Implementing AI in your organization: Key challenges and benefits 

A dozen years ago, enterprises saw a comprehensive digital transformation of their business processes as a key recipe for gaining a sharp competitive edge and providing maximum customer satisfaction to their clients. Today, when any organization with big-time aspirations performs its business operations and solves business problems by leveraging state-of-the-art know-how and cloud computing and storage resources, being just digitally savvy is not enough.  

Artificial intelligence and related technologies, including deep neural networks, machine learning models, computer vision, natural language processing, data science, and others, are quickly becoming a competitive differentiator capable of revolutionizing shop floor activities and generating enormous financial value across multiple verticals. Realizing this, however, many businesses still struggle with implementing AI successfully and selecting the right AI tools to drive the efficiency of their workflows. 

This article showcases the benefits of integrating AI systems in an organization’s pipeline. It suggests a detailed AI implementation strategy to achieve AI excellence and maximize the use of AI-powered tools to achieve business goals.

AI in business: Benefits

Nowadays, AI technologies are robustly entering all spheres of life, and the business domain is no exception. According to Forbes, 72% of enterprises currently leverage AI solutions for at least one business function, and this number is expected to increase and cover all companies within a few years. The global AI technology market is expected to manifest a breathtaking CAGR of over 37% within the next six years and exceed $1.8 trillion by 2030. 

Such exponential growth is the best proof of the significant benefits that new AI tools can usher in to help companies reach their business objectives.  

• Greater efficiency and productivity. AI’s capabilities allow tools based on artificial intelligence to automate simple and repetitive tasks, minimizing manual work and letting human personnel focus on more complex and creative assignments. Besides, AI applications work much faster than people, so you can leverage them to create robust data governance frameworks where all data processing routines (beginning with information retrieval from external data sources and ending with high-level data analysis and efficient data management) will be performed quickly and accurately.  
• Augmented decision-making. AI algorithms can swiftly sift through vast bulks of data (both historical and real-time), pinpoint patterns and trends across them, and derive actionable insights. Such AI-fueled data analytics become inputs during force field analysis that can be used for knowledgeable decision-making in resource management, marketing, sales, logistics, customer interactions, and other workflows. 
• Enhanced risk management. AI adoption elevates risk assessment efforts. Its cutting-edge tools identify anomalies in datasets, market fluctuations, fraudulent-looking activities, and the like, evaluate the risks they pose, alert the employees responsible for tackling them, and suggest measures to mitigate potential threats. 
• Improved predictive analytics. You can attain the pinnacle of business success only if you learn to anticipate future trends in the industry, customer preferences dynamics, operational bottlenecks, and other developments. AI integration empowers CEOs and rank-and-file employees with forecasts that are vital for devising proactive business strategies and driving growth. 
• Top-notch customer service. Customer support is the primary use case for contemporary AI initiatives harnessed by enterprises.   

AI-powered chatbots and virtual assistants are available round-the-clock and can provide customers with human-like communication while addressing issues and offering advice. Moreover, their machine-learning algorithms allow these tools to become more sophisticated after each interaction. 

• Tailored approach to each customer. In the modern, highly competitive business landscape, personalization of customer experience is the name of the game. Deep learning mechanisms of specific AI tools enable them to segment the audience on a granular level and obtain a 360-degree view of its members with their buying behavior, preferences, tastes, and pain points. Having such information at their virtual fingertips, AI solutions can tailor their recommendations and offers to suit individual needs, thus boosting customer satisfaction and fostering brand loyalty.  
• Expenditure reduction. By optimizing operations, reducing waste, improving resource allocation, and automating the lion’s share of elementary procedures, enterprise-wide AI adoption ushers in significant cost savings.  
• Innovation in products and services. Embracing AI in your business operations allows personnel to tap areas for product and service improvement, increase their quality, experiment with novel approaches and tools, enhance the speed of new product development, and ensure the consistency of high functionality and reliability standards across the entire product line or service range.  
• Flexibility and scalability. If you successfully integrate AI into your workflows, you can forget about hiring additional workforce when your organization expands or during seasonal fluctuations in the workload. AI-driven tools can be easily and promptly upscaled to meet the surging demand and downscaled when the tide has subsided.  
• Synthetic data generation. Training AI models requires vast amounts of data. Real-world dossiers and records may be expensive to acquire, biased, and subject to copyright or data privacy laws. AI systems can produce unlimited quantities of synthetic data that will be further utilized in AI development, scenario modeling, data analytics, and other fields. This year, the proportion of synthetic data in such AI projects is expected to reach 60%.  
• Improved talent management. Organizations can apply AI-fueled tools in key areas of workforce management, such as hiring, training, retention, and communication. They can streamline and facilitate talent hunting and screening, assess employee sentiment, pinpoint the best performers, determine equitable pay, and deliver engaging and highly tailored workplace experiences. 
• Lower ecological impact. By leveraging AI to improve supply chains, optimize delivery routes, and upgrade fleet management, organizations can reduce their waste and energy consumption, cut down on material usage, and minimize their carbon footprint, ushering in sustainable business practices.  
• Sharper competitive edge. This is a corollary of all the previously mentioned boons. A highly productive, flexible, cost-efficient, environmentally friendly, and innovation-driven organization that has top-notch experts on its staff and provides best-in-class customer service and a personalized approach to each client is sure to attract more consumers who become loyal customers. 

Harnessing AI tools in your shop floor operations makes perfect business sense. However, you can’t add AI capabilities to your legacy systems. To successfully implement AI solutions, you should adhere to a rigid roadmap.  

How to implement AI in business settings: An algorithm to follow

As a seasoned high-tech vendor with in-depth expertise in the AI implementation process, we at DICEUS have devised a detailed strategy that allows our certified and qualified engineers to carry out this procedure smoothly and efficiently. 

Step 1. Assessing business needs 

Launching multiple AI implementations just because everyone (especially your competitors) has already done it is a bad idea. You should conduct a comprehensive analysis of your current situation, identify business challenges AI can help you address, establish workflows where AI can bring maximum business value, prioritize the mission-critical ones, and determine business outcomes and ROI expectations you want to obtain. 

Step 2. Building an AI strategy 

When you know what to aim for, you should outline how to reach the goal. To do that, you should involve a broad audience of stakeholders across all organizational departments and devise a comprehensive and meticulous plan. It should include specific, achievable, and measurable objectives benchmarked against industry standards, the budget you can allocate for the project, the timeline with major milestones, and the expected KPIs the newly created system must display.  

Step 3. Choosing the right AI tools and technologies 

The tool selection procedure starts with finding an answer to pivotal questions about the problems the future AI software will solve, the type of data it will work with, and the technical skills (basic or advanced) of its prospective user audience. Depending on the answers, you should make a shortlist of tools satisfying the requirements and compare them in terms of functionality, scalability, integration potential, customizability, cost, and learning curve. Adequate support from the tool’s creators and a strong user community are also essential factors to consider. 

Finally, after finding the best tradeoffs and balance between the tools’ strengths and weaknesses, you should give the ones you like a shot and use their demo versions or free trial options, paying attention to their limitations and issues revealed during this. If the tools and technologies operate properly in your working environment, commit yourself to them.  

Step 4. Preparing data 

Data quality is mission-critical for the accuracy of the outcome. That is why you should devise an effective data strategy that includes data collection, storage, cleaning, and analysis. To guarantee that your model will be trained on adequate amounts of high-quality data, you should accumulate as much input as possible from all available sources and curate it.  

Your ultimate goal at this stage should be creating a relevant, complete, accurate, consistent, and understandable training data bank that is understandable for AI mechanisms and compatible with them in format. Also, you should pay attention to data privacy and security. If the real-world data available to you doesn’t meet these requirements, you will have to generate and leverage synthetic data. 

Step 5. Assembling a competent team 

The successful implementation of any software requires a qualified workforce. For AI projects, such a team should include data scientists, AI architects, machine learning engineers, business analysts, and ethics and compliance advisors as non-negotiable figures. The common effort of such diverse specialists is doomed to failure if it is not directed and supervised by a vetted project manager who oversees the full-cycle project’s implementation and its alignment with business goals, ensuring the team is on the same page and stays within schedule and budget.  

You should understand whether your company has such experts on its roster. If not, you should adopt an outstaffing or outsourcing model, depending on the expertise gap you have to fill. In case the project will require a joint effort of in-house specialists and external experts, it makes sense to establish an AI excellence center that will serve as a collaboration hub for all stakeholders and coordinate their activities. 

Step 6. Developing AI models 

The basic model types leveraged for business purposes are supervised learning, unsupervised learning, and reinforcement learning ones. But before creating an AI model for your solution, you should choose the one that will handle the pipeline assignments the AI software is called to solve. Other pivotal factors that influence the choice are the type and size of data it will process, its availability, computational resources it will require, model performance, complexity, explainability, inference speed, and the cost and duration of model training. Also, you should bear in mind hardware constraints and updating and maintenance needs. 

Once you understand what kind of model you are going to develop, you should design its architecture and train it, involving as large a dataset as possible. When the training is completed, you should validate the results and test the model on the so-called test sets containing real-world unstructured and unlabeled data. 

Step 7. Piloting AI solutions 

We recommend starting small and implementing an isolated Proof of Concept, which will allow users to embrace a holistic application of the solution. The bottlenecks and pitfalls identified at this stage should be addressed immediately. When the standalone product’s performance is satisfactory, you should integrate it with the existing ecosystem. But, again, it is prudent to implement the tool for a separate workflow (say, customer service automation) and see how it tells upon its efficiency and KPIs. 

Step 8. Scaling AI across the business 

According to a survey, three-fourths of enterprises struggle to nail the scale of AI initiatives for their professional needs. As companies’ executives admitted, only 16% of enterprises moved beyond experimenting with isolated AI tools on the departmental level to their full-scale implementation across the organization’s workflows.  

To integrate AI enterprise-wide, it is necessary to create a universal employee buy-in, appoint effective transformation drivers, engage multiple dedicated AI teams, and build a strong data foundation. Such a combo will enable companies to expand AI initiatives from department to department, align new technologies with the major business goals, and achieve their full-blast implementation with minimal disruption and maximum outcomes.  

Step 9. Measuring AI impact 

When the AI solution is deployed, you can’t rest on your oars. You should monitor its functioning and make adjustments if necessary. Naturally, you should steer by major performance metrics (such as speed, accuracy, efficiency, etc.) and user feedback. Besides, you should compare the achievements against ROI benchmarks – both calculable (cost reduction, time saving, lead generation, and the like) and intangible (workforce skill enhancement and customer satisfaction). 

Step 10. Navigating AI ethics and compliance 

It is wrong to say that this is the final touch in embracing AI business solutions because the implementation team (its ethics and compliance specialist, to be precise) should keep track of ethical considerations and regulatory compliance throughout the entire process.  

Ethical concerns about using AI include drawing distinct accountability lines between development team members, providing the transparency of AI models’ operation, minimizing unfairness and bias in data employed for model training, and ensuring its safety and security.  

The latter aspect overlaps with compliance requirements, which are especially stringent for certain industries handling sensitive customer data. That is why organizations in banking, insurance, fintech, retail, and healthcare sectors should pay primary attention to adhering to GDPR, CCPA, HIPAA, and other legal regulations if they want to avoid hefty financial and disciplinary penalties. 

As you can see, the AI-driven digital transformation of businesses is a tough row to hoe, and organizations can hardly handle it without the involvement of external expertise. Skilled mavens of DICEUS possess the necessary competencies and experience in developing AI-enabled solutions across various verticals to tackle an AI project of any type and scope, delivering a top-notch product within time and budget. Drop us a line to embrace artificial intelligence and reinvent your routine workflows. 

Summing it up

Growing competition across most areas of the contemporary business landscape urges enterprises to embrace state-of-the-art know-how for stepping up their pipeline operations. Artificial intelligence and related technologies (machine learning, computer vision, natural language processing, and more) are the most popular disruptive achievements that gain significant traction with entrepreneurs and display mind-blowing growth indices. 

Organizations that harness AI-driven products in their workflows report greater productivity and efficiency, more knowledgeable decision-making, improved risk management, enhanced predictive analytics, significant cost reduction, boosted flexibility and scalability, augmented talent management, personalized customer service, and other weighty perks.  

If you want to maximize the boons artificial intelligence brings to your company, you should follow a well-thought-out AI integration strategy and hire high-profile AI experts to develop and implement the best-in-class AI enterprise solutions. 

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