AI: Seeking clarity within the buzz

Practical advice on shaping your thinking and strategy when stakeholders and investors ask how you’ll capitalise on AI.

The spotlight is shining bright on AI. Tech giants have raced to launch products to customers, entrepreneurs are launching AI ventures to tackle problems and disrupt industries, what is your business doing about it?

There are so many unanswered questions around AI that it can feel overwhelming, how it’ll shape jobs in the future, how it’ll shape the customer experience and expectations, at what pace it can or should be allowed to progress, moral and legal implications, unpredictable repercussions, and how to avoid being left behind.

If your business is facing some of these questions, how prepared are you to respond? Whether it’s your CEO, stakeholders or investors, they will be expecting clear and thought-out answers.

So, “what is your business planning to do about AI?”

Strategic questions can be challenging. Sometimes ambiguous or loaded with subtext, they can be time-sensitive or touch on confidential information, and without sufficient time to research, can easily create a feeling of pressure.

Take time to pause and consider the context, the audience perspectives to cater to as well as how personal biases might impact a response.

New innovations and trends tend to go through phases, and having awareness of where in that cycle we are may relieve the pressure to respond rashly, as well as set the context from which you build out a strategy for how your business acts on them.

We’re in the early growth phase of the applications of AI. ChatGPT and other tools have created excitement, but these are early experiments designed to test what sticks and how people use them. There is plenty of room for innovation and disruption, and investment to match.

The imagination of investors, entrepreneurs and the general public is being captured with the promises of revolutionising daily life, but perhaps we’ve seen this before. Many are sceptical and have drawn comparisons to the dot com era of the late 1990s, could this be a bubble waiting to burst?

As you explore the potential for AI in your business, it’s crucial to balance enthusiasm with thoughtful consideration. Whilst many new ventures are springing up to capitalize on the trend, there will be consolidation and fall-out before standard practices emerge for the utilisation of AI in business.

Taking a balanced view of AI’s potential

With great promise for the applications of AI, there are also an equal number of far-reaching challenges that should be taken into consideration.

Looking at both some of the most common benefits and drawbacks at present, you may begin to see direct links between creating efficiencies and job displacement, as well as rising ethics and safety concerns as human interaction becomes reduced in public-facing services:

Pros

• Efficiency: Perform tasks faster, more accurately and save both time and resources by automating repetitive work and processes.
• Decision-making: Analyse and identify patterns in vast amounts of data that can be used in forecasting and better-informed decision-making.
• Personalisation: Contribute to greater customer satisfaction by learning from their preferences and behaviours to provide personalised experiences, recommendations, and targeted content and services.
• Automation: Free up your team to work on higher value and more creative tasks by automating complex and effort-intensive processes.
• Availability: Expand your reach to larger audiences, at times that it is convenient for them; with fewer delays or constraints, automation can relieve the complexity of staff capacity and shift planning challenges.

Cons

• Job displacement: The potential for automation to replace jobs that involve routine tasks may lead to direct unemployment, a change in roles and responsibilities or the need for employees to retrain.
• Cost of creation: Large-scale projects to develop and operate AI-powered systems can be costly and require specialist teams that may be unavailable to smaller businesses.
• Bias and ethics: Prejudices and biases can be perpetuated or amplified by AI-powered systems. These systems are only as good as the data that they are trained on, and the ethics of organisations providing services.
• Lack of human judgement and empathy: There are many scenarios where human emotions and interactions are essential. AI currently lacks the human qualities of empathy, intuition and morals.
• Security and privacy risks: Many systems are built on large quantities of sensitive or personal data which raises significant privacy concerns, protecting this data from unauthorised users is critical.

Formulating your business approach to AI

To show strategic thinking, adaptability, and the ability to capitalize on opportunities there are some key areas that you need to form views on. Having an understanding and considered approach towards emerging trends is likely to build more confidence than rushing to adopt them.

Market awareness

Monitor developments on how AI is being adopted by your competitors to understand your market position better and gain inspiration. There are great blogs and podcasts available on the uses of AI and the potential for transformation across various industries, start by reading and listening.

Innovation and differentiation

Start to think about creative uses of AI that may create a competitive advantage for your business. Run workshops to look at your business through the lens of AI and see what ideas emerge. Investors are keen to see innovation and how businesses differentiate from their competitors.

Business relevance

Not every business will benefit from AI. It’s worth understanding the value that it could add to your business model, products, and how services are delivered to customers. Show your stakeholders that you’ve looked at your vision and overall strategy to see how implementing AI could align.

Scalability and growth potential

Could the potential scalability and growth of your business be enhanced by automating repetitive and complex tasks? Have you looked at where you’re constrained by headcount growth to deliver services to customers? Consider the role AI could play in increasing your business scalability.

Team capabilities

Data scientists and machine learning engineers are a core part of any team looking to build great products with AI. Understand what capabilities you might already have in-house, whether you need to build a team or partner with specialists, and what funding that might require.

Execution plan

Planning to build with AI? Formulate a plan. Define the business requirements, how you’ll execute and implement new ideas, changes needed to your operations, and how you’ll measure success. If you’re seeking investment, a clear plan will help you stand out amongst entrepreneurs looking to cash in on the buzz.

Being realistic about strategic fit and readiness

Does AI enable your strategic direction and priorities?

Is it an inspiration or a distraction? Should it be a core part of your business or something that you utilise within tools? Look back at your strategy before getting carried away with competitors and AI trends.

Ask yourself the key questions:

• Will AI enable your goals and strategic direction?
• Will it add value to the business proposition?
• Will it enable efficiencies, or add to the customer experience?
• How does this rank amongst other priorities?
• Can you predict what return investing in AI would generate?

Some businesses have AI at their core, working with large datasets to provide high-technology services. In the UK, Mosaic TX uses ML and AI in computational genomics to develop new treatments for cancer, and Wayve builds AI software that operates using onboard sensors as well as fleet metrics for autonomous vehicles that can drive anywhere.

For many businesses this level of investment in AI may not be necessary, instead coming as advances to the everyday tools already used to deliver services to your customers.

Is your business ready to leverage AI?

Start by having clear use cases of where AI would add value back to your business and customers. This might involve conducting an assessment of the business needs across departments, understanding how the use of AI can solve those needs, and defining clear objectives and measurements.

Automating tasks or creating efficiencies within your business requires having well-defined and established business processes in place to build upon. Having stability and clarity in your operations will make it easier to identify existing processes and repetitive tasks that would benefit from automation.

Machine learning and AI requires training on large quantities of data to be able to make predictions or provide decision-making support; if you have a large dataset on your operational or customer behaviours, or can acquire data related to your industry then you may have a good foundation.

Researching and implementing AI is both a financial and time investment. Ensure that your key stakeholders are available to support the project in being successful. Hiring specialists is a big decision, if you don’t have data scientists or machine learning engineers in-house it may be more cost-effective in the short term to leverage partners for proof of concepts.

How could AI transform the nature of work in your business?

Some people are naturally concerned about what AI will mean for their roles, whilst others are embracing and experimenting with AI tools in their workflow. At the very least, AI seems to have a place in jump-starting work.

It’ll be some time before tools emerge that benefit all fields of work, but if you want to get ahead of the wave of change and invest in your people, reflect on which capabilities make your business successful, and how they might be affected by AI.

What types of work can be automated or made more efficient? These could form a basis to redefine the work that your team focuses on, predict how team shapes might evolve, and consider the key areas to invest in as new tools become available.

With AI tools in their hands, what will your team’s roles look like, how can you start planning to cultivate those skills and mindsets over time?

Final thoughts

If you do take the plunge into AI, start small and be willing to fail fast; focus on one business outcome that you have a dataset for as a proof of concept. Use this to shake out any challenges within the business and provide great learning to build on, including better insight into the costs of researching, implementing and operating AI projects.