AI adoption is moving at breakneck speed. Generative AI has catapulted us into a new era of possibilities, with business leaders expecting a 22.6% improvement in functional productivity within 12-18 months, according to Gartner.
Yet, despite its transformative promise, the challenges of adopting AI remain daunting: data issues, uncertainty about best practices, employee skills gaps, environmental costs, and change fatigue. These hurdles are vividly illustrated in the Gartner Hype Cycle, where AI is transitioning from the “Peak of Inflated Expectations” to the “Trough of Disillusionment.”
At the same time, we’re not just dealing with AI adoption but a broader shift toward multimodal experiences. The traditional concept of interacting with AI through typed prompts is evolving. Now, “prompting” might mean speaking to a device, uploading an image, or even using gestures. The future is “omnisense” - a world where people interact with brands using any modality they choose, whether text, voice, video, or something yet to be imagined.
Can businesses keep up?
This rapid evolution raises a pressing question: Can businesses and their people keep pace? As one thought-provoking quote I heard at a conference a while ago (sorry, can't remember who said it) reminds us, “The pace of change today is the slowest pace of change you might ever experience again.”
For businesses still grappling with the basics of AI adoption - training teams, cleaning data, and establishing best practices - keeping up with multimodal advancements feels like chasing a moving target !
Research from the World Economic Forum (WEF) indicates that 50% of employees will require reskilling by 2025 due to advancements in technology, and the rise of multimodal AI will only accelerate this need. Furthermore, a McKinsey survey shows that 87% of businesses recognize they are unprepared for the level of AI integration needed for competitive survival...
Coping with accelerated change
Here’s how businesses can navigate this rapidly shifting landscape:
- Address the skill gap asap:
Employee readiness is one of the biggest challenges in AI implementation. According to PwC, only 23% of executives feel their workforce is prepared for AI integration. Businesses must invest in upskilling and reskilling their teams, not just for today’s tools but for future modalities. If you end up needing to fire people because they end up being obsolete, you may be accused of not having done enough to upskill them - this is an argument courts are likely to hold against you.
- Adopt a learning mindset:
Organizations must be agile in adopting new practices, experimenting with multimodal AI, and learning from failures. This mindset aligns with research from the MIT Sloan School of Management, which emphasizes that adaptive learning cultures correlate directly with AI implementation success.
- Start small, scale fast:
Rather than overhauling entire systems at once, businesses can test multimodal solutions in specific areas. For example, implementing voice-to-image recognition tools for customer support or AI-driven product recommendations based on multimodal inputs has already shown up to 35% increases in customer satisfaction in early studies by Deloitte.
- Prepare for continuous change:
As the Gartner Hype Cycle suggests, disillusionment is inevitable before AI reaches its full potential. History teaches us that most transformative technologies (like cloud computing or mobile internet) experienced setbacks before achieving widespread adoption. Businesses (and boards) must brace for ongoing challenges, knowing they are part of the process.
The road ahead
Generative AI and multimodal technology promise a future that’s not just smarter but more intuitive. Accenture’s 2024 Tech Vision report highlights that 95% of executives believe multimodal AI will significantly impact how they interact with customers and employees within three years. However, we’re still in the early stages of realizing this potential.
The key for businesses is to stay adaptable and robust, focus on long-term goals, and remember that the fast-paced evolution of technology will only continue to accelerate. As highlighted by Stanford’s 2023 AI Index Report, the cost of AI model training has decreased by over 1,000x in the past decade (this number excludes the enviromental cost though), meaning that innovation cycles will only quicken, making multimodal adoption both an opportunity and a challenge.
For those willing to embrace the shift, the opportunities are limitless.
For those who resist, the risk is being left behind and, utlimately, fail...