Source: AI READINESS AUGMENTATION WITH HUMAN - Bing images
How can organisations transform to accept AI?
When embedding AI organisations need to be able to maximise the potential value which can be driven by integrating AI into everyday practices. AI integration can take many forms from data sharing, pattern recognition, insight generation or process automation, as examples. Effective and sustainable acceptance of AI will support strategic decision making and should be seen as a source of strategic advantage.
Leading the Transformation
The ability to transform an organisation from ambition to reality moving beyond strategy and planning toward meaningful AI transformation requires highly effective leadership and a culture which embraces change and transformation.
The World Economic Forum (WEF) study into The Future of Jobs (2020) estimates that by 2025 85 million jobs may be displaced by a shift in the division of labour between humans and machines, while 97 million new roles may emerge that are more adapted to the new division of labour between humans, machines and algorithms.The world of work is changing, and AI is playing an increasing role in this change.
The AI market is expected to exceed $191 billion by 2024 accelerating at a 37% annual growth rate (Deliotte 2021).
While a natural fear is that AI will replace jobs, current research shows that AI is complimenting the world of work and creating a refocus on the need to drive human social skills such as emotional intelligence, persuasion and negotiation, while other narrow technical skills are better competed with the help of AI, such as programming or equipment and control. The integration of AI has increased dramatically over the past few years across many sectors, as the previous chapters have highlighted. This has been further amplified by the daily use of AI technologies during the global COVID pandemic.
The key leadership challenge for successful AI integration is the augmentation of AI and human capabilities into the culture of the organisation. Humans and AI enhance each other’s complementary strengths, the leadership, teamwork, creativity and social skills of human nature and the speed, scalability, and quantitative capabilities of AI. Organisations requires both kinds of capabilities, one cannot exist without the other.
Organisations that use AI to primarily replace human labour will not gain the full advantage of augmenting AI and human capability, it will be organisations that are able to embrace collaborative intelligence, transforming their operations, markets and industries that will reap the full rewards of this new approach to the world of work. Leadership teams need to fully understand how AI will integrate into operations and how this knowledge will be shared between departments, as such planning and quantifiable measurements are key. Investment may be needed in the attainment of cognitive technology talent, which is scarce in the general labour market, hence the need to upskill and reskill labour forces.
Change can be difficult for employees to accept, especially if there is a lack of understanding of how AI can work best, i.e. through augmentation with human capabilities. When planning for AI integration organisations need to be aware of the change curve and work with employees towards acceptance. Kubler Ross change curve (1969) is a useful model in enabling individuals to understand the impact of change, originally developed to cope with grief, the model has subsequently been used to enable and enlighten change both on a personal and professional level.
How quickly employees will travel through each stage of the model is individual, however a number of key contextual factors can impact the acceptance of change, including communication ensuring any fears regarding job displacement are alleviated. The model has 5 key stages:
- Denial
- Anger
- Bargaining
- Depression
- Acceptance
Approaching the upskill and reskill opportunities positively will enable quicker acceptance of change and recognition of new opportunities for both the individual and the organisation.
Source: https://masteringbusinessanalysis.com/wp-content/uploads/2018/04/Kubler-Ross-Change-Curve.jpg
Leadership is responsible for building the behavioural readiness to accept the transformation of the business, enabling AI to be effectively embedded into operations and strategic vision. Consideration needs to be provided to ensure there are sponsors at all levels of the organisation who have the capacity to lead the change, employees need to know how AI will change their job roles and be ready to accept that change.
The leadership team is responsible for developing and delivering on an effective communication strategy which acts to inform, engage and develop awareness and acceptance of how the organisation will change and the benefits which can be derived in real terms for all employees and stakeholders. Leadership for digital transformation will be further discussed in Chapter 6 (Digital transformation, sections 1 & 2).
Identifying the AI Gaps
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A key aspect of building transformational readiness is understanding where the AI gaps are both strategically and perceptually within the organisation.
AI gaps can occur when for example there are inflated perceptions of AI capabilities in leaders compared to line managers. Organisations need to seek value capture through quantifiable targets and engagement through communications with key stakeholders. A whole quality approach needs to be taken whereby the operators are consulted to maximise value and seek opportunities for continuous improvement. This in turn not only helps identify the AI gaps but ensures a culture of AI acceptance is embedded.
Less than 15% of organisations are implementing AI at scale (Deliotte, 2021).
A challenge to AI adoption is the availability of skilled labour to lead that transformation and a perception from organisations that current labour forces will not be able to fully realise the benefits of AI integration.
To ensure successful AI integration across an organisation a whole company approach needs to be considered and adapted with a clear strategy and roadmap alongside benefits tracking. An aspect of this is integration between departments, when this is not properly planned and implemented AI Gaps can occur. Benefits tracking and quantifiable measures can assist in identifying any gaps which may occur. Organisations need to have employees who can explain AI outcomes and can work to ensure that AI systems are functioning properly, safely and responsibly, focusing on prevention of harm by AI, e.g. in the case of driverless cars and protecting GDPR and privacy.
There are several gaps which need to be addressed in the implementation of AI, namely the ethical and trustworthy aspects of AI, elimination of biases and fairness, privacy and data rights and manipulation regarding freedom and democracy. Further discussion will be provided in the Chapter Ethics around these key issues, however from the transformational aspect organisations need to be fully cognisant of these issues to ensure when AI is being implemented into organisational structure adequate consideration has been given to these key areas.
A significant gap can lie between the perception and strategic advantages of embedding AI into new working practices to the acceptance of the change from within. This is where organisations need to ensure their strategy is aligned to the culture and heart of the organisation to enable acceptance of that strategy, as Drucker commented ‘Culture eats strategy for breakfast’ (2006).
Speed is the key to successful AI implementation and a significant challenge when gaps occur. Aligning AI efforts to business strategy and goals and communication of these is a critical success factor. Integrating AI into business operations and strategy relies on organisations being able to pivot ‘how’ they work, transformation can take time.
Deloitte’s ‘State of AI in the enterprise, 2nd edition’ survey (2018) identified optimizing internal operations (42%) as a top benefit of using AI to drive transformational change, while improving existing products and services remained a top benefit at 44%.
Integrating AI into organisational strategy drives key benefits however for full optimisation organisations should consider combining experimentation with execution discipline, prioritising cybersecurity at the start of the process, deploying AI beyond the IT infrastructure to enable full integration, leverage off the shelf software solutions opposed to relentlessly searching for bespoke solutions and design an agile talent strategy. By doing so organisations can work to reduce the potential of AI gaps and build a more sustainable strategy for success.
Transforming the future
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AI has the capacity to work effectively with humans in the world of work in an augmented and ethically approached manner. The fourth revolution as referred to by WEF is already generating significant improvements in the world of work and life. Several years ago, the anticipation was that robots would replace humans however this has not been the case. AI advantages of managing mega data has worked in tandem with the search for more emotionally led skills such as emotional intelligence, persuasion and negotiation.
Organisations which can successfully transform operations and implement AI at speed can gain significant advantages for future growth and profitability in a competitive environment. These include:
- Agility: adjusting seamlessly to market conditions with better and faster insights to facilitate timely decision making.
- Accuracy: Arming decision makers with reliable trustworthy information
- Ability: Enabling innovation and accelerating efficiency
Organisations need to consider several questions before they can successfully consider the implementation of AI:
- What decision in your business do you need to use AI for?
- How does your org make the decision today?
- How does your organisation recognise good and bad decisions today?
- What data do you need to make the decisions today?
Transforming the future requires a team approach, a recognition that true success can only be achieved supported by an integrated whole strategy approach. Where leading by example, delivering on key rewards and promotions to support career enhancement and educational programmes to support change, work in tandem creating the change required to drive success.
AI teams need to understand the business challenges to truly integrate AI solutions, it is no longer enough to only know the technologies, AI teams must be business savvy.
A key challenge to AI integration is the availability of skilled labour, it is anticipated that computer occupations as a group will grow by 11.5% by 2029, in 2019 this group only represented 2% of all STEM jobs.
It is unrealistic to expect to find people with an abundance of experience in machine learning as the technologies are too new and changing at such a fast speed, therefore an agile recruitment and development strategy needs to be adopted to upskill and reskill workers to take on the work challenges for the future.
Change is the only constant in today’s environment and digital disruptions are more frequent, complex, and uncertain. Organisations can remain resilient by embracing these changes, adapting their cultures, and remaining competitive.
According to the WEF AI machines handled approx. 29% of tasks across 12 industries in 2018. By 2022 62% of search and data processing tasks will be handled by machines.
AI will transform the future of work by reducing repetitive work and supporting employees, machine learning, automation and language processes are key areas of AI development and integration supporting these changes. Moving forward the pressure to reduce costs and focus on leaner strategies to reduce duplication of work while managing efficiency, will focus organisations to invest in AI.
AI has already proved to highly effective in several key areas including fighting fraud and cybercrime, developing conversational AI enabling interaction with humans, e.g., Amazons Alexa. Bots and virtual assistants have supported the streamlining of work performance, AI recruitment and talent sourcing have supported the filtering of candidate profiles to meet recruitment specifications, saving huge amounts of time.
AI has proven to increase productivity and efficiency by reducing costs and duplication of labour meaning better customer service and improved revenue. This in turn enables humans to focus on core tasks and complete these tasks in a more efficient and customer centred manner.
Transformational readiness requires leadership, a clear and well communicated strategy to integrate across the whole organisation while appreciating there will be gaps identified and working to transform the future creating a more agile and competitive organisation.
AI should be seen as a complement not a replacement to human labour, the relationship between human workers and AI is likely to be a symbiotic one. Instead of being concerned regarding singularity it is time to consider the concept of multiplicity, whereby combinations of machines and people can work together to create innovations not yet imagined.