Aug 04

Enterprise AI adoption: Top challenges and solutions to overcome them 

Over the last decade, artificial intelligence (AI) has been revolutionizing how businesses work. There is an increasing number of businesses that are ready to invest in AI to evolve their products and operations. According to McKinsey’s ‘The State of AI in 2020’, 50% of businesses have already been adopting AI in at least one operational function. A 2019 Accenture's Annual Report states that 75% of global executives believe they risk going out of business in 5 years if they don't scale AI. 

A lot of organizations have successfully adopted AI and transformed: from giants such as Amazon, Google, or Netflix; to small and medium companies. However, implementing AI is no easy task. There are many challenges which businesses have to face and find solutions to when it comes to AI deployment, including different operational aspects such as technology, production, human resources, and work culture. 

Successful adoption of AI for your business requires a good understanding of the major challenges involved and a concrete plan of potential solutions to deal with them. 

A brief guide to getting going with AI adoption 

The construction of enterprise AI requires businesses to build their AI systems at an organizational level. Your enterprise AI system should include data, algorithms and other technologies needed for the sustainability of the system.  

Basic requirements for kick-starting an enterprise AI system are: 

  • A large number of datasets, a collection of high-quality, relevant data from all relevant areas of your business 
  • A reliable database to store your collected raw data 
  • A trustworthy infrastructure to develop your algorithms and deploy your AI models 
  • A team with qualified experts including machine learning (ML) engineers, data engineers, data scientists, and data analysts who work together to build the AI model 

However, ticking all these boxes is only step one. The are several challenges which companies must face and find solutions to achieve a successful journey with AI. 

Hand taps on geometric brain corporate background

Lack of high-quality data 

AI and ML technologies support businesses with managing and utilizing massive amounts of data. Yet, the possibility for these technologies to create outstanding results still depend on other crucial factors such as computing system, as well as the quantity and quality of training data given.  

Training data is the foundation of AI projects. AI models require access to an adequate amount of high-quality training data in order to be trained and built. More and better data can guarantee improved outcomes for your AI models. Nevertheless, it is important to note that merely possessing a massive collection of data does not secure the usefulness of it. With data, especially for enterprises, quantity and quality must go together. 

However, it can be incredibly challenging to acquire accurate and consistent data. A McKinsey survey points out that the most significant barrier for businesses in their AI implementation process is the shortage of insightful, usable and relevant data: among 100 businesses that have taken AI initiatives as part of their operations, 24 have been facing this roadblock. 

Numerous businesses have, in fact, had to delay or stop in the middle of the AI-implementation process due to lack of appropriate data. Insufficiency of quality data can lead to poor insights and forecasts, despite heavy investment in data infrastructure and management systems. The lack of useful data, therefore, can leave businesses incapable of training their AI algorithms. 

To tackle this foremost barrier, businesses should employ a strong data quality management tactic to safeguard data generating process. All data generated within the business should be gathered, processed, and stored correctly, securely and efficiently. Businesses can consider establishing a centralized storage system to consolidate smooth workflow between various models. 

Regarding data collection, starting with the section where there is the most useful data can be a safe choice for enterprises. 

Businesses should also focus on implementing a future-forward approach, such as plans of integrating and scaling data. This helps to make sure that in the future, integrating data from numerous new sources does not become a roadblock. 

a large amount of data in dataset network

Lack of skill sets needed  

After solving the problem to do with data, organizations then need to see if they have the required skills to move along with their AI adopting journey. 

AI integration requires a wide range of skill sets and this creates the major challenge for businesses which is to find and hire the right industry experts to take charge of the generation and operation of an end-to-end AI project. 

Another point to consider is that AI needs time to develop and time for advancement. The system needs persistent technological and innovative investment until it can begin evolving and satisfying an acceptable level of accuracy. Hence, it is crucial that businesses hire the right talent with exceptional creativity who are at the same time capable of enhancing technological use cases.  

As a result, many enterprises find it challenging to seek and employ machine learning and data experts. According to a survey by Deloitte, 31% of businesses struggle with skill shortage and this is one of the top three roadblocks in these businesses’ journey of AI implementation. Further research by Juniper found that 41% of its participants were concerned about training the current workforce to support the business’s AI integration and the challenges surrounding it. 

If AI integration is urgent while running short on industry specialists, the best option for businesses of any size is outsourcing to an external team of reliable field experts. This team then can develop the AI systems which should be smoothly adapted to the business’s existing tools.  

For AI governance, Juniper also advises organizations to promptly handle procedures and policies to diminish risks.  

Technology costs 

Costs of new technological tools and applications are unquestionably high and can be another challenge for enterprises that want to deploy AI. 

With this in mind, many smaller businesses with more limited budgets and lack of expert experience choose to outsource their AI integration to a partner that already possesses the required resources. 

AI governance and elimination of bias 

AI governance is among one of the major issues for enterprise AI as it involves monitoring all models developed across different areas of the business. It also includes return-on-investment (ROI) evaluation, risk management, bias assessment, algorithmic efficiency, ethical AI utilization, and legal compliance. 

These are important to businesses that are initiating AI deployment as they help clarify all processes and identify the challenges ahead.  

Bias in AI systems leads to hesitancy in decision-making which in turn leads to struggles when starting to work on AI governance. Since the AI models process substantial amounts of data, learn from them, and make decisions, businesses need to ensure that these decisions are accurate and unbiased. Bias in AI models can very likely lead to inaccurate decisions and unsolved problems. 

Bias in AI often appears because of the utilization of datasets which tend to discriminate specific groups. To block unknown biases from getting into the AI systems, enterprises should set up concrete and secure quality assurance procedures. Quality assurance processes should be extendable after AI deployment to prevent biases caused by data drifts and feedback loops. 

enterprises in corporate buildings with bubble icons

How StageZero resolves these challenges to boost your business’ AI deployment 

Despite all the challenges that come with it, it is undeniable that it is time for enterprises to consider AI integration. According to Statista, revenues from the global AI market are forecasted to double between 2020 and 2024 - a clear sign that many organizations are preparing for their AI leap to foster growth. This is also an opportune time for your enterprise. 

Collecting quality data, finding AI talent, and AI governance may be challenging – but we’re ready to share our expertise with you and your business. StageZero was founded by and employs some of the most highly qualified and experienced AI experts in Finland.  Our reliable industry experts build, label and validate data sets for ML and AI solutions, ensuring you have the time to focus on your other priorities. To maintain your model accuracy, and to ensure minimal bias, we use our unique proprietary algorithms for model verification to ensure that there is no slippage or concept drift creeping into your AI over time. 

Our unique technology enables a 100x user scale and reduced data turn-around times as we have people available 24/7. This also means we can employ human verification up to five times at no additional cost, which provides a huge accuracy benefit compared to our competitors. In fact, you will benefit from 10% higher accuracy than the market leading competitor for your diverse data tasks. Our global crowd of more than 10 million contributors guarantees that your data is diverse and consistent.  

StageZero specializes in end-to-end AI projects, which means our services span the entire AI life cycle: from data sourcing to algorithm development and model maintenance.  

So get in touch and learn more about how your business can benefit from our services! 

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