Organizations Should Adopt a Post-Covid AI Strategy: Here are some Tips
We need a strong AI strategy to deal with the after-effects of Covid.
Nobody saw the worldwide effect of COVID-19 coming. When was the last time the whole world was shut and we deserted the roads to isolate? The aftermath has been destroyed in numerous ways—at least for us humans. Artificial intelligence (AI) has ended up being a crucial technology in the fight against the Covid pandemic. Artificial intelligence has empowered the implementation of predictive models of potential disease contagion and containment, and has been utilized for screening and tracking patients.
But at present, enterprise agility, workforce resilience, and cost reduction have all become top priorities for business leaders. Digital transformation through AI can play a vital part in supporting a COVID business recovery plan. From reallocating resources to increasing operational proficiency, the case for digital transformation and AI is growing stronger.
As we enter another era of innovation, work and life, there will be increasing pressures for IT leaders to rapidly scale AI and its strategies – including machine learning, computational intelligence, knowledge representation, natural language processing, and that’s only the tip of the iceberg – to empower an automated, smart, insight-driven organization.
How can we do that? Here are tips for creating a post-Covid AI strategy
Deploy cross-team AI and data science collaboration
Implementing AI can take a town. Data science and AI teams have a long way to go from application development and DevOps teams in operationalizing the process from ideation to results at an accelerated pace. The Cross-coordinated effort is vital. By harnessing the potential of talent with cross-discipline foundations, companies can speed up learning, increase efficiency and bring models to production quicker in a unified environment.
Enable multidisciplinary teams
Companies should come up with methods of working that empower multidisciplinary teams to work together effectively, provide the best products and services, and enhance predictably and effectively. For instance, Accenture’s Legal organization joined hands with its Global IT Applied Intelligence team to build up a solution for the challenge of overseeing and figuring out the huge number of legal documents that are executed every month.
The Applied Intelligence team worked along with the Legal group to apply predictive models, machine learning, and AI to make powerful and self-learning search tools that assist its Legal team with performing accurate data searching and extraction, harnessing data that was beforehand not effectively accessible. UI and experience skills were similarly as imperative to guarantee its end-clients were able to effectively harness the power of the AI models.
Reinforcing AI in professional schooling and training
With the developments in technology, AI technologies may assist the change to improved quality of jobs and increase demand for skills protected from automation, like leadership, interpersonal communication, creativity and organisational skills. Collaboration with digital devices is additionally a critical attribute of occupations with lower automation risk, even more huge in the Covid period given the developing requirement for workers to do their jobs remotely.
As the requirement for distance learning is growing, more have additionally been exploring AI technologies as a method for improving personalised learning solutions and open schooling resources, which can be custom-made and adapted to students’ learning abilities. Artificial intelligence tools can likewise screen learning challenges, detect early signs of possible student failure, and do a remote evaluation.
Measure the ROI of Explainable AI
Investments in explainable AI are essential for scaling AI for resilience in the post-Covid world. By composing an AI model, its normal effect and expected biases, explainable AI capabilities can assist companies with overseeing regulatory risks and, significantly after COVID, link model accuracy with monetary value. As one data scientist in financial services said in a commissioned Forrester study, with explainable AI, models are currently more precise, which implies we can all the more likely estimate our necessary cash reserve requirements. A 1% improvement in accuracy opens up millions of dollars for loans or investments.