Starting AI in Enterprise
“Over last 50 years half of the economic growth has come from innovation.”
Getting started in moving beyond personal exploration of AI and into the everyday workspace is going to be complex. It is a change programme, and an innovation programme combined. Neither make for straightforward activities.
Here are things to consider.
It’s all about the people
Firstly there is getting over the fear, uncertainty, and doubt questions.
Will it end the world? What does it mean to my job? To everyone's jobs?
While these are legitimate concerns, the narrative surrounding AI is often fueled by misunderstanding. Education and awareness-raising are crucial steps in the change management process. Without a well-informed team, any AI initiative is doomed from the outset.
Then there is finding the explorers in your organisation who want to innovate and lead change. Identifying these individuals is more than a talent hunt; it's an investment in the future. These explorers will serve as the pioneers who navigate the complex terrain of AI and offer insights that can shape strategic decisions.
Then there is giving them time, opportunity, and space to learn these new skills and techniques. The organization must support these individuals, both in terms of resources and a conducive learning environment. Without proper backing, even the most eager explorers will face unnecessary barriers.
For context, even if there was 100,000 skilled Prompt Engineers 3.0 with 1.8 million medium-sized businesses in the UK, that’s less than 1 hour of resource, per business per week.
A safe place to try
Innovation and change need a safe place to try. To prove its worth, it needs to have an impact.
This is a CEO moment.
High impact and safe to fail are not going to coincide in most businesses. It is crucial for upper management to understand that innovation is inherently risky but carries the potential for high rewards.
Unless that business is broken, in which case rolling the dice to save it has been tried many times in digital transformations as a last-ditch attempt. They fail. Don't do that; maybe implement AI into some of the 'C-suite' decision-making to help out. Struggling businesses might see AI as a lifesaver, but without a proper foundation, this advanced technology will quickly turn into a complex problem.
Pick a safe place, a safe task, a mundane task, a task that has repetitive volume that no one likes doing, or is done poorly or needs predictive outcomes.
Identifying the right task can set the tone for your AI venture and offer quick wins that boost organizational morale.
Pick a solution that is mainly done outside of the enterprise systems, Excel-based, user systems. Avoiding system change is good for exploring. This approach minimizes operational disruptions and allows for a smoother transition, offering the business an opportunity to adapt gradually to this new technology.
How about customer feedback sentiment analysis? Sentiment analysis offers a low-risk entry point for many organizations. It involves minimal changes to existing workflows and provides immediate benefits by enhancing customer relationship management.
Measure where you are now
Document and measure the current process.
You need to know effort to do (realistic, take a measurement), you need to know accuracy rates, success and fail rates, and also frequency. Frequency may be low because it's hard to do. Without an in-depth understanding of the current state of affairs, any change initiative will be a shot in the dark. Analytics serve as the North Star, guiding the organization in its quest for operational excellence.
The AI unlock might be in making it able to be done, when before it was not possible. AI technologies like machine learning and natural language processing can unlock new possibilities, from automating repetitive tasks to gaining insights from large data sets that were previously impractical to analyse.
Start
Use what you know, use what you have, and where you are.
Launching an AI project doesn't require a massive investment or a complete organizational overhaul. Small, incremental changes can yield significant results over time. The key is to start with a well-defined problem and a feasible solution, making adjustments along the way based on empirical evidence.
Measure it. Write it down.
Documentation is not a bureaucratic requirement but a necessity for long-term success. It offers a historical record of what has been tried, what has worked, and what needs improvement, offering future initiatives a strong foundation on which to build.
Be critical. Is it working? Has it increased value for everyone, for good?
Conduct regular reviews to assess the effectiveness of your AI initiative. If it's not delivering as expected, don't hesitate to recalibrate or even terminate the project. Wasting resources on a failing project helps no one.
End the Experiment
Know when to call it, this is an experiment. It is not implementation.
Do not implement experiments into customer production, even if it is tempting. Just take a moment to industrialize. Realize that a successful experiment doesn't automatically translate into a successful implementation. Scaling involves its own set of challenges that need thoughtful planning and execution. Know when to move from the experimental phase to full-scale implementation, ensuring that the organization is fully prepared for this transformative step.
Being the Leader
Embracing Artificial Intelligence is a visionary move, promising to elevate business capabilities to unprecedented levels. While it's a journey that requires thoughtful planning and commitment, the payoff in efficiencies and insights can be extraordinarily rewarding. It's a landscape rich with untapped potential, offering organizations the chance to be trailblazers in leveraging technology for sustainable growth. So, while the first step in any transformative journey is significant, it's also filled with promise. By approaching it with a well-considered strategy and an eagerness to adapt, the opportunities are limitless.