By enabling businesses to automate operations, develop creative products and services, and improve consumer experiences, AI is transforming the world of business. The use of AI has rapidly accelerated in recent years thanks to the emergence of conversational AI applications like ChatGPT. Nevertheless, there are a tonne of untapped possibilities for developing AI applications that can grow and quicken throughout a complete organisation.
Nowadays, enormous amounts of data are being gathered, and there is tremendous compute power available to extract value. Through insights and forecasts, the development of technology in large language models (LLMs), machine learning (ML), and data science may truly alter enterprises.
The immense power of AI, however, comes with great responsibility, just like anything else. Many AI initiatives and projects won’t yield corporate benefit if a careful strategy isn’t taken. Making an AI strategy is a wise move to ensure that your initial projects provide commercial value and spur further expansion and investment.
We’ll examine the factors that contribute to the failure of many AI plans, consider the advantages of developing an effective AI strategy, and then provide a step-by-step manual to assist your company in developing a successful AI strategy.
Initiatives in AI and ML without a strategy often fail, although not necessarily in the same way. While some failures are swift and spectacular, the most expensive failures are those that take a long time to develop. Here are a few typical failure modes that we’ve observed.
Investment is necessary for any AI projects. Before making large development investments, it is critical to determine the prospective business value of the solution in order to support that investment. Teams that are enthusiastically developing solutions have been known to get ahead of themselves and underestimate the value of those solutions.
The inspiration for many ML projects comes from outside sources. A person may want to implement a great image-processing programme they saw at a trade event, for instance, within their company. However, originality does not always imply that a solution is appropriate for the issues your organisation is facing. Try to picture how you would resolve that issue without using machine learning before constructing a solution. Does your company still find it interesting to tackle this issue?
WISE TIP : Consider how you might resolve that issue without the use of machine learning (ML) before coming up with a solution.
Solutions must be implemented in a production-grade environment in order for AI to be operationalized. In other words, software projects are really what AI projects are, only more complicated. The infrastructure or organisational support needed to move data scientists‘ innovations into production is frequently lacking.
Make sure you have the platform and teams in place to advance the solution if you’re creating an ML solution to address an AI problem.
AI initiatives frequently demand a lot of research and development work. Research is only worthwhile if it can be replicated. Are your data scientists working in isolated settings (worse case: on their laptops) and not versioning their code and outputs in a single repository? If so, a significant chance exists that their efforts will be ineffective. That work is immediately gone if those data scientists change careers.
Reproducibility is crucial for transferring solutions to engineering and operations teams when the time comes to put those models to production. To guarantee data scientists are working in a reproducible manner, be sure to identify best practices and offer infrastructure. The first step is to utilise a Model Registry.
Sometimes ML technologies achieve operationalization but fall short of reaching production-grade status because of a lack of automation. Models need to be updated and deployed repeatedly. These systems will eventually be too labour-intensive to maintain if teams manually retrain models and deploy artefacts. Make sure your MLOps stack incorporates the ideas of DevOps automation.
The value that an ML solution brings to your company can only be quantified. You won’t be able to report on the effectiveness of your solutions and defend your investments without monitoring and observability. Furthermore, you won’t have the knowledge required to enhance the solution or address problems as they develop. Make sure to construct your solution so that monitoring, alerting, reporting, and assessment are possible.
After reading about all the potential project failure scenarios, it should be obvious why a strategic approach is necessary. Here are a few reasons why developing an AI strategy is beneficial for your business or organisation’s AI projects.
Huge amounts of data may be analysed by AI, which can then offer insightful data analysis that aids in decision-making for organisations.
AI can lower operating costs, boost efficiency, and optimise resource allocation by automating tasks and procedures.
Better customer service may be delivered with the help of chatbots and AI-driven personalisation, increasing client loyalty and happiness.
By providing cutting-edge goods, services, and client encounters, a well-executed AI strategy can differentiate a company from rivals.
Employees have the ability to concentrate on more innovative and strategic work thanks to the automation of repetitive operations, which increases productivity overall.
With the help of AI, businesses can foresee trends, consumer behaviour, and market developments and respond to them in advance.
With the help of AI, organisations may identify possible hazards and security concerns and take steps to reduce them before they have serious consequences.
AI can give workers the resources and knowledge they need to flourish in their jobs, promoting a culture of ongoing learning and development.
According to MIT Sloan statistics on artificial intelligence, 75% of top executives think that AI will help their company expand and gain a competitive advantage.
Artificial intelligence is actually taking over the globe, according to the data. To gain a clearer idea of the impact of AI on organisations and people, let’s look more closely at each of those statistics.
Today, more and more companies are realising that they have little chance of expanding without AI. The world really is moving very quickly. Businesses use a variety of forms of automation to stay up with it. Companies have the chance to find solutions that are ideal for them thanks to the wide range of AI-powered tools available, including voice assistants (like Amazon’s Alexa, Google Assistant, or Siri), chatbot platforms, email automation services, visual AI, and more.
There is no doubt that the number of people using AI systems will increase. Nine out of ten businesses already make continuing investments in artificial intelligence, and they will continue to do so as time goes on. The time to join them is probably at this point.
The market for artificial intelligence is expanding rapidly. In 2023, it is predicted that the worldwide AI market would grow to $500 billion.
It’s hardly surprising that the market is expanding so quickly given that more and more companies are implementing AI solutions. The widespread adoption of cloud-based services, AI virtual assistants, and conversational AI is one of the factors behind such growth rates.
The value of providing outstanding customer service is also rising. 96% of consumers reportedly consider customer service when deciding whether to remain loyal to a firm. Consequently, AI-based consumer engagement solutions are becoming more and more popular.
The market share of AI seems unlikely to stop expanding in the near future. It continuously creates new chances to add value and provide fresh remedies for various problems.
Many people still think of artificial intelligence as something from a science-fiction film. Nevertheless, artificial intelligence is increasingly becoming accepted and trusted globally. Currently, less than half of people accept AI, only about 10% say they do not, and about a third say they do.
Researching is the first step in developing a valuable AI strategy. You will gather data about your business throughout this phase, which will help you make strategic choices. Don’t let anyone convince you that the discovery phase is dull; on the contrary, it can be the most stimulating and fascinating. Making a thorough finding will also lay a solid groundwork for everything else. Organisational discovery and use-case discovery are the two types of discovery activities.
Organisational exploration identifies the specifics that distinguish your organisation. Which corporate goals are most crucial for the upcoming year and beyond? Selecting use cases will be aided by knowing corporate priorities. How big is your IT department, and what level of technical expertise do you have? This knowledge will be used to choose technologies. And how will businesses create and maintain AI solutions? It is critical to know if business units or central IT will be responsible for ownership of solutions.
The discovery process really picks up during use-case research, where you’ll actually be outlining concepts for prospective AI solutions. Interviews with business units will be used to acquire a lot of the data. You should pinpoint the most significant issues and areas that are bothering you that AI can help with in these interviews. Then, data scientists can find issues that AI can tackle.
Customer Attrition Prediction in Sales and Marketing – based on previous data and customer factors, prediction of which customers are most likely to churn.
Creating Marketing Content – For creating unstructured natural language for marketing or sales, large language models and generative AI tools like ChatGPT excel.
Inventory or Demand Prediction – on the basis of past patterns, seasonality, unique events, etc., predicting future demand or supply.
To assess the viability of the solution, crucial questions must be answered for each use case. Make sure you comprehend the following information for each use case:
AI solutions don’t magically appear in production out of thin air in the lab. Some essential infrastructure parts are needed to enable data science and ML engineering teams as they operationalize solutions. It is crucial to ensure that your organisation’s architecture makes sense.
The following phase’s technology selection will be aided by having a clear reference architecture. Additionally, it will ensure that you don’t omit any MLOps cycle steps that would prohibit you from developing comprehensive solutions.
The various diagrams and documentation that make up a reference architecture offer various perspectives on your system. Similar to zooming in and out on a map, having several perspectives aids audiences from various backgrounds in understanding the architecture. We advise that a reference architecture contain at the very least these components:
A diagram or paper that details the capabilities of the system should be part of your architecture. This viewpoint aids in describing the value offered by the platform rather than concentrating on particular technological decisions. Depending on the use cases and skill level that exist within your organisation, the included capabilities will vary. Explicitly displaying capabilities will aid in gaining support for your platform.
To enable capabilities, innovations are essential. Your technological decisions should be backed up by written justifications that include any alternatives that were thought of but ultimately rejected.
The secret to agility and scale is common development practices. Outline DevOps and MLOps practises for standardised pipelines and automation to assist your organisation in achieving recurring results. This entails putting boundaries in place for the development, monitoring, and promotion of models for use in AI and ML.
The demands placed on AI and ML vary across different businesses. Data use is strictly regulated in some circumstances, such as HIPAA in the healthcare industry, and certain criteria must be satisfied. A right to justification for any automated decision-making in credit reporting is one example of how ML forecasts themselves are subject to particular rules in other fields.
How Comprehensive Should a Reference Architecture Be?
Your reference architecture should be extremely comprehensive and, to some extent, aspirational. It’s possible that not every component of the reference design will be used in your initial projects and use cases. You might be able to avoid some steps in these early endeavours in order to provide a solution rapidly. However, having a reference architecture will ensure that you can finish the relay even if you have to navigate a few obstacles.
You should consider a few essential elements while considering AI capabilities and technologies:
The strengths and weaknesses of your organisation should be tied into these elements as well. Which of these elements would it be wise to develop and maintain internally? Which would be more advantageous to buy or have supported as SaaS?
The AI and ML market is becoming more saturated and fragmented. Every day, new, amazing items enter the market, but not every one of them is a good fit for your company. Make sure to choose vendors who can effectively address the strengths and shortcomings of your company.
During this phase, your reference architecture will serve as a beacon of guidance. You should make a list of potential tools and vendors for each component in your reference architecture. When evaluating each, consider the following:
Purchasing these tools should be a top priority as well. What tools are going to be required for the initial stages of your projects? Which will eventually be required? Making sure your architecture is built out in an organised manner can save you time and work later on when you need to buy and integrate tools.
No company is fully ready to launch its initial AI projects. There may probably be gaps in specific skill sets and disciplines within your organisation. For instance, some businesses have excellent IT and software departments but are lacking in machine learning and statistical modelling. While some organisations excel at science and R&D, they lack the engineering know-how or operational experience necessary to properly implement ideas.
As a choice, you can prefer to outsource some of the process’s steps. Perhaps it makes sense to do R&D within your company so that you can control your intellectual property, but to contract out the deployment and operations to an outside company. Naturally, companies with strong engineering and IT teams can want to achieve the exact opposite!
A Statement About Automation
Numerous AI solutions aim to automate ineffective work duties or tasks within your organisation. Of course, you’ll want to take those cost reductions into account when planning. However, be mindful of how your solution can affect the organisation’s structure and its employees. Will there be job losses? Which departments will be impacted, exactly?
It’s time to create a roadmap now that all the necessary data for your AI projects has been acquired in the earlier steps. To show business value and support both current and future investments, you should create a plan that gives quick wins first priority.
Each stage of your roadmap will have a price tag. Make precise calculations of these expenses and explicitly state when they will be incurred. The continuation of investment in AI ventures will depend on the transparency and honesty of cost presentations.
Create your roadmap by prioritising the following tasks:
Your roadmap should begin to take shape as a result of the decisions you make above. Make sure to list the teams that are involved at each stage of the roadmap, along with a clear description of their roles and responsibilities. Make sure the appropriate stakeholders are identified because the roadmap will eventually be used as a communication tool.
Swiftness and repetition
Although road maps are crucial, nothing is set in stone with regard to timing and location. Your projects will be dependent on questions that you haven’t yet resolved, even with a clear approach. These include some research-related inquiries. Make sure you incorporate the right feedback methods into your roadmap design, and be specific about when you might need to pivot.
Although developing an AI strategy is a very satisfying exercise, it is only the first step. Presenting your strategy to management will ensure that everyone is on board with your approach and assist you avoid making mistakes and squandering money. It will be fantastic to celebrate your endeavour at this moment and envision a world powered by AI!
We truly believe that this guidance will assist you in advancing AI activities at your company. Although some of the processes may need to be customised based on your expertise and particular objectives, the general framework ought to hold true for most firms.