The last few decades have witnessed the advent of remarkable technologies. Anyone having a cool idea can transform it into reality with the help of technology. It would not be wrong to say that the technology sector is deeply in love with AI. Its wide-range applications including high-end data science and computerized customer services are doing miracles.
Big Tech giants like Amazon, Google, and Microsoft, etc., who adopted AI technology are getting a competitive advantage and enjoying bottom-line benefits from AI strategies. Such enterprises are incredible inspirations for all other companies who want to utilize AI potential to grow into the list of big tech giants but they don't know in what ways they adopt AI to become tech giants. They don't have enough knowledge about the ways of incorporating AI into their business processes. AI adoption by most enterprises is at its early stages. Therefore an in-depth understanding of AI is crucial to get benefits from AI in its full essence. In this blog, we will cover why an enterprise should adopt AI, what barriers a startup has to face for AI adoption and what effective strategies it must follow for successful adoption of AI.
Why Adopt AI?
AI has undoubtedly become the spotlight and the flavor of the decade at tech conferences. CEOs of many tech giants consider AI as the most strategically significant technology essential to achieve business growth and transformation along with competitive strength. The global AI market is predicted to be $126 billion by 2025.
The advancements in AI are attracting businesses to take benefit of the opportunities AI is presenting to them. Companies are now more ambitious towards automating their business processes, transforming customer experience, and differentiating product offerings. From anticipating customer behavior and optimizing the supply chain to customizing the shopping experiences of the customers, AI has great potential to improve the overall processes of an organization. Companies are striving to adopt AI to optimize their output and profitability. A report by Accenture reveals that by 2035 AI could boost labor productivity up to 40% and profitability up to 38%. It is offering the best solution to companies by automating repetitive processes and getting insight into the customer base. This potential of AI is grabbing the attention of many enterprises and they are investing their extensive capital to adopt AI.
What are the Challenges to Adopt AI?
Despite its great potential, AI adoption is lagging behind expectations. At the start of each year, about one-fourth of organizations predict that by the end of the year they would be adopting AI in their infrastructure, however, according to Whit Andrews – a Gartner analyst, the actual figure of the companies adopting AI is significantly less. A report of McKinsey survey reveals that just 20% of AI-powered companies are deploying AI.
AI adoption is not as straightforward as it seems. Where many enterprises are striving hard to formulate a strategy to adopt AI in concurrence with their capabilities and culture, several enterprises don't even know from where they should start and how to advance with it. Furthermore, while striving to adopt AI, enterprises have to face various challenges that they need to overcome so that they emerge as big tech companies. Let's discuss a few of the major challenges:
Limited and Poor Quality Data
Ensuring the quality and quantity of data is very important. A report by O'Reilly revealed in 2019 that the data challenge was the second-highest issue and an obstacle in AI adoption. In the case of AI, the more the data, the better would be the results as it is a data-driven technology. No doubt digitalization has enabled enterprises to get a huge amount of data but still, the data available to startups is nothing when compared with large tech giants. This is because the startups lack the customer base and traffic that could enable them to generate a considerable amount of data for AI integration just like tech giants do. Most of the time, startups have to rely on some public data sets for training data such as "ImageNet". However, this public dataset has succeeded in getting just 14 million images despite the untiring efforts of the professors of the world's renowned universities. Not just the quantity but the quality of data is also poor for these startups. AI needs accurate, categorized, and labeled data that is hard to collect at the initial stages of a business.
Lack of AI Talent
On account of high demand and limited AI experts, big organizations are in a better situation to hire AI experts and pay them a handsome amount. Big tech companies are hiring AI experts that are resulting in a lack of AI talent for startups. These enterprises being at the initial stage can't even pay well to AI professionals so most of the AI talent prefers big tech giants.
Huge Cost to Train AI Models
Another spiking challenge for AI startups is the huge cost associated with artificial intelligence. AI training models like DL (Deep Learning) need considerable time and computation to get trained. Moreover, these models are trained multiple times that incur high costs. As the startups have limited capital, resources, and lack of AI talent so it is hard for them to manage training and computational cost linked with AI. An Alegion survey reveals that almost 81% of respondents consider AI training a far difficult process than the expectations. Therefore, as compared to large organizations, it is difficult for startups to adopt artificial intelligence.
The most underestimated challenge for an organization in adopting AI is the culture of that organization. Most often in an organization, the higher management and the other stakeholders are not on the same page that causes a barrier to the successful adoption of AI.
However, despite all the challenges, it is the right time for companies to initiate adopting AI and ML as such advanced technologies are essential if enterprises want to remain competitive. No doubt, AI implementation differs from those of other IT applications, but overcoming the challenges in AI adoption enables a seamless integration.
AI Adoption Strategies
Several enterprises are undergoing many AI projects that are delivering business value to some extent. But this kind of AI implementation is not enough at a broader scale to generate expected growth.
Just like many other big tech companies who have successfully adopted AI, if a startup or any other enterprise aims to adopt AI, it must approach it strategically. For an organization, the best strategy to adopt AI must entail the following features:
- A long-standing vision
- A detailed and holistic strategy
- Comprehensive but economical tested prototypes
- A few pilot projects to resolve small business difficulties and apply the expertise to find solutions for more complicated tasks.
Let's discuss AI adoption strategies that an enterprise must follow to become a big tech company. Businesses must adopt these strategies to overcome the challenges so that AI can be leveraged in its full essence.
Make Decisions about In-house Development vs Outsourcing
At present, most enterprises have inadequate capabilities to create, implement and manage AI technology in their infrastructure. First of all, an enterprise must identify AI-driven areas like ML, DL, and Robotics. Then it should evaluate the best option and decide whether to create in-house or outsource AI solutions. Organizations must not just evaluate the human resources but also the infrastructure. After that, it must select the AI adoption strategy that is in concurrence with the enterprise capabilities.
Align AI Initiatives with Business Strategy
It is a common blunder that several companies start adopting AI without analyzing their business strategy. They start experimenting with AI before analyzing how it could help them overcome business challenges. They don't even bother to unfold the opportunities that AI could bring for their business. Ideally, a business strategy must be aligned with the AI strategy. An organization must understand the objective of AI for its business and then build a strategy to adopt AI.
Ensure Right Data Access – Metadata Libraries
To embrace AI with its full potential, the most important thing to remember is that an AI-driven solution would be as good as the data used because "Right data fuels Right AI". Another important thing is that all the gathered data is not useful to make accurate predictions. Inadequate and poor-quality data used to train a model can create a troublesome situation for the company as it would be impossible to make accurate predictions based on inadequate and poor data. On the other hand, if AI algorithms are trained on the right data, they can perform amazing activities for example they can see in the form of machine vision, speak in the form of natural language generation, read in the form of natural language processing, walk in the form of autonomous robots, and much more. In short, a comprehensive dataset from credible sources is vital to get the best results.
Businesses today have more access to data but this data is not that good to feed AI applications. There are issues regarding data integrity, complexity, siloes and information architecture, etc. Therefore, businesses have to address these issues strategically and raise a culture of valuing, defining, mapping, classifying, and governing data assets. So to adopt AI, good data is very crucial. If the data we use in the AI algorithm is inappropriate, we cannot achieve AI potential. To access the optimum and appropriate data, companies must have comprehensive "metadata libraries". The metadata catalogs describe data features like its type, ownership, lineage, sensitivity, quality, and much more. This metadata assists in describing and defining data and include technical as well as business details.
Ensure Right Technical Talent Access – Skills and Data-Literacy Strategy
AI has been growing at a rapid pace since the last decade. This rapid growth is creating an AI skill gap. Today, enterprises are competing to grab the best AI talent but the talent lacks in the market. And if it exists, the big tech giants grab that talent, leaving the startups struggling to find AI talent to step forward in the race of big tech giants. According to a survey by Deloitte, 68% of respondents highlighted that the AI skill gap ranges from moderate-to-extreme level. They also believed that AI researchers, data scientists, and software developers are the top talents essentially required to fill this AI skill gap. Here building a strategy about Skills and Data-literacy could be quite useful. This strategy serves as a roadmap to build the relevant teams. The key to implementing this strategy involves upskilling and training the existing employees, improving data and its literacy, and finding out the good partners. Let's have a look at the different approaches of this strategy to get access to the technical talent:
Outsourcing Technical Talent – One of the best ways to prepare a team to overcome challenges in AI adoption is outsourcing technical talent including data scientists, ML engineers, and data consultants.
Training existing Talent – The other way to get AI talent is to train the existing talent of the company through MOOCs (Massive Open Online Courses)
Engaging External Partners –Businesses can engage external partners like IBM with their AI developments to get a mix of expertise. Businesses must be ready to initiate a collaborative partnership with external partners to work side-by-side to co-develop AI. The staff of both organizations must work collectively as a team to gain the necessary insights. As a team, a company must approach its partner in an agile way and work co-operatively to get the full benefits of AI. You must also ensure that your partner is happy to step forward with co-creations and paired developments. At the initial stage, it's fine to build hybrid teams to utilize the talent of the partners, but in the long run, an enterprise must build in-house expertise to coordinate with delivery partners and to manage outcomes.
Invest Time in Modifying Management – Digital Adoption Platform
Deploying an API for a new AI dataset is straightforward. However, training the analysts and engineers and adjusting the management about using AI-driven processes can be delicate. AI normally results in automated binary decisions. But sometimes ML algorithms integration results in more subtle responses. Therefore, before fully adopting AI, the management and other employees must spend time analyzing the results delivered by AI algorithms to interpret the results more accurately. Employees must invest time in analyzing how they can get the best results from the new system. If possible, a company must use a "Digital Adoption Platform", for instance, that of Whatfix. Adoption of this digital solution can be a great investment to speed up user adoption.
Ensure Right Organizational Culture
Progress of an organization always starts from the higher management with good leadership qualities encouraging the adoption of modern technologies. Companies that aim to adopt AI to grow into big tech giants must arrange an executive sponsor such as a Chief Data Officer or Intelligence Officer to develop an AI strategy. Once this chief officer is arranged, the next step is to engage other employees in this mission to achieve growth. Including AI champions can also be very helpful as they can easily engage with techies and business users as well. The entire organization from the top level to the lower level must understand data science and AI. It would be extremely helpful in promoting data governance.
According to a study by McKinsey Global Institute, the companies that adopted AI successfully at a wide level had almost two times greater C-suite support than those without AI adoption. Therefore, a dedicated top-level leader must execute AI transition. He must arrange two conferences per month with the key stakeholders to ensure that everyone is on the same page with a constantly refined role regarding AI adoption status. He must also communicate investment and resources along with the overall strategy to all the stakeholders. This is essential to get the support of everyone for the AI adoption strategy.
Moreover, the organizational culture must ensure that data is being considered and treated as the most significant asset of the business. If everyone in the organization understands the need and significance of high-quality data, it would be an achievement towards getting better data and ultimately better AI to help the business grow like never before.
Ensure Governance and Ethical Frameworks
AI can do miracles, but if misused as in the case of biased and intrusive AI, it could be very harmful. Creating fair AI is a challenge because of training data sets that may favor some features over others. To combat this, an enterprise must eradicate biases from its system while ensuring data privacy. Every data used to train AI must be checked and validated continuously to avoid the algorithm's unintentional favor for one attribute over the other. Hence, an organization must have a governance and ethical framework to ensure the compliance of AI systems with legal requirements. Let's have a look at the features of such an ethical framework:
- An enterprise must have highly accurate unbiased data and use it with compliance and regulations like ISO, GDPR, and HIPAA.
- It must respect the privacy of the users and must not exploit it for political and commercial gains.
- AI Algorithms must be transparent.
- A human, not a robot must hold the responsibility of making critical decisions.
The above-discussed strategies are the foundations for the organizations that are struggling to adopt AI successfully so that they could grow to become big tech companies. Once adopted successfully, AI has the potential to deliver massive ROI (Return on Investment) for the company.
AI is undoubtedly the future of the world, but the fact cannot be denied that AI adoption is quite favorable for large organizations having resources, skilled AI researchers, time, and money to research and implement the latest AI models. They can easily integrate AI algorithms into their cloud platform or software. On the other hand, startups with limited resources and customer base have to strive hard to utilize AI in its true essence because they come up with several barriers that hinder them from successfully adopting AI. For an organization to transform into a big tech giant there is a need to address these roadblocks in its infrastructure and have a strategic outlook for AI adoption.
To join the race of big tech giants, a company must analyze all the challenges and adopt AI strategically. First of all, an enterprise must analyze its capabilities like human resources and infrastructure to decide whether it affords to develop its in-house AI solutions or outsource these solutions. Based on this analysis, it must align its AI initiatives with its business strategy. Then it must understand what type of data AI engineers may require training a model and from what source to collect credible data because data challenge is one of the most crucial challenges for a startup having low traffic and customer base. To overcome the challenge of the AI technical skill gap, it must follow the strategy of outsourcing AI talent and building hybrid teams with AI partners or upskilling its existing employees. Besides getting technical talent, a sort of non-technical talent is also needed. It means that everyone in the organization from top-level to lower level must be on board. The company's managers and creative heads must play their role in bringing together every stakeholder on the same page to resolve the complications regarding successful AI adoption. Hence, for the company-wide adoption of AI, top-level executives must be engaged with other stakeholders. Last but not least, an enterprise must counterpart AI with appropriate governance and ethics.
In a nutshell, the successful adoption of AI is a hard task for startups and is challenging at the same time. However, by adopting AI strategically, a company can lay the foundations for its future growth to become a big tech company.