A famous tech truism states that "data is the new oil." But like oil, data needs to be extracted, refined, and piped to where it can fuel productive activities. In 2022, data science will be in the insights business.
5. Augmented Analytics Goes Mainstream
With the deluge of data facing most organizations, deriving insights fast enough to act on them is a growing challenge. Augmented analytics promises to compress the insight discovery process from weeks or months down to minutes.
By leveraging AI and machine learning techniques, augmented analytics platforms can automatically prepare data, generate features, select algorithms, build and deploy models, and produce visualizations and natural language explanations. This dramatically reduces manual effort and enables insights to be surfaced in near real-time.
Startups like Tellius and Sisu, as well as incumbents like Salesforce (Tableau CRM), SAP, and Oracle are battling to bring augmented analytics capabilities to the masses. By 2025, Gartner projects that augmented consumerization functionality will drive adoption of analytics and business intelligence capabilities beyond 50% for the first time, up from 35% today.
As augmented analytics matures, it won‘t replace human analysts but rather supercharge their productivity, creativity and impact. Automating rote tasks will free up data professionals to focus on higher-order sense making, story telling, and change management.
6. Edge AI Accelerates
With billions of IoT devices generating data, sending it all to the cloud for processing has downsides in terms of cost, latency, privacy and connectivity. The alternative is to push AI to the "edge" – enabling algorithms to run locally on devices.
Edge AI has the advantage of real-time responsiveness, making it ideal for applications like autonomous vehicles, robotics, and smart factories. Since data can be processed on-device, it reduces bandwidth costs and addresses data sovereignty concerns. And edge deployments can keep functioning when connectivity is spotty.
In 2022, edge AI will gain momentum thanks to more powerful edge computing hardware and lightweight software stacks optimized for on-device machine learning. The major cloud providers are extending their AI platforms to the edge, as are chipmakers like Intel and NVIDIA. Venture funding is also flowing to edge AI startups.
However, edge AI still has limitations compared with cloud AI in terms of compute power, storage, and flexibility. Many applications will thus adopt a hybrid architecture leveraging both edge and cloud AI capabilities as appropriate.
7. Language Models Get Supersized
Since their introduction in 2017, transformer-based language models have achieved remarkable progress in tasks like translation, summarization, question-answering and generation. GPT-3, released by OpenAI in 2020, set a new standard with 175 billion parameters.
In 2022, the race to build ever-larger language models trained on more diverse datasets will continue. DeepMind, Microsoft and the Chinese tech giants are expected to counter with their own massive models. As language models become more knowledgeable and coherent, a wider range of practical applications will emerge.
Pre-trained models will be fine-tuned for domain-specific use cases like medical diagnosis, legal contract analysis, and educational content generation. Enterprises will also increasingly adopt large language models to power chatbots, knowledge management, content moderation, and semantic search.
However, challenges remain in understanding exactly what these black-box models have learned and mitigating issues of bias, toxicity and factual inaccuracy. Techniques to make language models more interpretable, controllable and aligned with human values will thus be a key focus of research.
8. Automating Feature Engineering and Selection
Building effective machine learning models hinges on having high-quality input features that capture the salient aspects of the data. But feature engineering – the process of manually constructing relevant variables and representations – is time-consuming and prone to human biases.
In 2022, we‘ll see increased adoption of automated feature engineering and selection techniques powered by AI. Rather than relying solely on domain expertise to handcraft features, AutoML tools will intelligently generate, combine and winnow down features.
Deep learning‘s ability to automatically learn abstract feature representations will be complemented by classical statistical approaches like hypothesis testing and penalized regression. Evolutionary algorithms will be used to efficiently search the feature space and identify optimal subsets. Reinforcement learning will enable iterative feature discovery guided by model performance.
Automating the feature engineering process allows data scientists to evaluate a wider range of candidate features and arrive at more predictive models faster. It also makes models more adaptive to changing data distributions. While domain knowledge remains critical, AI assistance can enhance the art of feature creation.
9. Decentralizing Data Science with Blockchain
Today, most enterprise data is siloed within a single organization‘s systems. Sharing data across companies is cumbersome due to competitive, regulatory and technical hurdles. But many data science problems, from medical research to supply chain optimization, could benefit from secure cross-organizational data pooling and analysis.
In 2022, blockchain technology will emerge as a tool to facilitate decentralized data science. By leveraging the trust and transparency of distributed ledgers, companies can create data consortia to break down silos and collaborate on shared challenges without ceding control.
Blockchain-based platforms like Ocean Protocol, Synapse AI, and SingularityNET are enabling data owners to monetize their assets without sacrificing privacy. They provide mechanisms for data discovery, licensing, and computation that give data scientists permissioned access while ensuring strict usage controls.
Decentralized data science ecosystems have the potential to accelerate innovation by allowing AI models to be trained on larger, more diverse datasets that are impractical for any single organization to assemble. They can also make data pipelines more resilient by eliminating single points of failure. But significant challenges remain around scalability, confidentiality, and governance before enterprises will entrust sensitive data to blockchain networks.
10. The Talent War Escalates
Demand for AI and data science skills continues to outpace supply. McKinsey Global Institute projects that the number of AI/ML jobs will increase by 39% by 2030. But 51% of organizations report significant data science skill gaps, and universities are not graduating enough students with the requisite expertise.
In 2022, the war for data and AI talent will intensify. Tech giants will dangle ever-higher salaries and bonuses for top performers, while non-tech enterprises will struggle to compete for scarce skills. Diversity and inclusion will be critical to expanding the talent pool, requiring organizations to cast a wider net and support employees from underrepresented backgrounds.
At the same time, there will be a heightened focus on upskilling and reskilling the existing workforce. With coding bootcamps, online courses, and certifications, a growing number of individuals are switching into data and AI careers from other fields. Companies will need to invest in continuous learning opportunities to keep their teams on the cutting edge.
The Future of Work will be defined by human-machine collaboration. As AI augments human intelligence, data literacy and AI proficiency will become essential skills across all job functions. Domain experts who can work effectively with algorithms to solve real-world problems will be in high demand. Managing the organizational and societal impacts of these workforce shifts will be a key priority for leaders.
Conclusion
AI and data science are poised for explosive growth and impact in 2022. As we‘ve explored, the trends driving progress include the maturation of cloud AI platforms, the consumerization of AI development, the imperative for responsible AI governance, the pursuit of timely and actionable insights, the rise of augmented analytics, the acceleration of edge intelligence, larger language models, automated feature engineering, decentralized data sharing, and the escalating battle for talent.
While the pace of technological change can seem dizzying, these developments share a common thread – the increasing democratization and diffusion of AI and data science capabilities. What was once the province of a highly specialized elite is rapidly becoming accessible to developers, analysts, and business users across the enterprise. As barriers to adoption fall and new tools emerge to augment human expertise, we can expect to see AI-powered insights and efficiencies proliferate throughout the economy.
But with great power comes great responsibility. As AI systems exert more influence over high-stakes decisions, it‘s crucial that we imbue them with human values and oversight. Only by proactively addressing issues of fairness, transparency, privacy, and security can we ensure that AI remains a positive force for social good. The coming year will be pivotal in shaping the norms and governance structures surrounding this transformative technology.
One thing is certain – data science and AI will be at the forefront of solving the world‘s toughest challenges, from climate change to public health to education. There‘s never been a more exciting time to be part of this dynamic and purpose-driven field. Those who invest in the right tools, talent and culture to harness the power of data will be positioned to drive innovation and thrive in the intelligence era. The future belongs to the data-driven.
