The First AI winter took place between the 1970s–1980s. This describes a time where funding, particularly for emerging technologies, was often shifting between active and inactive cycles. Lack of progress and unrealistic expectations were the main drivers behind the cut back in funding. The situation deteriorated further from the 1980s to the 1990s, leading to the onset of the Second AI winter, primarily due to widespread doubt about the capabilities of AI technologies. However, by 2018 the tide had shifted. Compared to other industries, healthcare received the most substantial support of their projects from the government and investors. It was stated that in the United States from 2017 – 2019, over $100 million was invested in American start-ups specializing in pathology AI, with a focus on developing practical AI solutions for diagnostic purposes. As part of the UK government’s second Life Sciences Sector Deal, a £1.3 billion investment was announced to aid experts in the detection of diseases earlier with the use of AI (Serag et al., 2019). Furthermore, 38 projects received investment from the UK government of £36 million in 2021, in the hopes of driving innovation and improving the quality of care in the UK healthcare industry (GOV.UK, 2021). Consequently, opportunities for partnerships between academic institutions, industry organisations, and the NHS have opened with the aim of collaboration and innovation towards future technological development. Therefore, an inevitable “AI implementation race” ensued in the manufacturing, financial and banking, retail, transportation, and hospitality industry.
It is advisable, although tempting, not to enter guns blazing into this race. Businesses must carefully consider several factors to assess their readiness to implement AI. Some factors to consider include investment, strategic alignment, regulatory, and ethical considerations. The fundamental functions of management, planning, leading, organising, and controlling are geared to steer management efforts towards organizational objectives. When coupled with AI, these functions become formidable allies for decision-making, therefore advancing businesses towards their primary objectives of profitability, market leadership and growth, and stakeholder value creation.
Business Goals
Profitability
Enhanced predictive analytics powered by AI can play a pivotal role in guiding strategic decision-making, enhancing the precision of financial forecasts, and improving risk management. This is closely tied to the functions of planning, organising, and controlling within management. AI-infused project management solutions leverage predictive analytics to foresee project risks, allocate resources effectively, and streamline project timelines and finances. There are various project management solutions that incorporate AI. Microsoft Project includes AI features such as automated scheduling, risk assessment, and resource optimization to help project managers plan and execute projects more effectively. Asana is another widely-used project management tool that employs AI to assist with task prioritization, resource allocation, and scheduling, helping teams streamline their workflows. Companies like Accenture may leverage AI to analyse project data and identify trends that can inform project management decisions. Consulting firms like McKinsey may use AI-infused project management tools to improve project planning and optimize client engagements.
This translates into:
- Optimizing resource utilization
- Driving cost efficiencies
- Optimizing revenue streams
Market Leadership and Growth
AI-driven insights can identify market trends, consumer preferences, and competitive dynamics, facilitating the development of innovative products and services. Companies like Amazon may leverage AI to optimize warehouse operations, forecast demand, and coordinate product deliveries. Similarly, AI-fortified risk management systems evaluate risks across the enterprise, simulate various scenarios, and propose strategies for risk mitigation, ensuring adherence to regulations and protection against unforeseen circumstances. AI algorithms analyse data from multiple sources, including supplier performance metrics, geopolitical events, and weather forecasts, to assess supply chain risks proactively. Such information may affect where and when organisations form partnerships and expand.
This results in:
- Entering markets with a competitive edge
- Maintaining a strong position by outperforming competitors
- Identifying opportunities for strategic partnerships
- Exploring expansion possibilities in new markets
Stakeholder Value Creation
AI has the potential to elevate customer experiences through personalized recommendations, efficient customer service, and tailored product offerings. E-commerce platforms like Amazon and Netflix use AI-powered recommendation systems to personalize product recommendations based on user behaviour, preferences, and purchase history. Sentiment analysis tools, powered by AI, monitor feedback from employees, reviews from customers, and interactions on social media. This is then used to assess sentiment and pinpoint areas for enhancing leadership strategies and boosting employee engagement. Amazon Comprehend is a natural language processing service from Amazon Web Services (AWS) that offers sentiment analysis features. Amazon Comprehend offers multi-language support and integrates with other AWS services for seamless integration into existing workflows. Additionally, leadership development programs augmented by AI utilize machine learning algorithms to scrutinize leadership attributes and conduct, pinpointing areas of proficiency and areas needing improvement, and offering tailored coaching and training suggestions. Platforms such as Peakon use AI to analyse employee feedback data and provide actionable insights to leaders and HR professionals for improving leadership effectiveness and employee engagement.
Thus, leading to:
- Enhanced brand reputation
- Customer loyalty
- Employee retention
Stakeholder Engagement and the Role of Transparency
The power-interest matrix provides decision makers insight on key stakeholders. As this can vary across organisations, identifying key stakeholders when discussing implementation of AI systems is pivotal. It is vital for organisations to have a clear view on who has the highest level of influence or interest that may be affected or negatively influence their decisions.
Skepticism from stakeholders, particularly pertaining to the healthcare industry, of the implementation of AI often comes from its lack of explainability. Accountability and trustworthiness are significant driving forces for successful AI implementation as these directly address the issues encountered when discussing explainable AI. Regardless of the industry, trust needs to be built and maintained with stakeholders. Stakeholders such as the general public and employees can have a negative impact on implementation, particularly if they display resistance to change. If resistance primarily stems from key stakeholders, the implementation may face delays or failure. It is important to assess the origins of this resistance in order to address it. Cases of resistance to change are often related to misinformation accumulating over time. Therefore, to address resistance to such a case, informing potential users of its benefits and openly and honestly addressing their fears may potentially result in less push back. An undercurrent of resistance from healthcare employees resulted in Welsh Health Boards experiencing difficulties when attempting to implement technological solutions to increase operational efficiency. Bridges states, the first task of transition management involves convincing people that there is more to life than where they are right now (2003). However, if they are consumed by fear and are kept in the dark, they will never be open to the idea of change.
Resource Allocation for AI Implementation
Financial Resources
While the introduction of AI systems may strain business finances, it’s essential to consider the Return on Investment (ROI). Conducting a thorough ROI analysis provides a clearer picture of the associated benefits, whether short or long term. Factors such as efficiency gains, cost savings, and potential revenue growth should be considered.
People
During the talent acquisition phase, valuing previous experience working with AI is essential. Alongside recruiting new talent, businesses should consider existing employees and explore strategies for training and upskilling. Temporary hiring of experts to train existing employees can empower and instill confidence in working alongside AI.
Technological Resources
For businesses unaccustomed to technology, investing in a robust and scalable technological infrastructure is crucial for effective AI implementation. Considerations around cloud computing, data storage, and computational power are necessary components in this technological transformation.
More and more organisations are joining in on the “AI Implementation race”. However, the last thing you want is to join a race you are not ready for.
Recommended considerations before entering the race:
- SWOT Analysis (Strengths, Weaknesses, Opportunities and Threats)
- Capacity Analysis
- Feasibility Analysis
- Technology Acceptance Model (Davis, 1989)
Planning, leading, organisaing, and controlling are pivotal when driving primary objectives of profitability, market leadership and growth, and stakeholder value creation. There are a range of benefits of AI implementation including increased efficiency, accuracy, insight, innovation, automation, and risk mitigation. As a result, adding in AI tools into the daily operations of organisations leads to businesses are accelerated towards achieving their objectives.
Sources used for this article
Bridges, W. (2003). Managing transitions: Making the most of change (2nd ed.). Nicholas Brearly
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information Technology. Management Information Systems Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
GOV.UK. (2021, June 16). £36 million boosts for AI technologies to revolutionise NHS care. https://www.gov.uk/government/news/36-million-boost-for-ai-technologies-to-revolutionise-nhs-care
Serag, A., Ion-Margineanu, A., Qureshi, H., McMillan, R., Saint Martin, M. J., Diamond, J., O’Reilly, P., & Hamilton, P. (2019). Translational AI and deep learning in diagnostic pathology. Frontiers in medicine, 6, 185. https://doi.org/10.3389/fmed.2019.00185
