A nine-step guide for enterprise leaders to explore AI 

AI is in the news almost everywhere, everyday. Leaders are constantly searching for ways to make it useful for businesses and organisations. There is a clear fear of missing out on this opportunity for many. The fear is real. AI is dramatically evolving. So let’s be ready to explore it diligently.

Here are steps to begin your organisation's journey into AI in a quick, straightforward, and low-risk manner.

1. Explore Current AI Capabilities 

The first step is to set what you want from AI deployment and explore current AI capabilities that can maximise productivity.  A logistics company might aim to use AI for optimising delivery routes, leading to cost savings in fuel and improved delivery times. Having clear goals directs efforts and resources efficiently. Leaders can ask what they need AI to do for them. To set that, you need to know what current AI capabilities are. Clarity is a friend in the age of disruption.

2. Build A Tiger Team 

A successful AI project is all about  having the right blend of talent. An insurance company that forms a diverse team, including IT experts, data analysts, and claim processors, to develop an AI system for automating claim assessments. This combination of skills ensures a holistic approach to AI development and implementation. An enthusiastic forward thinking team from business and technology who can bring great energies to experiment passionately.

3. Choosing the right AI tools 

Selecting the right AI technology is crucial. A manufacturing enterprise might choose AI-powered predictive maintenance tools to identify equipment failures before they occur, significantly reducing downtime. The quality of the data to train the model matters. The enterprise data sources should be made available for the tiger team. Choosing the right tools, platforms, and LLMs is an important step. All that it requires is to ask the right questions about user friendliness, security, privacy, speed, accuracy, scalability and cost effectiveness of the AI model.

4. Move faster, be agile 

An agile approach is a must for AI integration. A healthcare provider might plan a phased introduction of AI for patient data analysis, starting small and gradually expanding. This agile planning ensures to navigate surprises and is less affected by it. The initial phase can be a learning phase working on small sets of users to make it agile, helps to move faster.

5. Start with a pilot project 

It’s wise to test the waters with a small-scale AI project. A retail chain might implement AI in one store to personalise customer recommendations before applying the technology chain-wide. Starting small allows for manageable testing and learning. Always do pilot projects to start with minimising risks. Even tiny errors in the early stage can affect confidence. So keep it small until proven.

6. Stay humble and curious 

Evaluating the outcomes of AI initiatives is vital. A telecommunications company introducing an AI chatbot, for instance, should analyse customer interactions to refine the bot’s responses. Continuous learning and adaptation are key to AI success. Closely observe the user behaviour, AI’s responses - with a clear set of performance indicators. Don’t judge too much on the model’s  hallucinations. AI champions may have an excessive need to prove a point to the stakeholders leading to hiding errors.

7. Encourage wild possibilities 

Innovation is at the heart of AI. A software company encouraging regular brainstorming sessions for new AI applications in software development is an excellent example. This fosters an environment of creativity and innovation, crucial for AI advancement. Connecting ideas and possibilities that otherwise would not have been possible is something that AI can help do.

8. Scale that works 

If an AI application proves successful, consider expanding its use. A financial institution, after seeing success in AI-powered fraud detection for credit card transactions, might extend this technology to other account types. Scale things that work well to more users, bigger data, or even more geographies.

9. Get used  to roller-coaster rides 

Adaptability is essential in the ever-evolving field of AI. An automotive company needs to continuously update its AI models for autonomous vehicle navigation to incorporate the latest safety data and research. So the faster we can navigate the dynamic AI landscape, it offers better rewards.

In the process, encourage non-hierarchy discussions to get the best ideas flowing through. We are on a level playing field, where all players are seeing this for the first time.

Krishna Kumar is CEO of GreenPepper, Innovation Coach and Motivational Speaker. Views are personal, and do not represent the stand of this publication.