While the future remains uncertain, businesses must develop a digital strategy that not only builds resilience in the present – boosting productivity and driving efficiency – but also envisions the future. This is crucial to keep pace as all industries evolve to meet escalating customer and employee expectations.
AI will touch every department and workstream. Another big step forward will come when large language models (LLMs) are tapped not just for content generation and analysis but also for decision-making and workflow automation.
By identifying repetitive tasks and leveraging data integrations to make informed predictions and generate automations, AI is well-poised to absorb today’s routine work patterns and free our time for more fulfilling, productive, and profitable work.
While foundational LLMs will be the backbone of generative AI, companies will also start using a combination of smaller, domain-specific language models for cost, performance, and latency reasons.
Desk workers estimate generative AI will save them five hours per week. This presents an exciting new opportunity for companies, tapping into their teams’ creativity, strategic thinking, and innovation like never before.
Chatbots and virtual assistants will simplify the employee experience by automatically booking the right space for a team’s needs. AI will also provide quick responses to inquiries, guide employees to resources, and facilitate service requests.
Most importantly, businesses will transform the way they measure performance and productivity to focus on outcomes like products launched or leads generated, instead of inputs. To do this, leaders will need to shift their mindset from measuring activity to measuring impact.
Semantic query is a question written in a ‘human’ language that then gets translated into machine language. Businesses can provide quick and meaningful, hyper-personalised service with AI using text, images, videos, and audio for search. This sets the stage for a more intuitive and responsive digital economy, benefitting both businesses and end users alike.
As AI grows more adept at gleaning insights from both structured and unstructured datasets, we can anticipate a surge in businesses adopting semantic query capabilities with the merging of structured data, such as sales figures and customer demographics, and unstructured data like blogs, customer reviews, and social media commentary.
All of this is only possible when AI is fuelled by accurate, comprehensive data. Yet full confidence in an organisation’s data quality is elusive, particularly among business stakeholders.
In 2024, the focus on democratising data and analytics will take a new turn. Data and analytics will become more proactive by automatically identifying anomalies in underlying business data, delivering insights in natural language, and providing users with the ability to ask questions and get answers quickly, without pulling in a data analyst.
With overall data volumes projected to increase by an average of 23% over the next 12 months alone, teams are in a race to ensure the quality of the data underlying their generative AI initiatives before their competitors do.
In addition to investing in technical solutions to harmonise disparate data sources and reduce data gravity, teams are paying increasing attention to defining data governance protocols and cultivating strong data cultures among their cross-functional teams.
Broad swaths of customers have raised ethical concerns about how businesses intend to deploy this powerful technology.
Given the speed of its uptake by businesses, the use of generative AI in and of itself may soon be table stakes rather than a competitive advantage. But open, ethical, and transparent use of generative AI may be what sets companies apart in customers’ minds.