IT Services companies that utilize the power of GenAI can expect an absolute operating profit margin impact of 4 to 6 %, realizable through internal efficiency gains and revenue uplifts. As much of this value resides in easy-to-adopt use cases, the inability to act quickly will highly benefit competitors, possibly to an extent that non-movers are driven out of the market.
Irrespective of the industry, GenAI is the current talk of town for business value creation opportunities. While AI in general has been around for some time, GenAI is still a relatively young technology. In essence, it describes the ability of using human input prompts to improve, create or interact with text, data, code, images, audio and video. It reached mainstream attention due to the launch of ChatGPT by OpenAI in late 2022 as well as its efficient and easy-to-use nature which disrupts along four key dimensions:
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1. Easy to accessGenAI is readily available for anyone. Its application does not require specialized data science skills nor any cost-intensive software.
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2. Easy to operateIt is – different to traditional algorithms – easy to use, as it can process unstructured data without the need for logical operators.
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3. Easy to specifyGenAI is easy to configure. Its models can mimic any real or imaginary style, e.g. writing new poems in the style of Shakespeare.
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4. Easy to scaleIt is simple to scale as it has the ability to create outputs in any desired format and can improve the efficiency and experience of any performed task.
These dimensions open business opportunities for companies along six GenAI capabilities.
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SummarizationProducing an abbreviated form of a given document, coded program, or other body of text. Examples are the querying of large data sets (e.g., public company information) to summarize financials or the identification of customer sentiments from surveys or social data.
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Deep retrievalSearching for specific information within a given document or set of documents. Examples are the querying of a corpus of documents for business insights using natural language questions or the creation of metadata from documents to support business decisions.
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TransformationCreating a transformed version of data, such as image style transfer, text translation, or text personalization. Examples are the translation of long form texts to other languages or the re-formatting of code to company standards.
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AugmentationExpanding upon existing content, such as auto complete or synthetic data creation. Examples are the imputation of missing values with synthetic data or the auto-completion of written text (emails, code, or presentations).
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Q&A (dialogue)Responding to a given question. Chatbots, service bots, and virtual assistants support Q&A. Examples are responding to customer queries with intelligent service options or the generation of human-like dialogue for in-product functionality.
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Net-new creation Generating net-new content based on user-provided prompts (instructions). Examples are the creation of images or videos for marketing content or the generation of engaging titles for blogs, articles etc.
The impact that GenAI has on different sectors varies greatly. It depends on how easily it can be adopted, and on how disruptive its application is in terms of business and operating models as well as the overall competitive situation in each sector.
IT Services is one of the industries expected to take the center stage when it comes to the anticipated impact by GenAI on businesses. This is driven by both the relative ease of adaption and the relative level of disruption in the sector. The high ranking in the former category is driven by the comparably high harmonization and standardization of IT system landscapes (e.g., ERPs) within the IT Services industry, which typically allow for an efficient and swift roll-out of new applications, including GenAI. On the other hand, the IT Services industry is subject to an inherent competitive race to stay at the technological forefront, as barriers to entry are comparably low and margins upsides are achieved through cost discipline via continuously increasing levels of automation. The next lap in this race to technological superiority is expected to be fueled by GenAI, which offers vast application opportunities in the sector.
To underline this, we have identified many potential use cases for IT Service providers along all key GenAI capabilities. If pursued, they promise significant efficiency gains and revenue uplifts. If neglected – or if action is taken too slowly and indecisively – there is substantial threat to be outperformed and possibly even to be driven out of the market.
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In the following, we highlight two of those identified use cases
Use case 1 – Auto-commenting for repetitive reporting
Performance and financial reporting are repetitive tasks for IT Service providers that are closely monitored on a continuous basis. Typical KPIs that are tracked include compliance to service level agreements (SLAs), incident response times, Mean-Time-to-Repair (MTTR), as well as financial KPIs such as Cost-per-Ticket and industry-agnostic financial reporting items. Large parts of the data extraction, preparation and visualization work have already been automated, at least by most large players, with established interfaces between ERP and data reporting & visualization tools, such as PowerBI or Tableau. Yet, the qualitative commentary and explanations of these KPIs for internal and external reporting documents are still primarily created manually, being a major capacity driver for controller communities. Based on a PwC Strategy& study, it takes on average seven working days until quarterly reports can be published. This is mainly driven by:
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Generation of general comments
Commentary of descriptive nature, e.g., verbalization of graphs and charts, comparison with and changes to previous periods in absolute and relative percentage-terms, highlighting top and worst performing segments/ regions/ business units/ customer service teams, etc. For IT Service providers, this also includes relevant standard reports send out to customers, summing up the performance of the provided services in comparison to the stipulated SLA.
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KPI analysis
Dissection of selected KPIs and drill-down into their key components. If e.g., Cost-per-Ticket are to increase significantly in one location operated by IT Service providers, GenAI algorithms can conduct a de-composition into volume and cost deviations (incl. breakdown), supporting the controlling team in their root-cause identification.
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Expert analysis
Derivation of specific recommendations for actions on how to improve specific KPIs. For IT Service providers, this could include industry specific suggestions such as e.g., decreasing overhead cost items per service ticket, or increasing service staff during bottlenecks, etc.
For these tasks, GenAI and its augmentation capabilities provide substantial efficiency improvement potential for IT Service providers. Auto-commenting tools can fully automate purely descriptive verbalization or provide commentary suggestions to controllers for more sophisticated passages that can be used as a starting point or second opinion to increase accuracy and significance to the target audience. In addition, existing documents as well as additional data (e.g., market/ competitive data) can be integrated easily using GenAI reasoning capabilities. Hence, providing a more holistic view on the presented information or provide benchmarking data on selected KPIs to put e.g., the performance of the provided service level to customers or the Mean-Time-to-Repair into market context.
As a result, GenAI supported reporting creation becomes more efficient while increasing the generation speed, quality, and reliability of the deliverable at the same time. Controllers can utilize the freed-up capacity for more value adding activities, further driving the shift from a purely reporting oriented organization towards a proactive business decision enabling role. This can include offering deep insights into operative efficiency, as well as providing customer service improvement and value creation suggestions to top management.
Use case 2 – Chat bot in IT customer service
Customer service excellence is at the heart of IT Service providers, as it lays the foundation of the entire business model. Although customers ideally only spend little time on calls with their respective supplier, studies show that close to 30% would consider a change after just a single bad experience. Addressing this demand for service excellence and end-to-end solution competence has traditionally been time consuming in all industries and requires extensive conversation documentation for customer facing functions, tying up capacity that should rather be used to swiftly help customers solve their pressing problems.
However, the issue is particularly pressing for IT Service providers, as their managed service offerings typically also include time-consuming tasks such as maintenance, updates, as well as support in case of equipment/ application failures and further incidents. The timely execution and resolution of support tickets combined with the adherence to selected contractually binding KPIs defined in service level agreements (SLAs) are putting further pressure on IT Services provider and their customer-facing support staff.
To alleviate service center staff from this burden and to aid them to put their focus on more value creating tasks, GenAI support bots can help mainly along three dimensions:
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Automate documentation
Here, IT Service staff is supported via the automation of transcript creation based on voice recognition and the identification of key words to categorize the call, which is then permanently stored in the CRM system. As these tasks can often take as long as a customer call itself, tremendous efficiency gains are expected.
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Incorporate sentiment analysis
With this functionality, IT Service providers can optimize their general staffing capacity on a company basis and directly safe personnel cost. This is achieved via holistic data analysis, combining voice recognition patterns with purpose of the conducted calls as well as meta data (duration, time of day, etc.) to derive customer characteristics.
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Provide real-time assistance
With the help of this feature, service center staff is supported during the conversation with suggested prompts, based on existing customer data. This can shorten average call duration and thus saves time required to resolve issues. For very basic customer requests, real-time assistance can directly replace the service staff operator, with the GenAI answering customer questions directly.
On one hand, GenAI based support bots thereby enable IT Service providers to increase service quality by effectively supporting customer-facing service center staff and overtaking time-consuming documentation work. On the other hand, the achieved time savings, paired with concrete suggestions for capacity adjustments, can be a powerful tool for either allocating freed-up bandwidth to more value-creating tasks, or optimizing redundant personnel capacities drastically. On top of that, consistent and structured data collection enables to comprehensively leverage those data pools for marketing and CRM purposes.
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Simon Lobert, Dr. Tobias Kalsbach, Arthur Eberdorfer, and Luca Neuendorf have also contributed to this article.
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