Defining AI-Frameworks to Deliver Value-Driven Processes

Defining AI Framework to deliver value-driven processes

AI’s role in enterprise digital transformation

The power of AI lies in its ability to discern patterns within data, a capability that has enhanced how businesses perceive and tackle challenges. However, the true strength of AI emerges when these data patterns are translated into frameworks that redefine organizational processes. In this way, all business problems can be cast into a specific data pattern – even the most complex. 

The potency of an AI deployment is not constrained by software or developers but by the strategic vision of the C-Suite and business analysts. The appeal of AI lies in its capacity to generate diverse solutions rapidly, empowering organizations to reimagine workflows and drive innovation.

AI adoption peaked at 35% in 2023, but PwC’s Digital Trends Survey found that despite this promising development, nearly 70% of the businesses surveyed felt that their tech investments did not yield the expected results.

Despite this number, many organizations display a spectrum of achievements in their AI implementations, from small everyday operational benefits to more complex issues, like grappling with how employees can use AI to augment and complement their activities to deliver greater and new value.  

With the high expectations surrounding AI capabilities, enterprises can only benefit when they have a defined, thought-out strategy. From investment in AI, to achieving the desired outcomes a pathway needs to be determined.  

AI integration alignment within organizations presents a set of challenges and opportunities, which I will present in this article. 

Cultivating an understanding of AI and AI frameworks within the enterprise

A critical challenge for the C-suite is fostering an understanding of AI frameworks that can redefine business operations. How can AI frameworks be reimagined to support value-driven processes?

Promoting a mentality of continual learning and experimenting is necessary to cultivate an AI-centric ethos within the organization. This entails giving staff members the freedom to suggest ways to improve workflows and procedures currently in place as well as to investigate creative applications for AI frameworks.

Furthermore, sharing knowledge and working together are essential to fostering AI literacy inside the company. Organizations may expedite the adoption and deployment of AI by using collective knowledge through the promotion of cross-functional cooperation and the creation of opportunities for staff to share insights and best practices.

Rethinking AI frameworks to facilitate value-driven processes ultimately calls for visionary leadership and an openness to innovation.

Talent: Preparing workforces with reskilling and upskilling.  

Historically, technology has never replaced human beings; it has always just augmented human activity. AI is no different, but senior leadership must understand how to leverage technology sustainably to foster growth and an environment of adaptability. 

Research has found that incorporating AI into organizational procedures has a beneficial effect on employee fulfillment and staff engagement. Employers can establish a more rewarding atmosphere that encourages staff participation, loyalty, and commitment by automating repetitive jobs, providing workers with AI-powered analytics, and encouraging an innovative culture.

Erik Brynjolfsson, Director of Stanford Digital Economy Lab, talks about the following in his research

Customer agent productivity improved – productivity indicators, such Average Handling Time (AHT) and the number of requests discharged in a specific timeframe, can significantly increase with the use of AI. Customer service representatives respond to requests faster and more effectively by automating repetitive processes and giving them access to real-time insights. This CNBC study found that 72% of employees working with AI technology felt it helped them work more efficiently.

Customer satisfaction improved – AI-powered solutions have a major impact on raising client fulfillment levels. By leveraging multi-channel experiences, statistical modeling, and tailored interactions, companies can provide exceptional service, improving customer loyalty and retention metrics.

Enhanced adherence of tool usage – AI technologies enable staff to apply technological solutions more consistently. AI frameworks encourage uniformity and productivity in tool application, which results in observable cost savings and operational optimization. They do this by streamlining processes, delivering user-friendly interfaces, and providing rich analytics that can be applied to future dealings.

Enhanced learning for semi-skilled and skilled agents – The effective upskilling and reskilling of workers is made possible by AI-powered instructional systems. AI encourages ongoing learning and career advancement by providing staff with the expertise and abilities necessary to succeed in an ever-shifting digital world. This is done by providing customized training options, flexible evaluations, and engaging material. 

Greater job satisfaction and reduced attrition – People want to work in companies that invest in them. Employees can be given greater responsibility and focus on more fulfilling projects when routine jobs are automated. This transition from mundane to purposeful work creates a greater sense of individual achievement and fulfillment at work. Employees have many chances to grow professionally and make meaningful contributions to the organization over time if they can be freed-up and pursue professional development opportunities with time saved. 

This research underscores two important questions – how do you empower our teams to not just support the development of a tool that delivers these outcomes, but if we take this one step further – how do we empower our teams to explore the possibilities and originate and deliver these solutions? 

Hence, reskilling and upskilling is about a fundamental change in culture within the organization.

How does cultural change come about?

There have been interesting examples of radical cultural changes to merge technology with management and business mindsets. One example of this are the practical activities introduced by Digibank, Singapore (DBS) CEO Piyush Gupta, such as hackathons for process engineering by pairing a software developer with a business process owner.  

This exemplifies how fostering a culture that merges technology and business mindsets is crucial for successful AI integration. One of the members of TIAC, Handan Aymaz gave us her input on a hackathon she oversaw for process automation using RPA and beyond.  Generative AI can generate a range of possibilities, as well as collaborations for better working. This re-engineering culture is a product of the AI framework that the C-Suite must take ownership of.  

It is important to establish avenues for employees to exchange ideas as they gain proficiency with the technology. Considering creative thinking promotes department-wide cooperation across teams. This increases efficiency throughout the entire company. Not only are you changing the way you do business, but you are introducing a culture of open thought and teamwork.

Developed and emerging countries  

There is a clear difference between developed and emerging countries. In the former, skilled labor has to be wooed in a highly competitive job market, and existing labor has to be either upskilled or reskilled to incentivize them to stay in the company.  

In the latter where salaries are lower and economic conditions are difficult, the leadership attitude can lean towards cost optimization, which would result in a preference for hiring several people to do manual jobs rather than spending money on hiring people with training in AI/ML. 

However, the latter strategy ignores the lasting advantages of integrating AI, such improved productivity, expansion, and creativity. Organizations must underscore the importance of AI technology in fostering cost savings, competitive edge, and quality operations in order to counter this mentality. Furthermore, the ecosystem that surrounds the use of AI is crucial in determining the strategies and results of organizations. To foster breakthroughs in AI and learning, cooperation between governmental bodies, business associations, academic institutions, and technology companies is crucial. Policies and measures that support talent recruitment, business acumen, and AI advancement can boost social and economic progress in both developed and developing countries.

Navigating policy developments 

As AI advancements pave the way for human development, legislation and control around AI arises as a contentious issue. Legislation to regulate AI is welcome – but to what extent? On the one hand, we need regulation to protect us from malicious applications, such as creating deep fakes, data privacy concerns, and beyond. But will this legislation effectively prevent the creativity required to take AI to the next stage? Congressional hearings, for example, have cautioned that major players could leverage legislation to stifle new competition.  

As legislation like the AI Act is passed, businesses investing in AI will see an increase in legal uncertainty arising from regulatory compliances. Based on the “risk” level associated with your business (which is determined by factors such as what sector you operate in and how often you collect and utilize personal and confidential information), you may be required to implement risk management systems within your AI frameworks, along with divulging how your AI implementation uses other people’s data. This legislation also requires increased transparency, such as informing users that the agents they are interacting with are not human, but chatbots. Under Article 3, businesses deploying AI systems must reveal whether content has been artificially generated. These laws also come with an increased onus on businesses to train their personnel on AI handling and data confidentiality, as well as ensuring that documentation is kept up-to-date. Human oversight of AI systems may become mandatory for those operating in high-risk sectors like education, healthcare and human resources to ensure data security and removal of biases.

Regulation can potentially limit creativity, as there may be restrictions on the development and implementation of certain kinds of AI programs. As leaders in this space, we need to make sure that regulation does not hinder the rise of startups for fear of startups disrupting the bigger players. 

Many questions come to mind. Will creating an AI regulatory agency and licensing for companies only support the big tech companies? What about transparency on training data and establishing clear frameworks for AI-related risks?  With AI not knowing borders how can we best prepare?  

Looking ahead, the journey of navigating regulatory landscapes in AI is fraught with complexities and uncertainties, but it also presents opportunities for innovation and social progress. By embracing a forward-thinking approach to AI governance, grounded in principles of transparency, accountability, and inclusivity, we can utilize AI to create a more sustainable path towards technological progress.

Community-led AI: how collaborations drive innovation

To better understand the type of collaboration discussed in this article thus far, it is important to talk about the rise of the Telecom Intelligence Automation Council (TIAC). It actually started with the digitization company I consult with, Mercurial Minds (M.M.). In my travels as a consultant, I found the need to change mindsets about how intelligent automation can support organizations. 

Through this interaction with process owners and heads of CoE’s, we felt that establishing a not for profit organization that published (observing sensitivities) use cases, would help other like-minded organizations in practically embarking on their intelligent automation path. These collaborations include Microsoft and UiPath.  

Collaborations with educational institutes can help resolve technology issues, such as the case of Erik Brynjolfsson, whilst collaborations with professional organizations will help achieve business value.   

Summary

The process of using AI models to redefine corporate processes necessitates meticulous planning, collective effort, and a dedication to ethical AI governance. It is important to remain vigilant while handling private data, and keep in mind ethical considerations, even as AI keeps evolving. The C-suite, in particular, has to be aware of the possible issues involved with using AI, while still fostering greater AI literacy and innovation within their respective organizations and the wider industry.

Author

Dr Khalid Basit

Director of Automation Consultancy at M.M. & COO TIAC (Telecom Intelligent Automation Council). A seasoned expert in guiding organizations through transformative journeys. Specializes in initiating process discovery sessions that allow clients to envision change. Supporting them through each step, culminating in the realization of digital transformation necessary to drive tangible business value.
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