When we think of Artificial Intelligence (AI), many people see a distant future with walking, talking androids, massive supercomputers running the show, or perhaps even armies of robots taking over the world. But while this may be the future that Hollywood sees for us, in reality, AI is already playing a huge role in our everyday lives. From image and speech recognition to ride-sharing apps, advanced chatbots, and entertainment platforms like Netflix, it is in businesses where Artificial Intelligence is really making waves. Knowing the right time to start using AI is critical to ensuring its success.
In 2021 alone, the boom in AI applications is stated to add $2.9 trillion in business value and 6.2 billion additional hours of worker productivity. Currently worth around $62 billion, the value of the AI market is predicted to effectively triple in the next half-decade or so, expected to hit a staggering $190 billion in market value by 2025!
However, many businesses are reluctant to implement this Machine Learning-driven tech, unsure if they have the capabilities needed to successfully incorporate AI into their infrastructure.
So, if you’re asking yourself if your business needs an AI solution and “Is my company ready for AI?”, read on as we explore what it takes to put AI in place at your company, looking at how ‘ready’ you are to adopt artificial intelligence into your existing structures.
Perform a Readiness Audit
According to Deloitte Global Human Capital Trends, while 72% of companies view AI as important, a meager 31% of them feel ready to consider it. Implementing AI is a process that requires careful consideration by decision-makers of the business’ fundamental, technical, and organizational needs. AI transformation can be a costly, disruptive exercise, so an in-depth assessment or readiness audit is required before committing to anything.
The readiness audit allows a company to examine:
- If a genuine need exists for your business to have an AI solution
- If the company has the right tools to implement AI
- If the appropriate resources required for implementation are available
- If the right external environment exists for AI to thrive.
Types of Company Readiness
Transformational Readiness
Before a company even starts to think about bringing AI into their organization, the first consideration is how ready your existing company structure is to go through the process of adopting it and if it aligns with the company’s strategic goals and direction. Implementing AI is a transformational process that depends heavily on innovation to ensure successful integration into existing infrastructure. It requires creativity, resourcefulness, and a strong ability to overcome the obstacles bound to pop up during the process.
People tend to be resistant to change. They struggle to trust new things – especially technology – and are often seen actively resisting opportunities to use Artificial Intelligence. When this happens, slow integration and acceptance can often wipe out the advantage intended to be gained through new innovations. People can sometimes feel concerned that AI may replace them or that extensive training may complicate their jobs.
But this trend is changing, with an AI at Work survey showing that 64% of workers are likely to trust Artificial Intelligence more than their boss, while 36% trust AI to answer specific questions and complete workplace tasks better than their managers.
Transformational readiness looks at how prepared a company is to actively accept innovations like AI and the relationship between its benefits and the levels of acceptance among the people who work there. This allows companies to determine if and where AI can reduce costs, improve operations, increase efficiency, or help employees in their day-to-day tasks.
Technical Readiness
Once we have a good idea of how ready a company is to accept AI, the next step is to consider if its existing data and human resources are technically compatible – both practically and legally – with AI. While AI algorithms depend almost exclusively on accurate, well-managed data to fuel their intricate functions, the data is only as good as the people administering it.
Technical readiness assesses a company’s current data infrastructure, alongside the cumulative skills held between the people handling this valuable resource. These capacities can be examined by looking at them separately.
Employee Skill Readiness
One of the biggest hurdles to successful AI integration is the inability of employees to assimilate the new skills and capabilities required to work successfully with Artificial Intelligence. What’s more, businesses are failing at upskilling them. Surveys among executives have found that while four out of five organizations plan to implement AI-related technologies, only 38% actively provide their employees with the necessary reskilling options.
Employee skill readiness is a reliable measure that informs management of how much needs to be invested in upskilling their employees. It also evaluates where workers at risk of being replaced by AI might be re-deployed to.
Data Readiness
Poor, unmanaged data is toxic to AI. Information is what feeds AI; it is the fuel that drives algorithms and allows Machine Learning to function effectively. AI can require a massive amount of data to be input before it can begin learning, though how much data is needed can depend entirely on the complexity and nature of the task at hand.
To ensure that your company is “data ready” before implementing AI, your existing data must be:
- Consistently formatted – AI algorithms cannot generalize as humans can. Typos and vague suggestions that might be obvious to a person can potentially be misinterpreted or ignored by AI. However, Machine Learning technology can identify typos and other human mistakes.
- Kept up to date – Obsolete, irrelevant, or inaccurate data must be deleted or modified and replaced with new data sets on an ongoing basis.
- Complete – From missing values to extraordinary outliers, incomplete data must be accompanied by an explanation for understanding why these missing numbers or strange results exist.
- Stored in a uniform format - With so much data required for AI, standardizing its format will ensure that the system has total access to everything it needs. A searchable database is a good start, allowing you to quantify and tag your data to express it in a format that your AI will understand.
If you’re not on top of your current data, your company may not be ready for Artificial Intelligence just yet. Data should be accurate and properly stored, and the more of it you have, the bigger the impact AI will have on your company. But simply having troves of data to feed into the machine is not enough.
Conversely, if you have little to no data at your disposal or the prospect of collecting new data will break the bank, then your data readiness will not suffice for AI. As many as 48% of companies are now using data analysis, Machine Learning, or specialized tools to address data quality issues, only adding to the scope of resources needed to run their AI.
Organizational Readiness
Where transformational readiness refers to a company’s willingness to go through the process of adopting AI, organizational readiness refers to its ability to do so based on the size of the company, its management style, and other important variables. It is aimed at answering that big question: ”Is my company ready for AI?”
Organizational readiness measures how financially capable the company is of implementing AI and what resources are required to train the AI to the point that it serves its purpose effectively. It tells us if your company is organizationally ready to use Artificial Intelligence.
Evaluating Financial Readiness
Besides the cost of disruption, assimilation, and execution, AI costs money. Once a company has identified a need for AI, it must then ensure that it can afford to implement it. Purchasing an AI solution for your business needs can cost a bundle, not to mention the time taken to install it and train your staff.
Decision-makers must make sure that the cost-benefit ratio for AI is acceptable, and a financial readiness assessment will tell them if it is worth investing in. Besides the cost of maintaining a Machine Learning system, they must also consider the cost of hiring data scientists and software developers.
The ‘Make vs Buy’ Debate
Companies looking to implement AI must also decide between building a custom solution in-house or purchasing a ready-made option. The requirements needed to train AI algorithms, the skills necessary to do so, and reliable data collection and management will drive this decision.
The most pressing issues surrounding organizational readiness are rooted in management’s vision, will to change, the openness with which they approach AI transformation, and whether they believe they have the finances and resources to commit to an AI solution.
Environmental Readiness
External factors play a significant role in how likely a company is to adopt an innovation like AI successfully. Issues such as regulatory frameworks, competition in the market, and the success of Artificial Intelligence applications in different industries are good indicators to a company of whether or not AI is right for them.
Exploring these external factors by looking at the market’s environmental readiness can be a double-edged sword. While a market rife with AI solutions points towards a mature artificial intelligence offering, at the same time, competition might be on the up, driven by the continuing integration of AI itself.
This means that if a company decides to dive in and pursue an AI solution, it needs to consider an integrated strategy that takes these external variables into account while also considering their stakeholders’ needs. From government policies to offering incentives and attractive benefits, integrating AI can be a smooth and lucrative process if executed correctly.
Through extensive planning and examination of these external elements, a company can choose either an in-house custom solution or one built and managed by a third-party vendor.
What is the Best Way to Begin Using AI?
Since mid-2020, AI adoption among companies has skyrocketed. A Harris Poll discovered that 55% of companies reported to have accelerated their AI strategies in 2020 (driven mainly by the COVID pandemic), and over two-thirds of them have continued to ramp up AI integration into 2021.
However, companies need to remember that AI is a long-term solution. That being said, executives must approach integration with careful purpose and a strategic mindset. They must remember that the right time to start using AI is the moment their business is ready to. Once they’ve explored their company’s readiness to embrace AI, the next step is to consider the right strategy to start adopting and using it.
To identify an effective strategy, decision-makers must remain focused on the desired outcomes and benefits. They must also follow a course of action that allows for a smooth transition.
This process looks a little like this:
- Identify opportunities in the company where AI can make a difference. Elements that benefit tremendously from AI include time-consuming or data-heavy tasks and processes that call for collective knowledge and data.
- Then, carry out a cost-benefit analysis to understand if an AI solution is worth investing in for those opportunities. Remember to consider both existing resources and those you will need to pay for.
- Next, build an AI-driven pilot program around one of those opportunities and run it for about four to eight weeks.
- After pilot testing, examine the outcomes. If positive, it’s time to expand and scale the AI solution across the board.
Even when following a step-by-step process, finding your first automation use case is never easy. We are aware of the complications this can pose, so we have put together a guide to help you find a process AI can automate by listing and evaluating the processes happening inside your company.
What is Stopping you From Using AI in Your Company?
AI adoption is still facing resistance from some quarters, driven mainly by uncertainty and a lack of understanding of the potential benefits of AI. The three most significant challenges ranked by Gartner, for companies who are considering implementing AI are:
- A lack of staff skills and poor data (56%) – This is far and away the biggest concern preventing AI adoption. Companies that don’t have the personnel to handle data and manage AI are reluctant to take the plunge. Look at hiring specialists and overhauling your data infrastructure.
- Fear of the unknown (42%) – Many executives simply don’t understand the transformative power of AI, relying on outdated legacy tools instead. These are far more vulnerable to security risks and errors. Demonstrate the benefits of artificial intelligence directly to decision-makers and cite case studies that offer evidence of AI’s potential.
- Finding a starting point (26%) – AI integration begins with identifying a need that an AI solution can solve for a business. Once this need has been clearly defined, a full readiness audit and report will make a compelling case for AI.
Summary
Machine Learning has given Artificial Intelligence the ability to predict future events and process data using information based on massive amounts of existing data. This new era in AI is allowing companies to transform their efficiency and operational capacities at warp speed.
But AI still faces many challenges and obstacles to its adoption, from executives who fail to see the benefits of this transformative tech to employees worried that AI may eventually take their jobs from them.
A comprehensive AI readiness audit will tell a company if they’re ready for AI, what they still need, and when to use Artificial Intelligence. While conducting a readiness audit will inform decision-makers if they have what they need to implement AI and how to plan accordingly, the ultimate test is to check whether a clear benefit can be obtained by doing so.
Is your company ready for AI? If your readiness audit has checked all the boxes, you’ve found the perfect recipe for AI adoption, and overcome those big challenges, then yes – now is the right time to start using AI.
