by Prashant Kelker

5 ways to use artificial intelligence to scale your business strategy

May 10, 2018
Artificial IntelligenceIT StrategyTechnology Industry

Intelligent technology expectations and returns remain unclear and will require the extrapolation of data and algorithms to solidify product ideas and investments.

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Credit: Getty Images

2016 marked the year that the enterprise world began to wake up to the potential of artificial intelligence (AI). Just two years ago, AI software programs were writing full movie screenplays, predicting the Kentucky Derby and beating video game world champions – and the technological breakthroughs did not stop there. Since then businesses, including many Fortune 500 companies, have started to grapple with what AI can do for their business and operations.

As they experiment, a clear pattern is emerging: anything which is a repeatable process can and will be taken over by AI and machine learning (ML). If you decide to extrapolate these technologies, then all normal processes will be automated, and the job of humans will be relegated to handling the complex exceptions. For example, a recent Harvard Business Review article highlighted how Stitch Fix, an online clothing subscription service, uses a machine learning engine to help make personalized recommendations for customers. Automating data collection and routine processes for employees increases productivity, helps to handle complex requests and allows greater focus on connecting with clients – which ultimately lets businesses better allocate their resources and boost profitability.

At this point neither the technology nor the details of its implementation are standing in the way of leveraging AI and ML. Instead, there are five factors that you should keep in mind while using new technology to scale the growth of an organization: the ability to imagine, the willingness to change, the rethinking of product design, the mastering of the art of partnering and, finally, the ability to view AI as a paradigm for business change.

1. Unleash the imagination of your workforce

The challenges of utilizing AI and ML do not lie within the technology, but rather in determining the most effective use cases. How do we enable each person in the organization to think big and imagine what the future will be like – and place him/herself in this picture? What is blocking this imagination? One of the culprits is hierarchical organizational structures that inadvertently shape the mindsets of employees and narrow their thinking. The creation and nurturing of communities within will enable talent and creativity to cross silos and break down barriers.

2. Overcome the willingness to change

It is counterproductive to push intelligent technologies upon an entire organization all at once. Instead, work with your employees to find the balance between investment in AI and ML technologies and maintaining the existing business. Employees are far more willing to master new technologies when they understand that these technologies won’t be taking their job. There is a misconception that AI and ML technologies will replace people. Rather, intelligent technologies are implemented to handle the routine processes with precision and allow employees to focus on the more complex, human aspects of their jobs.

Redesign business processes from end to end, instead of using a piecemeal approach. Doing this will highlight the importance of the human factor in context with AI/ML and will unleash a new set of human talents that machines are currently unable to match – creativity, interpersonal communication and empathy. In turn, this will lead to the creation of new value, new jobs and higher-level responsibilities.

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 inputs and outputs, and systems can now ascertain how to deal with data through self-learning algorithms. The art no longer lies in how to build systems with clear rules and logic, rather new skills will be required to build systems that constantly learn as more data is thrown at them. This will fundamentally reboot software development paradigms – like requirement analysis design and testing – regardless if it’s done agile or waterfall.

Enterprise resource planning (ERP) systems are also changing. Data architectures will come to the forefront, as customizing packaged software to model the processes of an organization will no longer be required. AI, combined with the rapid creation of apps, will relegate ERP systems into operating engines. Companies shifting their business processes to take advantage of new business models and emerging markets will seek out intelligent technologies to ensure a smooth ERP transition.

4. Master the art of scouting & partnering

Working with providers and technology platforms will undergo a significant change as we leave the realm of deterministic systems. Organizations will have to learn to rely on their providers now that part of the AI solutions are embedded in their offerings. The discussion will move away from which partner delivers the best quality to whether a partner can be trusted to run AI ethically and competently within a business process. AI startups have sprung up like mushrooms – each having a narrow focus, lack of real investment capital and access to the enterprise market. This is a huge matchmaking opportunity for procurement departments to sharpen their axes in the skill of creating partnerships. It will be important for enterprises to understand that partnering involves contracting for co-creation, joint success and sharing of the joint intellectual property created, so that they do not fall behind at the enterprise level.

5. View AI as a paradigm for business design

As the use cases of AI explode, organizations cannot afford to wait for the industry to show what is possible and the days of aiming to be the fast follower are over. Yet, the pressure to act will depend heavily on the competitiveness of the industry segment and the merging of industry segments. Manufacturing companies are tracking finance and retail companies using marketing AI tools and insurance companies are adopting manufacturing paradigms, like Digital Twins, to predict the risk of the assets they insure. AI should be seen not only as the means to improve operations, but rather to fundamentally change the way that an organization makes its revenue. AI and ML have the power to redefine value chains globally. Market leaders should look at AI as a means of defending their market position, while others should use AI to reposition themselves during the disintegration of the value chain. Employee talent, adoption of the technology, business design and potential partnerships will further drive the acceptance of AI. These factors, along with ultimately viewing AI as a core part of your business strategy will be a crucial in deciding when and how to implement intelligent technologies.

This will not be an easy task. Intelligent technology expectations and returns remain unclear and will require the extrapolation of data and algorithms to solidify product ideas and investments. The complexity of the technology and its implementation should not be considered a critical factor that stands in the way of leveraging AI and ML. Keep in mind the five tips detailed above, and you will better find success while using the new technologies to grow your organization.