How ready are we for a successful first step towards AI, and what are the solid foundations for it?
SwissCognitive Guest Blogger: Zhaklina Todorova – “The Lord Of The Indexes: The AI Readiness Index”
Why is this starting point so important? If we dare to even think about making an association between the commonly measured Happiness Index and the new ruler of indexes, the AI Readiness Index, we can figuratively say that this starting point of readiness is a prerequisite for a “happy” and successful transition to AI.
How ready are we to implement AI?
Various qualified studies and surveys indicate that more than 80% of companies are not fully ready to use AI and AI-powered technologies in full size and capacity.
In one of the representative reports on the AI Readiness Index, covering a survey of 8,161 business leaders responsible for AI integration and implementation in companies with 500+ employees across 30 global markets, the readiness of companies globally is examined against the factors for a successful AI model – Strategy, Infrastructure, Data, Government/Governance, Talent and Culture. Depending on the results in these areas, companies are divided into four Pacesetters (fully prepared) categories, Chasers (moderately prepared), Followers (limited preparedness), and Laggards (unprepared).
The overall AI readiness measured across the four categories of companies, according to this report, is Pacesetters (14%); Chasers (34%); Followers (48%), and Laggards (4%).
The group of leading companies across all metrics includes companies with high technology usage, such as Technology Services, Retail, Financial Services, and Business Services. Sectors that are dependent on providing personalized service and care are further back in the AI readiness rankings. These are sectors such as Education, Communications, Healthcare, and Transportation.
Another research study among 2100 business leaders shows that in over 90% of the cases, the application and implementation takes 12 months or less. Organizations realize a return on AI investment themselves in 14 months. Challenges to implementing and scaling AI according to the companies surveyed include lack of skilled talent, availability of ecosystem data from reliable and trusted sources, AI regulation, and partnerships with trusted technology solutions and service providers.
Creating Value with AI based business model
Implementing AI adopting AI technology alone is not enough for the AI Success Model to create such value. Therefore, choosing a business model to create value, delivering it to customers, and returning the investment to the company is important. AI Business Models can develop AI skills as a service, partner with various specialized vendors, and leverage AI ecosystems and platforms. Since AI is inherently a resource, it needs to be managed wisely and precisely as a resource to achieve the best results. Applied in totality, without one day and partial solutions, because here we are talking about the exceptional speed of development, but applied in a long-term concept.
Stable foundations of creating AI value
- A Business strategy with clearly prioritized goals and measuring the AI Value ROI, asking the clear question of what is the ROI, and using AI sample cases, and AI use cases for mature AI implementation. Gartner’s AI Maturity Model tool is dedicated to assessing the AI maturity of companies and identifying areas for improvement. According to it, the five levels of AI maturity are Level 1 Awareness – starting to explore AI technologies, any implemented AI solution yet, Level 2 Active – started experimenting with AI, implemented some pilot projects, Level 3 Operational – started to operationalize AI technologies, Level 4 Systemic – implemented and scaled AI technologies with business value of AI, Level 5 Transform – fully integrated AI in business process, drive transformational change.
- The Technology strategy and infrastructure for secure and fast access to quality and reliable data. It is important to note the realization by company leaders that technology is the means, not the end. They are not the outcome in and of themselves but are an accelerant for outcomes that only have an impact if implemented according to our company’s concept.
- Compliance with regulations and ethical considerations.
- Creating an organizational culture that nurtures innovation, agility, and teamwork. Having a clear model with active leadership support, which includes:
– AI expertise, AI talent, and AI skills as key resources in the AI business model as well as in building a digital mindset.
– Continuous Learning and Upskilling. The AI business model should foster a culture of learning and provide skill development opportunities, such as training programs and workshops to elevate the AI skills of its employees. This can cover topics such as applying AI in business for machine learning, deep learning, natural language processing, and data analysis, as well as applying various AI tools AI in business such as Prompt Engineering, AI for daily work tasks such as text translation, resume writing, proposal writing, email, contracts, estimating project turnaround time, creating presentations, website content and structure, applying AI in Marketing, applying AI in Design, Video and Audio with AI. AI in Educational topics, Analyzing documents with AI.
– Collaboration and knowledge sharing through dedicated professional platforms for experts create a supportive environment for growth.
– Partnerships and external networks with academic institutions, research and survey organizations, and various AI organizations can provide access to a large pool of AI talent and experts.
– Regulatory Environment and AI Governance, an environment that is compliant with privacy regulations, Data Privacy and Security, Data reliability and transparency, ensure responsible and ethical use of AI and process implementation.
Our Readiness Index
As we move up the AI Readiness Index ladder, there is a risk in betting on current trends, always catching the crest of the wave just to be there, without sticking tightly to the specifics of our identity and services. This risk and its consequences can be avoided by aligning ourselves with what is right for us, with what is aligned with our so-called Hedgehog Concept, according to Jim Collins, namely, what we can be best at, what we are most passionate about doing, and what our economic denominator is, i.e. what we profit best from. Only then can we sustainably and sustainably embark on digital transformation and the successful implementation of AI.
Building our successful AI business model is based on a strategic framework tailored to our industry, target markets, and the specific solutions we want to apply AI. This encompasses defining clear objectives, selecting the right AI tools, and developing a sustainable model against them that can scale. Additional factors such as continuous innovation, data management, talent recruitment and training, ethics, and regulations play a key role in our successful business model.
The lord of indices triumphs in the digital and physical space and it is up to us to know how best to use it for our specific use case and know where we are starting down the road. What the success index is measured by is Preparing for AI, our readiness for AI, to measure it properly, to only get into what’s right for us, without throwing ourselves in many different directions so we can be persistent in our future development.
About the Author:
Zhaklina Todorova, PMP, Business professional, with valuable experience in managing dynamic business projects (B2B – Sales, Manufacturing, BPO DACH) in EU companies with the highest technological and business standards, active “agent of change” dedicated to the synergy of AI Success and Business Models.