Top AI Technologies and Market Trends: Adoption Statistics, Vendor Landscape, and Implementation Barriers

Artificial intelligence (AI) is described as a liberating technology that, when integrated into enterprises, can boost innovation, creativity, and adaptability, although the field is still in its early stages.

AI startups are booming, attracting media attention and acquisitions, and corporate AI investment has risen sharply; a Narrative Science survey shows AI usage grew from 38% of enterprises in 2017 to 62% in 2018.

The term AI encompasses machine learning, deep learning, recommendation engines, predictive and prescriptive analytics, communication, and speech‑recognition solutions. Key findings from the survey include:

26% of respondents use AI to automate manual repetitive tasks (up from 15% in 2015).

20% have not adopted AI due to unclear value.

58% employ AI for predictive analytics.

25% use AI for automated written reports.

25% use AI for speech recognition and response.

38% view AI‑driven forecasting of machine, customer, or business health as the top benefit.

27% see automation of manual, repetitive tasks as the top benefit.

95% claim proficiency in using big data for business insights, and also use AI (up from 59% in 2015).

61% of innovative enterprises apply AI to data analysis for process improvement or new revenue streams.

Forrester predicts AI investment will increase by more than 300% in 2017 compared with 2016, while IDC forecasts the AI market to grow from $8 billion in 2016 to over $47 billion by 2020.

Forrester’s TechRadar report identifies the ten hottest AI technologies that enterprises should consider:

Natural Language Generation : automatically creates text from data; vendors include Attivio, Automated Insights, Narrative Science, SAS, etc.

Speech Recognition : transcribes human speech for computer use; vendors include NICE, Nuance, OpenText, Verint.

Virtual Agents : chatbots to advanced conversational systems; vendors include Amazon, Apple, Google, IBM, Microsoft, etc.

Machine Learning Platforms : provide algorithms, APIs, toolkits, and compute resources for predictive/analytical applications; vendors include Amazon, Google, H2O.ai, Microsoft, SAS.

AI‑Optimized Hardware : GPUs and devices designed for AI workloads; vendors include Nvidia, Intel, IBM, Cray.

Decision Management : embed rules and logic into AI for assisted decision‑making; vendors include Pegasystems, UiPath, Informatica.

Deep Learning Platforms : neural‑network‑based machine learning for large‑scale pattern recognition; vendors include Deep Instinct, MathWorks, Sentient Technologies.

Biometrics : image, touch, voice, and gesture recognition for natural interaction; vendors include Affectiva, FaceFirst, 3VR.

Robotic Process Automation (RPA) : script‑based automation of manual tasks; vendors include UiPath, Automation Anywhere, Blue Prism.

Text Analytics & NLP : statistical and machine‑learning methods to understand sentence structure, meaning, sentiment, and intent; vendors include Basis Technology, Lexalytics, Sinequa.

Despite the commercial benefits, many enterprises encounter barriers to AI adoption, such as lack of clear business cases (42%), uncertainty about AI capabilities (39%), insufficient AI skills (33%), need for modern data‑management platforms (29%), budget constraints (23%), unclear implementation requirements (19%), inadequate processes or governance (13%), perception of AI as hype (11%), lack of data access (8%), and overall uncertainty about AI meaning (3%).

Forrester concludes that once these obstacles are overcome, AI will become a driving force for customer‑centric transformation and enable enterprises to reap benefits from interconnected intelligent networks and applications.