Defining Organisational Readiness
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In general, organisational readiness refers to an organisation's ability to make significant changes or embark on a large new initiative. It is critical for an organisation to demonstrate that it has the resources and ability to successfully implement change. It can also safeguard the company's brand by demonstrating organisational change readiness. According to theory, a higher level of organisational preparedness boosts the innovation adoption success and lowers the probability of failure.
Today, Artificial Intelligence (AI) is playing a critical role in altering business. Digital technologies' availability, accessibility, scalability, ease-of-use and ease-of-deployment have enhanced the incentive for all organisations to innovate utilizing such technologies." According to McKinsey Group research, Artificial Intelligence will add $13 trillion to the global economy over the next decade. While technology is being used to improve decision-making in organisations, many companies are failing to reap the benefits. Only 8% of organisations engage in core AI operations across the board, according to the study. The majority of respondents polled had only tested or employed AI for a single business process or function. The fundamental cause for this was a "failure to rewire the organisation," or, in other words they lacked organisational readiness
The linkages between people, processes, systems, and performance assessment are critical to organisational readiness. To achieve a successful execution, they must be synchronized and coordinated. As a result, the organisation should have systems and people in place to manage and synchronize efforts, as well as openly and effectively communicate changes. The organisation must be willing to accept change, which necessitates an ongoing process of education and adaptation. The purpose of driving change is to reform the organisation and modify people's attitudes and approaches to their jobs.
AI will add $13 trillion to the global economy over the next decade
Barriers to Organisational Readiness
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There are numerous challenges that organisations face when introducing and implementing AI programmes. Failure to anticipate and address these issues can, and often does, lead to minimal return on efforts and investment.
In this video, Michael Boyle, Managing Director for Procurro Solutions, discusses some of the issues organisations should consider when evaluating whether they are ready for undertaking transformation in general.
Source: https://www.youtube.com/watch?v=ae8q2MvfKCQ
KEY BARRIERS
Failure to ascertain the rationale: Key to assessing an organisation’s readiness for adopting AI is knowing why they are adopting it, in other words, they should be considering, ‘for what reason do we need this AI technology?’. At the outset, failing to ascertain the key rationale for adopting AI is fundamental to ineffective implementation.
Complexity and scope of business model: The more complex an organisation’s business model is, the greater the number of factors that require attention when adopting a suitable AI framework. “Readiness models require context-specific consideration and need to be tailored to the related domain, i.e. a specific technology” (Molla and Licker 2005). Also to be considered is the organisation’s existing level of AI maturity. A common issue when they are new to embracing AI technologies is that the chosen framework is too advanced; subsequently they struggle to keep pace from both technical and operational perspectives
Short-term focus: Organisations that expect speedy results and measure their success accordingly are more prone to failure. This is often due to the inability to realise that the anticipated profitability desired from an AI initiative requires a long-term strategic approach.
Poor leadership: The lack of a fundamental understanding of AI among senior executives will impact negatively on an organisation’s ability to adapt and adopt. When leaders are not first prepared themselves, this inevitably permeates down through all levels in the organisation.
Project governance is not clearly defined or is not appropriate: Successful execution of an AI project is at risk when there is a lack of clarity regarding the key stakeholders responsible for the governance of the project or project phase, or if the make-up of the governing group is not fit for purpose. In addition, as the project moves through each phase the governing group is often not adjusted to reflect the change in emphasis.
Lack of skills and knowledge: Insufficient or inadequate skills is a recurrent challenge for organisations in the early stages of their AI journey. Proceeding without the required skills adds unnecessary risk to an AI project particularly in terms of project success and financial resources. Because emerging technologies such as AI are changing the nature of work, the skills and knowledge that organisations need and value are evolving and this is resulting in a skills gap for many. According to a recent McKinsey survey, 60% of global executives expect that about half of their organisation’s workforce will need retraining or replacing within the next five years. In addition, more than a third admitted that their organisations are unprepared to address the anticipated skills gap (Tun, 2020).
Lack of performance measurement: When unsuitable or inadequate project performance metrics are utilised for monitoring the adoption process, meaningless data will be generated and thus yield ineffective and skewed results. This can lead to issues such as incorrect resource allocation.
Rigid and risk averse: Organisations that possess a rigid mindset and are reluctant to take risks are unable to cope with the agile, experimental, and adaptable approach required particularly in the early stages of AI implementation and adoption.
Silo mindset: A silo mindset exists when departments do not share information with others in the same organisation. Similarly, if the organisation’s processes are siloed, it can impede the implementation and adoption of AI. Organisations that assign budgets by department may struggle to create the collaborative and agile teams required for effective execution.
Resistance to change from employees: Organisations that fail to communicate the reasoning and urgency of change initiatives struggle to get employees on board. Moreover, this is particularly pertinent with AI projects, as there is often the associated fear that AI will replace job roles.
Actions for Organisational Readiness
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“THE ESSENCE OF STRATEGY IS CHOOSING WHAT NOT TO DO” - Michael Porter, Harvard Economist
To prepare for the required level of organisational readiness, organisations must be cognisant of the potential challenges and barriers they may face and consider them in advance to ensure successful integration of AI. This will prevent unnecessary investments and costly failures, and as such, careful attention should be given in the following key areas:
1. Strategic alignment
“Strategic alignment provides a framework for decision making on what is important to the organization and what is not. It brings harmony in planning and action” (Collins, 2001). It is prudent for an organisation to arrange its internal and external elements to best support the achievement of its long-term goals and purpose.
Fundamental to strategic alignment is providing a vision that unites employees around a common goal. This provides a robust foundation on which to build and support organisational activities, and to cultivate an organisational identity. Employees need a comprehensive understanding of why AI is important to the business and how they will fit into a new AI culture. This will go some way to reassuring them that AI will augment their roles rather than diminish or eradicate them. McKinsey Group research indicates that most employees will need to adapt to using AI rather than be replaced by it.
Because AI initiatives are often difficult to implement and require more than a year to launch, organisations need to focus on developing a portfolio of initiatives with different timelines rather than on speedy results and quick wins. Priority should be centred around a long-term view (typically 3 years) and structured taking a phased approach to ensure value is maximised.
AI’s innate intricacies require change across all organisational levels and because of that, top level management must demonstrate their commitment and support. Leaders themselves must be knowledgeable and competent in all areas pertaining to the AI initiative, and role-model appropriate attitudes and behaviours. This will be instrumental in conveying the strategic importance of AI and its relevance to the organisation.
At middle management levels, there are 3 key functions that require equal representation to reflect parity of the varying perspectives: Business, IT and Analytics. Forming a joint taskforce to govern and oversee the AI journey will ensure that the 3 functions collaborate with one another and share accountability, regardless of the project phase and/or project emphasis. Roles and responsibilities within each execution team must be clearly defined, and one function may take a lead role, however this is dependent on the requisite expertise for each project phase and as such will be interchangeable. This interdisciplinary approach brings a diversity of viewpoints to the team, supports cross-function understanding and ultimately leads to more accurate and effective decision-making.
2. Resources
The resources required for AI implementations will vary significantly depending on the complexity and scope of an organisation’s business model. Success is contingent on having the right resources in place to complement and support other elements such as skills, infrastructure, technology, and processes. It is crucial to adopt an AI framework that is suitable and appropriate not only for the needs of the organisation, but also for their capabilities.
AI maturity plays a major role in determining what resources are required and where they are allocated. In general terms, resources are categorised as financial, technological and personnel (human) resources. Organisations that are ‘mature’ in terms of AI capabilities are more likely to apportion significant resources in technological areas as they are suitably founded to cope with the pace and level of technical innovation. Conversely, it is more prudent that organisations that are ‘new’ to AI, focus their resource allocation on integration and adoption as much as technology. This creates a solid foundation on which to build and develop an AI initiative.
Every organisation has their own unique requirements for determining successful performance dependant on their strategic objectives. 4 common categories include:
- End user satisfaction (voice of the customer)
- Financial goals
- Internal processes
- Growth potential
Ascertaining the most appropriate and effective performance metric will assist decisionmakers in the allocation and/or re-allocation of the resources necessary for ensuring that the associated targets are met.
3. Knowledge
Organisations need to address and close any skills gaps to safeguard their future and sustain their competitive edge by establishing the required skills and proactively recruiting if necessary. They must move toward a digital mindset where innovation is rewarded, and bring in additional digital expertise to assist in incorporating the digital world and acquiring the necessary skills and knowledge.
In addition, existing employees need to be provided with the relevant skills and knowledge to perform their new roles. Investments in in-house development training is vital for the successful adoption of AI and applies to everyone from the top level down.
Daly (2018) suggests that “making a success of AI in the workforce requires a three-pronged approach involving upskilling, diversity, and rethinking the nature of work itself”. Some organisations have created their own AI learning academies to support this process, utilising a mix of in-house and external resources and capabilities (HBR, 2019)
Common to most is a focus on training in 4 key areas:
i. Leadership: Top level management are trained for an advanced understanding of AI, as well as areas such as the impact on employees, barriers to adoption, skills development requirements, and cultural change.
ii. Analytics: The focus rests on honing the hard and soft skills of those who are responsible for data analytics, data governance, and creating AI solutions.
iii. Interpreters: These are employees who act as ‘interpreters’ between the analytics team and the end user. They usually work in the business function of the organisations and so require fundamental technical training, e.g. how to apply analytic approaches to business problems.
iv. End user: Those using the software, e.g. frontline employees, marketing and finance departments, will need tutelage on using the new AI tools.
Training and upskilling existing employees, and recruiting new employees with the required skillsets, require equal attention and emphasis for successful AI implementation.
4. Culture
Creating a culture that is conducive to successful AI implementation is critical. It enables employees to understand their organisation, to develop relationships and to recognise a common purpose.
“Bridging the AI skills gap is not just about having the capability to attract top AI talent, but an agile, cross-organisational approach to AI that upskills different stakeholders together in line with long-term business strategy” (Daly, 2018). Purging the organisation of a silo mindset underpins all efforts that support this approach and aids the reinvention or creation of suitable culture in which AI will thrive and prosper.
Collaboration should be promoted in various ways so that employees with diverse skills can complement each other. Employees will be required to work together in a new way that breaks down the silos to effectively deal with cross-dimensional issues. They need to learn from each other in order to respond more quickly and consistently to changes in the market and within the organisation. The use of ‘interpreters’ and the embedding of technology into the fabric of the organisation can help in doing this.
Because of the multi-dimensional and vigorous disposition of AI digital transformation, an organisation should undertake proactive measures and activities to move from being rigid, policy-driven, and risk-averse to becoming agile, experimental, and adaptable. This allows for more flexibility in their approach, particularly in relation to key areas such as decision-making, allocating (or re-allocating) resources, and innovation.
5. Innovativeness is based on the amount and pace of adaptability that employees possess (Kruse et al., 2019). Innovative actions are required by employees to instigate change at a swift pace and in multiple areas, so that the organisation can realise the full potential of AI. Such actions include experimentation, risk-taking, and problem-solving, and they should be actively encouraged (Microsoft 2020; Yuan and Woodman 2010). Innovativeness will encourage and enhance employees’ readiness to accept the change associated with the implementation and adoption of AI initiatives. Similarly, it is the frontline employees who build, deploy, and monitor new AI capabilities, so seeking their input and providing support for ‘bottom-up’ initiatives will further cement their acceptance and commitment.