Early adopters who applied the first generation of AI tools have already received good returns. AI-powered data analytics and robotic process automation helps replace human workers and deliver cost savings and efficiency improvements for the enterprise.
The use cases for Artificial Intelligence in enterprise settings have grown considerably since then. Artificial Intelligence now works under the hood, making existing tech smarter and delivering strategic advantage for the enterprise.
Companies across the spectrum, from biggies such as Microsoft, Google, and Salesforce, to innovative start-ups integrate AI as an intelligence layer across their entire tech stack. Advancement in Machine Learning, Deep Learning, and Natural Language Processing (NLP) makes it easier to integrate an AI algorithm layer into the software or platform. Here are ways AI adoption helps the enterprise to grow to the next level.
Actualize strategic decision-making
Artificial Intelligence improves enterprise decision making. Enterprises deploying AI-powered tools and algorithms can manage risks and innovate better.
Effective decision-making in today’s complex business environments depends on access to contextual and connected data. But even with access to rich, real-time data, human brains have inherent limitations. Artificial Intelligence helps enterprises overcome the natural limitations that impair decisions. AI-powered Big Data solutions retrieve and process vast volumes of data in seconds, with high accuracy. In enterprise settings, connected devices capture all customer transactions, gestures, and related indicators. AI works with nonlinear, exponential relationships and delivers consistent results.
The latest Artificial Intelligence (AI) services & solutions help business managers’ access relevant data-based insights. The deep insights help managers make informed, data-driven decisions to address complex challenges. AI takes guesswork, sentiments, and intuitions out of the decision-making process.
A portent of things to come – Deep Knowledge Ventures, a Hong Kong wealth management company appointed Vital, an AI-powered algorithm, as one of its board members, with voting rights! The algorithm sits in board meetings, weighs the risks, calculates outcomes, and makes recommendations on the validity of investment propositions. The algorithm gets one vote, along with other human board members.
Success in today’s competitive era depends on hyper-efficiency, for which streamlining workflows are a must. In a fiercely competitive environment, businesses who increase price risks losing customers. Instead, improving internal efficiencies help enterprises reduce costs and become more competitive.
The first generation of AI-powered tools automate repetitive processes to speed up processes and free human resources for complex tasks. The latest agile Artificial Intelligence (AI) services & solutions enable more complex use cases. Enterprises now use AI to remove inefficiencies in the production and value chain, improve accuracy, and optimize resource allocation.
Use cases include:
- IoT powered machines relay signals to the connected system when temperature, humidity, pressure or other parameters fall outside range. Connected AI-powered systems schedule preventive maintenance and pre-empt breakdown.
- Integrating industrial robots into workflows speeds up the process and improves accuracy. Earlier, the limitations of AI technology made it dangerous to deploy robots where humans worked. The advancements in AI technology makes it possible to deploy robots and humans in the same shopfloor. Robots handle labor-intensive and mundane tasks more quickly, accurately, and safely compared to humans.
- AI-powered algorithms streamline scheduling in field service platforms. Manual scheduling is energy draining and a full-time job. It is also replete with errors that breed inefficiency and revenue loss. Machine Learning algorithms match the technician’s skills with the complicity of the job, the technician’s upcoming leaves, company business hours, the customers’ preferred timings, and several other factors, to assign the right technician for the right job, at the right time.
- The latest versions of Cortana enable voice assistants to read emails and create automatic calendar events. Such AI assistants take notes, sends email, schedule meetings, and even auto-generate quality content.
Consider Airbus’ open data platform. The integrated platform pulls in data from the airlines and creates a 360-degree supply chain visibility. The platform delivers the analytics insights to suppliers and customers to improve performance. Suppliers get information on predictive maintenance needs. They may arrange for the spares and other inventory materials beforehand, pre-empting delays. Aircraft uptime ratio increases. Incidents of emergency repairs reduce, leading to lesser flight cancellations and happier customers.
Proactive customer support
The first generation of AI-powered chatbots processed simple queries and escalated complex queries to human agents. Developments in AI technology now enable chatbots to handle complex queries. Enterprises may also deploy Artificial Intelligence to offer proactive support and pre-empt customer complaints.
Intelligent chatbots replicate human-to-human service interactions, with advanced features such as multi-lingual support. Enterprises, hard-pressed to deploy human agents, may deploy chatbots to offer round-the-clock support.
Advances in Natural Language Processing (NLP) and Machine Learning make AI-powered systems understand written and spoken words. The machines extract subtle and hidden meanings in spoken conversations, chats, emails, and other messages. Enterprises tap into these Data Science-based technologies to understand customer sentiments. They may make proactive interventions to eliminate complaints and boost customer satisfaction.
Deploying Artificial Intelligence for customer support saves time and money for the enterprise. The HR costs to hire and train human agents also come down. The agents may focus their energies on complex issues. AI-based support also makes support consistent across channels. The resultant efficiency improvements and happy customers help the company become more competitive.
A good example of AI-enriching support is BMW’s “Project AI.” The project deploys AI across the value chain to support internal and external customers. Drivers get real-time help from the embedded Intelligent Personal Assistant. Internal employees may tap into the AI-powered assistant for handy tools that make their work easier. Some of the tools on offer include translation tools and context-processing assistants.
Personalized sales and marketing
Many enterprises apply Artificial Intelligence (AI) services & solutions to improve sales and marketing.
Machine Learning algorithms automate account and lead management tasks. AI-powered sales and marketing tools also enable targeted marketing. The AI engine identifies prospects with characteristics similar to existing high-value customers. Marketers may target them with personalized offers.
AI enables deep personalization through opinion mining. AI-powered analytics tools gather customer-related data such as search preferences, blog, or social media comments, sales records, CRM records, emails, tweets, and survey responses. Such insights clearly identifies sentiments and preferences of the customer. For instance, AI identifies the customer’s preferred engagement channel and payment choices. The algorithm uses such information to apply the best content or sales action that will convert the prospect.
Another extensive area of AI application in enterprise settings is for intelligent pricing. Most B2B or even high-value B2C transactions involve negotiations. Artificial Intelligence tools offer business executives comprehensive real-time insights, including path deals, win-loss rates at different price points, and other insights not easily decipherable using conventional means. The AI tool, accessible through the executive’s smartphone, suggests the most competitive prices to enable a win-win deal and help to close the deal. Likewise, several real estate companies apply Artificial Intelligence (AI) services & solutions to predict property prices.
Mitigating the skill shortage
There are many reasons for the talent crunch, including inability to keep pace with constant changes in technology, different mind-sets between Gen Y and Z and Gen X/Boomers, and changing attitude towards work-life balance post the pandemic.
Enterprises overcome the skill shortage by training the workforce, recruiting aggressively, and outsourcing. But such strategies have limited effectiveness. Artificial Intelligence (AI) services & solutions help enterprises ramp up their HR capabilities and grow to the next level.
Hiring for competencies in a particular technology is meaningless in a world where technology has a short shelf life. Instead, forward-looking enterprises hire candidates with the right skills and competencies. AI tools help recruiters’ shortlist candidates who tick all boxes. AI tools identify the key performance drivers for specific roles. The tool matches ideal candidates, internal or external, for each position.
Reskilling employees have become inevitable for enterprises to keep pace with technology. AI-powered tools crunch performance data and shortlist employees who are willing to learn and are amenable to change. Managers may entrust such employees with responsibilities to grow the company.
Artificial Intelligence in action
Several businesses, cutting across sectors, apply Artificial Intelligence (AI) services & solutions to offer new products, improve their quality of service, or provide additional value to customers.
- Banks combine genetic algorithms (GA), machine learning, and big data to analyze loan portfolios and weed out unsafe loan applicants.
- Vehicle managers use AI to offer value-added services to their customers. For instance, Volvo applies AI to monitor vehicle performance in dangerous conditions. Embedded sensors collect data on driving variables. Real-time analysis of the collected data enables prompt alerts.
- Fleet managers apply AI to plot the best routes to a destination and make faster deliveries. The AI tool considers road conditions, real-time traffic information, tolls, and other factors influencing driving time and driver safety.
- Business executives use AI to determine the proper inventory levels and avoid stock-out situations. With proper training, the algorithms opt for higher inventory levels to overcome supply chain disruptions and other contingencies. The algorithms make better decisions than the limited cognitive capabilities of the human brain.
One noteworthy application is Intel’s AI-powered retail shelves that enable self-checkout. The system detects unreadable barcodes, recognizes speech, and responds to gestures. It mimics checkout staff and enables seamless check-out.
Using Artificial Intelligence (AI) services & solutions is not always a smooth ride. Enterprises need to overcome data and scale-up challenges before they can enjoy success.
Clean data to avoid “garbage in, garbage out”
The success of AI deployment for business growth depends on having the right data. The potency of machine learning algorithms is only as good as the input data. Before AI works in enterprise settings, executives have their tasks cut out in training algorithms. Successful AI projects depend on accurately curated datasets.
- Remove silos. Enterprise data often resides in multiple databases. Many databases may be in legacy systems and not ready for integration into the mainstream analytics engine. Business groups with different priorities control much of the data. The first task of any AI project is to identify and tackle silos.
- Standardize data. Businesses collect raw unstructured information from various sources. Some of the sources include public datasets, internal workflows, and lead purchases. There is a need to bring data from such disparate sources to a common ground before Machine Learning algorithms can recognize and classify it correctly. Sort out the inconsistencies to ensure data is accurate and relevant. Use connectors and APIs to pull in data from disparate systems.
The data related challenges are often too vast for the IT team to tackle on its own. Form a cross-functional task force to address such data challenges. Wherever appropriate, undertake digital transformation to integrate data.
Start small and scale-up
AI projects usually fail when businesses try too much. AI is still a nascent technology with no clear-cut best practices. The mantra for success is to start small and scale-up. Successful AI projects strive for small, incremental gains per iteration. The project managers apply the model to a small sample of the data, work out the glitches, and scale up in increments.
- Optimize storage for data ingest, workflow, and modeling for each iteration. Also, build in bandwidth for storage, GPU, and security. The best projects understand the hardware and software limitations and iterate accordingly.
- Develop dynamic AI models that adapt and improve based on real-time input data, and validate the models through testing.
- Ensure continuous monitoring of the models in production to pre-empt glitches.
Scale-up using Suyati’s SAM
Many enterprises get their planning and use case identification right. But they still falter in AI implementation. About 95% of the enterprises struggle to scale up their models to real-life enterprise settings. Common pitfalls include suboptimal coding, optimizing the wrong objectives, and being overambitious. The root cause is skill-gaps, which internal training cannot bridge. Successful AI projects invariably need the support and services of outside experts or consultants.
A business environment is very complex and fluid. Customer preferences, technological stakes, regulations, and other critical aspects change quickly. Often, the AI models do not factor in such changes and falter in a real-world application. It again takes experienced stakeholders (internal and/or external) to develop robust models that remain resilient in a rapidly changing external environment.
Enterprises also need effective tools to develop and scale-up the models. One of the best-in-breed tools is Suyati’s SAM, an Artificial Intelligence miner. The tool applies models that leverage Machine and Deep Learning and enables effortless scale-up AI from the lab to real-time commercial use.
Suyati SAM comes with the backing of skilled data scientists, analysts, and engineers with many years of experience. The team ensures clarity of objectives, list obstacles, and draw up action plans to overcome the challenges. The team strikes an effective collaboration with the enterprise and ensure the success of the iterations.
The best application of Artificial Intelligence (AI) services & solutions depends on specific enterprise needs. Business executives who align AI with value creation deliver competitive advantages. Using the right tools, such as SAM AI miner, makes the task easy and helps the enterprise avoid the pitfalls common to AI-led business transformation.