AI is transformational. AI is exciting. AI is mysterious. AI is scary. AI is omnipresent. We’ve heard this oscillating narrative over the last few years (and will continue to in the future), but in this unprecedented year, one thing became clear — enterprises need to find a way to safely, creatively, and boldly apply AI to emerge stronger both in the short-term and in the long-term. 2020 gave leaders the impetus, born out of necessity, and confidence to embrace AI with all its blemishes.
The kinks in AI still remain: lack of trust, poor data quality, data paucity for some, and a dearth of the right type of tools and talent. 2021 will see companies and C-level leaders tackle some of these challenges head on, not because they want to but because they have to. The time is now for AI to shine.
In 2021, we predict:
- AI and machine learning (ML) will permeate new use cases and experiences. In 2021, the grittiest of companies will push AI to new frontiers, such as holographic meetings for remote work and on-demand, personalized manufacturing. They will gamify strategic planning, build simulations in the boardroom, and move into intelligent edge experiences. Coupled with this, lucky laggards will use no-code automated machine learning (AutoML) to implement five, 50, or 500 AI use cases faster, leapfrogging their competitors with capable, entrenched data science teams that take a traditional, code-first approach to ML.
- Workplace AI will boost automation and augmentation needs. In 2021, more than a third of companies in adaptive and growth mode will look to AI to help with workplace disruption for both location-based, physical, or human-touch workers and knowledge workers working from home. This will include applying AI for intelligent document extraction, customer service agent augmentation, return-to-work health tracking, or semiautonomous robots for social separation.
- There will be more progress toward trusted data for AI. 2021 will showcase the good, the bad, and the ugly of artificial data, which comes in two forms: synthetic data that allows users to create data sets for training AI and fake data that does the opposite; it perturbs training data to deliberately throw off AI. Companies are also facing increasing pressure from consumer interest groups and regulators to prove data’s lineage for AI, including data audit trails to ensure compliance and ethical use. In 2021, blockchain and AI will start joining forces more seriously to support data provenance, integrity, and usage tracking.
Read our full AI predictions report for more details on what these trends will mean for your organization.
Several Forrester analysts and research colleagues authored and contributed to this year’s AI predictions research including Mike Gualtieri, Craig LeClair, Michele Goetz, Brandon Purcell, Martha Bennett and Aldila Yunus.