Integrating AI for Long-Term Value: A Strategic Guide for Executives

Finding the Right AI Use Cases for Your Business

There’s been a lot of discussion and success stories of different organisations leveraging AI and generative AI to automate functions, unlock efficiencies, and discover more intelligent ways of working.

There have been some incredible examples like China-based video game company, NetDragon Websoft, which appointed an AI program, Tang Yu as its CEO in August last year. Or Polish luxury drinks company, Dictador, which implemented an AI algorithm named Mika as its CEO and board member.?

While giving an algorithm to a company’s top position may seem extreme, NetDragon Websoft’s shares rose 10% in the six months following Tang Yu’s appointment, bringing the company’s valuation to over $1 billion.?

While not every business is equipped to make such a massive move like NetDragon Websoft or Dictador, AI is transforming the ways people work, communicate, and innovate.?

I see AI as an umbrella capability framework that contains many tools that?humans can use like deep learning, machine?learning, knowledge-based systems, natural language processing, computer vision, GenAI, and recommendation systems. But how does one know?which tool or combination?of tools to use??

The aim of this blog is to help organisations properly select and use the right tools as a long-term solution. So, let’s take a closer look at how organisations can integrate AI seamlessly, building the right use cases, resources, and support systems to ensure these intelligent technologies deliver value and performance.?

Key Strategic Considerations

Firstly, it’s important to recognise that every business has unique challenges, needs, and contexts that determine where AI can help. It’s important to delve deep into underlying issues, explore various options, and even enlist external help to get a good sense of what’s out there and how it can help you achieve your goals.?

Finding the right areas for leveraging an AI solution requires well-defined criteria and a shared understanding of how AI can drive value. This often boils down to two key elements that determine the right choice: identification and prioritisation.?

Begin by examining your company's overarching strategic priorities, outlining your goals for the?next 3-5 years. You may find different use cases depending on whether your strategic focus is about addressing current challenges (e.g., reducing production costs) or exploring new opportunities (e.g., entering new markets).

The early assessment stage is also a good opportunity to identify the business areas with the highest ROI potential and see if they align with your company's overarching strategy. Understanding which strategic goals can deliver strong returns helps create a strong narrative that stakeholders can latch onto, thereby encouraging stakeholder buy-in while making it easier to optimise resources, accelerates adoption, and ensures that?implementation and innovation are collective efforts rather than siloed processes.?

While identifying strategic goals requires a top-down view, the next step reverses that approach, taking a bottom-up perspective to pinpoint bottlenecks in your daily processes and workflows. Most organisations focus on slow, inefficient, or error-prone processes?that involve highly repetitive tasks. However, another key focus may be on key operations that are primed for the next stage of innovation. Successful AI implementations isn’t just about choosing the best suited use cases but understanding how the technology’s capabilities and limitations and enable your business.?

This means staying abreast of industry trends and successful AI implementations, within and outside your industry. It’s important you can distinguish between effective ROI and mere marketing stunts, but the key is seeking out insights and inspiration from anywhere, including industry conferences, external experts, and even industry competitors.?

Preparing Your Business for AI

Data is what fuels intelligent technologies like AI and GenAI, which is why an important first step for any business is to assess the quality and accessibility of their data. The adage, ‘rubbish in, rubbish out’ is true for data and its subsequent AI outputs, so it’s vital that your team has access to top quality data to ensure the quality of any AI solution and its efficacy.

Often, organisations are managing disparate datasets across numerous databases and software systems, which can present serious challenges when it comes to compatibility and access. Consolidating these data insights to create a consistency is paramount. It’s also useful to look at tools that can automate data transformation and enrichment, freeing up time and resources early.

Considering the speed and scale of change we’re seeing within AI, it’s also critical that businesses keep their models and applications up to date. This should be an automated process otherwise significant time and resources will be wasted on manual updates, which can really hinder the ROI of any AI investment.?

Another important aspect of finding the right AI solution is understanding how much flexibility and scalability are built into the application. Business needs can change and evolve, requiring the integration or deletion of specific data sources, software, or variables. That’s why it’s all about finding an AI solution that can manage multiple AI use cases or machine learning flows from one and the same environment.?

By ensuring that connections with other software or databases can easily be reused, organisations can easily scale and adapt their AI solutions to suit changing needs. One easy workaround for this is choosing software that has an open API architecture, which makes integration more seamless and efficient as they don’t require customised development, providing a more ‘plug and play’ model.

A good AI solution provider should offer ongoing support via an intuitive and readily accessible help desk. They should also be transparent when it comes to pricing. For organisations, it’s useful to map out all requirements early including any expected future costs like model updates, adding data sources, and expert advice/consultations, which are often free but can sometimes add to the cost of implementation.

Ensuring Seamless Integration

Successful AI integration requires cross-functional collaboration, involving all key stakeholders early and communicating with them clearly and regularly about the benefits of the new solution. This helps secure buy-in from leadership and stakeholders, ensuring a seamless adoption process that caters to the different perspectives and varying needs across the business.?

Another important consideration is the impact of AI use cases on the team. It’s important to help them understand how the technology will affect their daily workflows including the ways it will improve their efficiency and any potential skills they may need to develop to use AI effectively. Understanding these factors can help you address potential resistance and ensure employees see the AI project as a beneficial tool rather than a threat or disruption.

Ensuring clear strategic alignment means the AI project directly contributes to your business objectives, adding genuine value to your operations. This is furthered by a clear understanding of the potential savings or gains an AI solution can deliver, justifying the investment made with measurable ROI.

AI implementation can be a challenging and time-consuming process, which is why its important to understand the feasibility of the project and the capability’s ongoing sustainment. This means assessing the technical requirements plus the data availability, and quality needed for AI use cases. It’s also about accounting for the complexity of the use case and whether your business can execute it repeatedly.?

This point then leads to another consideration about AI’s integration with the business operating model. One of the operating model considerations is whether AI is operated in a single function (e.g. Centre of Excellence), hub and spoke model or is it fully federated across the business units. Similar to what the industry has seen with cloud adoption, the sustainment of this new capability will be heavily dependent on how well it is integrated into the business operating model.???

This may involve factors such as the need for technical know-how or domain expertise, or potential disruptions to existing operations. However, it’s also important to address current needs with space for potential future development, finding AI solutions capable of growing and evolving alongside your business strategy and goals.?

Next Steps

The ongoing discussion around AI has been fascinating to watch – so much so that I’m currently studying a Graduate Certificate in Applied AI to understand the technology?better.?From what I’ve learned, most people?see AI as an easy-to-use cloud solution like they have experienced with other?cloud?offerings like social media and personal productivity tools. However, they’re often unaware of the numerous considerations they should think about with AI.?

What I’ve outlined here is just the tip of the iceberg – there are also major challenges when it comes to data privacy and cybersecurity as AI requires vast amounts of data to operate. There are so many opportunities for this evolving to be misused due to people’s inexperience or lack of understanding.?

Furthermore, AI is providing opportunities for malicious actors to work more efficiently, streamlining the development harmful codes and algorithms that are growing more human like and therefore more deceptive and impactful.?

There are real risks and limitations when it comes to AI, but it’s important to remain ahead of industry trends, always learning about the latest developments and solutions that will transform our ways of working.

Overall, I’m excited for the future of AI. As the technology becomes more intelligent, capable, and accessible, people will find creative ways to leverage it. While GenAI dominates the discussion due to its creative outputs, each AI solution has its own benefits that can greatly help people and businesses work faster and more effectively, however, finding the right solution takes a lot of self-reflection, understanding, and research to get right.?