AI Adoption Strategy: 5 Strategies for Using Artificial Intelligence to Scale Your Business

AI Adoption Strategy


ai adoption strategy

At present, the expectations and returns of smart technology are still unclear, and data and algorithms need to be constantly updated to consolidate product ideas and investments.

2016 was the year the corporate world began to unleash the potential of artificial intelligence (AI). Just six years ago, artificial intelligence programs were capable of writing entire movie scripts, predicting the Kentucky Derby and beating world champions in video games — and the technological breakthroughs don’t stop there. Since then, enterprises, including many Fortune 500 companies, have begun to wonder what AI can do for their businesses and operations.
As they experiment, a clear pattern is emerging: Any repeatable process can, or will, be taken over by artificial intelligence and machine learning (ML). If you understand these technologies, you can predict that all normal processes will be automated, and human work will be relegated to only dealing with complex exceptions. For example, a recent Harvard Business Review article highlighted how online clothing subscription service Stitch Fix uses a machine learning engine to provide customers with personalized recommendations. Automating data collection and routine processes for employees increases productivity and helps handle complex requests, allowing businesses to focus more on connecting with customers – ultimately allowing businesses to better allocate resources and improve profitability.
At this point, neither the technology nor its implementation details are conducive to leveraging AI and ML. Instead, there are five factors to keep in mind when using new technologies to scale an organization: the ability to imagine, the willingness to change, rethinking product design, mastering the art of collaboration, and lastly, seeing AI as a key to business transformation. model.
1. Unleash the imagination of employees
The challenge in leveraging AI and ML is not the technology, but identifying the most effective use cases. How do we enable everyone in the organization to think and imagine what the future holds – and place him/her in this fantasy world? What stops this imagination? One of the culprits is hierarchical organizational structures , inadvertently shaping employee mindsets and limiting their thinking. Creating and nurturing communities within communities will enable talent and creativity to transcend silos and break down barriers.
2. Resist the urge to rush
Rushing to push smart technology all over the organization at once will be counterproductive. Instead, it’s best to work with your employees to find a balance between investing in AI and ML technologies and maintaining your existing business. When employees understand that these technologies will not take away their jobs, they will be more willing to master these new technologies. There is a misconception that AI and ML technologies will replace people. Conversely, implementing smart technologies can help businesses handle day-to-day processes with precision and allow employees to focus on the more complex, human aspects of their work.
Redesign entire business processes from end-to-end, rather than using piecemeal approaches. Doing so will highlight the importance of the human element in AI/Machine Learning and will unlock a range of abilities that are currently out of reach of robots – creativity, interpersonal communication and empathy. In turn, this will lead to the creation of new value, new jobs and higher levels of responsibility.
3. Rethink existing product design and architecture patterns
Technologists have been designing deterministic systems for decades. Software products and systems are designed based on clarity of input and output, and these systems can even determine how to process data through self-learning algorithms. The point is no longer how to build a system with clear rules and logic, but that the system requires new skills to build the system, and the system learns as more data is fed into the system. This will lead to the need to fundamentally reset the discipline of software development – such as requirements analysis design and testing – whether it is based on agile or waterfall methodologies.
Enterprise resource planning (ERP) systems are also changing. Data architecture will be at the forefront, as custom packaged software to model organizational processes will no longer be necessary. AI will combine with rapidly created applications to transform ERP systems into operational engines. Enterprises will transform business processes to take advantage of new business models, seeking smart technologies for emerging markets to ensure a smooth ERP transformation.
4. Master the art of scouting and cooperation
Working with vendors and technology platforms will change significantly as we leave the domain of deterministic systems. Parts of AI solutions will now be embedded in their products, causing organizations to learn to rely on their providers. The discussion will start with which partner can provide the best quality of service to determine whether a partner can be trusted to run AI in a business process and perform flawlessly there. AI startups are popping up like mushrooms — but each has a narrow focus and lacks real investment capital and access to the enterprise market. This is a huge opportunity for procurement departments to sharpen their partnership-building skills. Businesses must understand that collaboration involves co-creation, joint success and sharing of the intellectual property created so that they do not lag behind their peers.
5. See AI as a paradigm for business design
As the use of AI explodes, the days of organizations passively waiting for industry peers to show where they might be headed, and being a fast follower, are over. However, the urgency to act depends on the competitiveness of the industry segments and the consolidation of the industry segments. Manufacturing companies are using AI tools to track financial and retail companies, and insurance companies are adopting manufacturing models, such as Digital Twins, to predict the risk of the assets they insure. AI should not only be seen as a means to improve operations, as it can fundamentally change the way organizations earn money. AI and ML have the power to redefine global value chains. Market leaders should use AI as a means to defend their market position, while others should use AI to reposition themselves during value chain disintegration. Employee talent, technology adoption, business design and potential partnerships will further drive AI adoption. These factors, along with seeing AI as a central part of corporate strategy, will be key in deciding when and how to implement smart technologies.
This is not an easy task. The expectations and rewards of smart technology are still unclear, and data and algorithms need to be constantly updated to solidify product ideas and investments. The sophistication of the technology and how it will be implemented should not be considered a key factor in leveraging AI and ML. When you use new technology to grow your organization, remember the five tips above, which will make it easier for you to succeed.
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