It is not news that AI adoption is accelerating across industries. In 2018, McKinsey outlined how 58% of business organizations invested at least 10% of their budget in AI-based techniques and tools. In 2022, Venture Beat stressed that around 70% of the organizations were spending $1 million on AI. There are even instances of brands that have compressed digital transformation plans of 5 years’ worth into just a few months.
While the AI culture is definitely becoming a norm in the corporate space, it is not a one-time process. AI techniques are constantly evolving, and so should your AI Adoption strategy. This requires making sure that your strategy is future-proof. Let’s look at a few measures you can take to do this.
Future-Proofing Your AI Adoption Strategy
Identify the Correct Use Cases for AI
While it may be surprising, the biggest hurdle to future-proofing your AI adoption plan is not cost. It also isn’t the lack of technical infrastructure. The most significant factor in the way of a successful AI adoption strategy is identifying the correct AI use case for your business or brand. Ask yourself the goal you wish to achieve by implementing AI, and an AI capability map can help you. Unless you clarify what do you expect AI to do for you, you are bound to miscalculate while devising your AI adoption strategy.
Do you wish to enhance the quality of the products and services you provide through AI? Or do you aim to reduce the costs of business administration? Do you want AI to help you generate more ROI, or do you want it to help expand your customer base? You need to have these things clear about your business to be able to develop a future-proof plan.
Data Preparation for AI
This is the next step in ensuring your AI adoption strategy is prepared for future evolution. After translating your business requirements into an AI solution with respect to your business problem, you need to prepare the data.
The raw data should be prepared in a way that it is in the form of a large, combined analytical record. This is followed by software optimization, model building, data mining, and feature selection. Careful data preparation ensures a better relationship between data analysts and the data. In other words, it enhances the comprehensibility of data, which helps analysts ask more pertinent questions from their data. This leads to more meaningful and accurate analysis, making sure your AI adoption strategy maintains its precision throughout.
Embrace Security and Ethical Standards
Contrary to popular belief, AI security does not just pertain to protecting financial assets. It also includes the elimination of technical bias meted out by AI tools, protection of compiled data, having a clear set of security policies, and abiding by them. You must ensure that your AI adoption progress, even if it’s a low-risk AI system, is not hindered due to not taking care of the possible legal and regulatory risks.
Building a framework for ethical AI can help prevent problems of this sort. This framework must clearly state the AI ethics and principles, followed by measures to be taken for quality assurance. A structure of this sort will ensure a concrete level of governance and a proper code of conduct. This will help maintain the ethical standards of your AI adoption strategy.
Prioritize Value and Transparency
One of the main reasons to operate in the data ecosystem and analyze any business’ problems and skills is to add value to the brand. Making your AI strategy fit for the future will need you to continuously demonstrate this value and transparency. Make sure your company maintains a level of transparency and opts for AI use cases that are associated with quantifiable KPIs.
The data science department of a business should work on creating an AI culture that promotes an innovative environment. It should also demonstrate brand success in a way that will constantly increase the value of AI in the company.
Scale Up, Skill Up
Besides introducing value and transparency into the data ecosystem, the values need to be made sustainable. When the company is scaling up, and some of the employees move on to a different position, often a vacuum of skills is created in the infrastructure.
This can pose a threat to making the AI adoption strategy adaptive to future changes. Thus, to maintain consistent growth, ensure that upskilling is a part of the work culture. This will confirm that the organization does not fall short on skills and is capable of handling the constant AI developments and application updates.
The business should be agile, quickly pick up on recent AI trends, and imbibe new skills. This will not only help improve previous projects but also pave the way for producing new assets – even in the absence of the original creators of the project.
Make a Flexible Infrastructure
Change in the AI infrastructure is a must if it has to be at par with future developments. Hence, you should design your approach to AI in a way that is agile, highly adaptive, and can easily evolve with changes in the technological landscape.
- The infrastructure should be flexible enough to adapt to changing data processing requirements and data volume.
- It should be such that it is easily scalable and upgradeable.
- It should quickly change as per the requirements of the contemporary AI landscape.
- This infrastructure should also have the bandwidth for experimentation with AI solutions and exploration of various trends within the organization.
Choose the Correct IT Strategy Provider
Ultimately, the reason for future-proofing a business’ AI adoption is to deliver better business outcomes and better ROI. Choosing the ideal IT strategy provider can help organizations achieve all these goals and ensure continued success.
Data-driven digital transformation can help realize value from business processes and data assets. Thus, it is best to choose a knowledgeable IT Strategy provider who will curate a customized future-proof AI adoption strategy for you.
Apart from the above, there are some other measures that can help future-proof your company’s AI strategy. These include:
- Regularly monitoring your AI model’s performance
- Developing a long-term vision for your business’s digital transformation
- Investing in maintaining data quality
Instead of seeking instant gratification, develop your AI model in a way that ensures an unhurried but consistent growth of the infrastructure. Most importantly, make sure that the changes incorporated are quick and lasting.