Build a Winning AI Strategy for Your Business

Going back to the question of payback on artificial intelligence investments, it’s key to distinguish between hard and soft ROI. To start using AI in business, pinpoint the problems you’re looking to solve with artificial intelligence, tying your initiatives to tangible outcomes. AI engineers could train algorithms to detect cats in Instagram posts by feeding them annotated images of our feline friends. Elon Musk, the billionaire founder of the neurotechnology company Neuralink, has said the first human received an implant from the brain-chip startup and is recovering well. “While many data leaders feel they need to be doing something with AI, they also face an intrinsic level of resistance built-in before they can even start doing anything.”

If you care about how AI is determining the winners and losers in business, and how you can leverage AI for the benefit of your organization, I encourage you to stay tuned. I write (almost) exclusively about how senior executives, board members, and other business leaders can use AI effectively. You can read past articles and be notified of new ones by clicking the “follow” button here.

Rely on humans to improve your AI system.

User experience plays a critical role in simplifying the management of AI model life cycles. Begin by researching use cases and white papers available in the public domain. These documents often mention the types of tools and platforms that have been used to deliver the end results. Explore your current internal IT vendors to see if they have

offerings for AI solutions within their portfolio (often, it’s easier to extend your footprint with an incumbent solution vendor vs. introducing a new vendor).

• Gaining buy-in may require ensuring a degree of trustworthiness and explainability embedded into the models.
• AI-infused applications should be consumable in the cloud (public or private) or within your existing datacenter or in a hybrid landscape.
• AI technologies such as neural-based machine learning and natural-language processing are beginning to mature and prove their value, quickly becoming centerpieces of AI technology suites among adopters.
• Forrester Research further reported that the gap between recognizing the importance of insights and actually applying them is largely due to a lack of the advanced analytics skills necessary to drive business outcomes.
• Defining milestones for an AI project upfront will help you determine the level of completion or maturity in your AI implementation journey.

Intelligent document processing (IDP) is the automation of document-based workflows using AI technologies. We see a lot of our clients use these tools for things like invoice processing, data entry and contract management, which allows them to save time and resources. Artificial intelligence is not some kind of silver-bullet solution that will magically boost your employees’ productivity and improve your bottom line — not even if your company taps into generative AI development services. This survey was overseen by the OnePoll research team, which is a member of the MRS and has corporate membership with the American Association for Public Opinion Research (AAPOR). Businesses also leverage AI for long-form written content, such as website copy (42%) and personalized advertising (46%).

Three Steps to Implement AI

The following are some questions practitioners should ask during the AI consideration, planning, implementation and go-live processes. Despite the hype, in McKinsey’s Global State of AI report, just 16% of respondents say their companies have taken deep learning beyond the piloting stage. While many enterprises are at some level of AI experimentation—including your competition—do not be compelled to race to the finish line.

Additionally, businesses foresee AI streamlining communication with colleagues via email (46%), generating website copy (30%), fixing coding errors (41%), translating information (47%) and summarizing information (53%). Half of respondents believe ChatGPT will contribute to improved decision-making (50%) and enable the creation of content in different languages (44%). Business owners expressed concern over technology dependence, with 43% of respondents worrying about becoming too reliant on AI. On top of that, 35% of entrepreneurs are anxious about the technical abilities needed to use AI efficiently. Furthermore, 28% of respondents are apprehensive about the potential for bias errors in AI systems.

Automation is another excellent benefit of AI technology because it can complete tasks in a fraction of the time that usually takes humans. Another option is using automation software to make decisions that humans would traditionally make. Predictive analytics and automation are just two out of many different ways companies can use AI in their business.

This can help businesses identify potential risks and opportunities—for example, identifying customers who are likely to churn, which allows companies to take proactive measures to retain these customers. According to Intel’s classification, companies with all five AI building blocks in place have reached foundational and operational artificial intelligence readiness. These enterprises can carry on with the AI implementation plan — and they are more likely to succeed if they have strong data governance and cybersecurity strategies and follow DevOps and Agile delivery best practices. In other cases (think AI-based medical imaging solutions), there might not be enough data for machine learning models to identify malignant tumors in CT scans with great precision. Data preparation for training AI takes the most amount of time in any AI solution development.

Businesses are turning to AI to a greater degree to improve and perfect their operations. According to the Forbes Advisor survey, businesses are using AI across a wide range of areas. The most popular applications include customer service, with 56% of respondents using AI for this purpose, and cybersecurity and fraud management, adopted by 51% of businesses. A company’s data architecture must be scalable and able to support the influx of data that AI initiatives bring with it.

Carefully consider the information you’re feeding into the learning models. Because most companies polled do not yet have a robust talent pool for AI, 53% prioritize hiring new outside talent with AI skills to aid their transformations rather than training existing employees to do those jobs. 65% of respondents reported that they acquired AI as an off-the-shelf product or contracted service rather than building their own how to implement ai in your business artificial intelligence solutions. Assembling a skilled and diverse AI team is essential for successful AI implementation. Depending on the scope and complexity of your AI projects, your team may include data scientists, machine learning engineers, data engineers, and domain experts. Once the overall system is in place, business teams need to identify opportunities for continuous  improvement in AI models and processes.

There are many open source AI platforms and vendor products that are built on these platforms. Consumers, regulators, business owners, and investors may all seek to understand the process by which an organization’s AI engine makes decisions, especially if those decisions can impact the quality of human lives. Black box architectures often do not allow for this, requiring developers to give proper forethought to explainability.

Survey results indicate that businesses are adopting AI for a variety of applications such as customer service, customer relationship management (CRM) and cybersecurity. They are also focusing on improving customer experience through personalized services, instant messaging and tailored advertising. Additionally, AI is enhancing internal business processes such as data aggregation, process automation and SEO tasks. Depending on the use case and data available, it may take multiple iterations to achieve the levels of accuracy desired to deploy AI models in production. However, that should not deter companies from deploying AI models in an incremental manner. Error analysis, user feedback incorporation, continuous learning/training should be integral parts of AI model lifecycle management.

This can account for up to 80% of the time spent from start to deploy to production. Data in companies tends to be available

in organization silos, with many privacy and governance controls. Some data maybe subject to legal and regulatory controls such as GDPR or HIPAA compliance. Having a solid strategy and plan for collecting, organizing, analyzing, governing and leveraging

data must be a top priority. Nearly 80% of the AI projects typically don’t scale beyond a PoC or lab environment.