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View This Exciting New Video: AI Means All Firms Must Become Software Firms

by PRC Agency PR

In 2022, 70% of top economic performers in traditional companies used proprietary AI software to massively boost performance. In essence, the AI revolution means all companies must become software companies or go bust. This will challenge management in 90% of traditional businesses currently operating. DigitalBiz’s new videos show how any company in any sector can transform itself into a software company using AI.

Interested parties can access the newly launched videos at https://digitalbiz.ai

The new guide recognizes that traditional companies are not specialist software developers but include retail, construction, healthcare, financial services, transportation, food and so on. And despite the current hype around AI, it doesn't mean a company has to write software from scratch.

A huge challenge in 2023 will be for traditional companies to crush the risk associated with implementing AI. The video explains how any company can deploy AI to create unique software which drives added value.

A company deploys an AI which learns from a company’s own internal data sources and is corrected by deeply knowledgeable staff who are expert at the task involved. This unique blend of AI and data delivers competitive advantage by learning from staff edits which allow it to fine tune itself to execute tasks better, faster and cheaper.

According to Gartner most companies already use software to organise, produce, market, sell and deliver. Cloud computing now delivers unprecedented power and expertise to non experts anywhere. So the lesson is clear. Value will be increasingly software driven.

Familiarity with existing software means most companies view AI as a software “bolt on” so videos emphasizes the importance of human oversight and staff expertise. AI churns out information as data strings with little idea of meaning, relevancy or accuracy. It is a combination of AI software, company data, and staff with deep knowledge of operations, that delivers productivity gains.

Other important issues covered include:-

Committing to a new culture. Valuing creativity, innovation and risk taking in all areas of the business from the CEO down. Risk taking is particularly important.

Supporting staff on the journey. The key to making AI work is staff with deep company knowledge. The more staff fix AI errors the more efficient, effective and productive the AI will be as it fine tunes itself to the task at hand.

Investing in the process, not just the AI software. Time is needed for AI to learn from staff and for processes and procedures to be fine tuned. The more complex the task the longer the learning curve.

Educating everyone from CEO down. Cloud computing means huge leverage can now be given to non-expert staff using no-code tools and AI-based programming assistance. Ordinary none AI expert employees will be empowered to create proprietary company software.

Communicating successes and failures to everyone. Take on board staff suggestions. AI’s learn from staff corrections. An adjustment in one area might work in another.

Encouraging autonomy and trusting staff. AI learning thrives on staff flexibility whilst legacy top down management causes delay.

Empowering and retaining staff. Competition for AI talent is exploding and companies will discover unknown AI talents with deep company knowledge in many departments. Internal talent needs to be retained, nurtured, developed and given opportunities to grow.

When a business owner or manager has committed to the cultural and organizational changes needed to transition to a software company, the next step is to identify a use case and select which AI to test it on.

DigitalBiz advice is that there is no need to go overboard or make a huge financial commitment. Start off small on a single task. And build from there.

Pick a job that needs data entry, is repetitive or predicts the future. AI will likely do it more proficiently and cost-effectively. All businesses are different. But most will have separate, distinct, data driven, repetitive and prediction tasks as part of a process.

A variety of specific AI’s is normally needed to implement complex tasks and present AI should not be expected to deliver a complete end to end process. Staff should be used to drive AI's up the learning curve by making corrections and to fill gaps AI can’t handle. Generally, as AI’s gradually learn from human edits, the level of process automation will increase.

A key issue is that all AIs have the ability to learn from mistakes and need time to soak up human experience until it is fine tuned for a specific use case. It is this ability to learn from mistakes and not repeat them that delivers business value.

Select a task and tackle it with a quick, simple AI pilot test. Snags w


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Created on Aug 1st 2023 23:30. Viewed 126 times.

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