What Google Cloud Next 2026 actually told us about the titan pecking order

Google Cloud Next 2026 wrapped last week. The official version of the story is the one Google wanted you to read: 260 announcements, 1,302 customer use cases, the Gemini Enterprise Agent Platform, eighth-generation TPUs, a $750 million partner fund, an $240 billion Marketplace backlog. Big numbers. On-message keynote. Tidy "agentic era" framing.

The more interesting story is who showed up to validate it, and what Google actually built underneath.

Five of the seven enterprise titans I track walked into Las Vegas and announced expanded partnerships that all rest on the same architecture: Gemini Enterprise as the agent control plane, with the titan's product playing the role of premium ingredient. Salesforce. SAP. ServiceNow. Oracle. Adobe. Add Workday and Palantir Technologies to the picture, both adjacent to my titan list but visibly aligned in the same direction.

Two titans were not in the picture. Microsoft, because Copilot is the direct counter-position and Cloud Next is not Microsoft's stage. Zoho, because Zoho's stack does not need a Google motion and Zoho's buyer is not the same buyer.

Both absences matter. More about them a little later.

What Google actually built

Let’s start with the framing. Google did not just ship a model platform with new features. It repositioned Google Cloud from "AI development environment" to enterprise agent control plane. Vertex AI services and roadmap evolutions are now delivered through the new Agent Platform rather than as a standalone product. That is not a naming change, it's an entirely different playground.

The Agent Platform stack now visibly includes:

• Agent Identity for cryptographically secure agent authentication
• Agent Registry as the catalog of every agent and MCP server in scope
• Agent Gateway for traffic control and screening
• Agent Observability for production monitoring
• Agent Simulation for pre-deployment testing
• Agent Evaluation for measurable performance against benchmarks
• Agent Runtime with sub-second cold start
• Agent Inbox for human oversight of long-running agents
• Agent Studio as the low-code builder
• Agent Development Kit (ADK) across Python, Go, Java, with TypeScript

Sitting alongside this is the new Knowledge Catalog, which aggregates native context from partner data platforms and applications including Salesforce Data360, SAP, ServiceNow, Workday, and Palantir into a single accessible layer for Gemini agents.

That layer matters. It is the third leg of the orchestration story alongside interface (Gemini Enterprise app, Slack, Workspace) and runtime (Agent Platform). And physics teaches us that a third leg creates stability.

The Agent Marketplace and Agent Gallery surface partner-built agents directly inside the Gemini Enterprise app, with an IT-driven request-and-approval governance model. Open protocols carry the connective tissue: A2A, A2UI, and MCP, all positioned as neutral interoperability standards rather than proprietary lock-in.

This is the playground Google built. It is not a naming change. It is Google trying to redraw the enterprise AI battlefield.

Not by owning the CRM.

Not by owning the ERP.

Not by owning ITSM, HCM, marketing automation, or the system of record.

But by embracing them all.

It is Google Cloud repositioning from “AI development platform” to enterprise agent control plane.

Now look at who joined this merry party.

The seven partnership announcements, decoded one by one.

Salesforce

Agentforce Sales is in open beta inside Gemini Enterprise. Slack hosts Gemini Enterprise as a private preview app. Agentforce gets native Gemini reasoning through Atlas Reasoning Engine, with multimodal support across text, image, and video. Zero-copy access to Google Lakehouse is on the late-2026 roadmap. New BigQuery connectors for Salesforce Informatica IDMC are available now. Pepkor reportedly consolidated 64 million customer profiles down to 24 million using Salesforce Data 360 plus BigQuery, a 25 percent personalization reach lift. The framing from Srini Tallapragada is "agentic interoperability".

The translation is simple: Salesforce is letting Google become a distribution and work-surface partner for Agentforce, while Salesforce keeps the customer-data gravity and Atlas Reasoning Engine. Slack is the part of the deal that helps Salesforce most. Google Workspace and Gemini Enterprise are too big to ignore. This move is consistent with the recently announced Headless 360.

The tension that nobody named on stage is nevertheless there. If a buyer ends up with Agentforce on one side and Gemini Enterprise on the other, who governs the agents, where do they run, and which vendor gets paid for the orchestration? That fight is coming. Get yourself some popcorn!

SAP

SAP's announcement was imo the most strategically interesting of the week, and worth having a deeper look.

SAP Business Data Cloud (BDC) Connect for Google enables bidirectional zero-copy data sharing between SAP and BigQuery. Cortex Framework metadata in BigQuery grounds Gemini agents in SAP enterprise context. Joule Agents in SAP CX become deployable inside Gemini Enterprise. SAP Engagement Cloud picks up agentic capabilities for content development, marketing briefs, visual concepts, and collaborative multi-agent execution. Marketing is the first GA use case in H2 2026, with the model designed to extend across the SAP CX portfolio over time.

The headline from SAP itself describes Gemini Enterprise as "central hub for data integrations and multi-agent coordination”. On the surface, that is a vendor conceding the orchestration layer.

It is not. Read it again.

SAP is not handing over the operational core. SAP is making sure the Gemini agents that buyers run cannot meaningfully execute against enterprise data without going through SAP's very own grounding layer. Cortex Framework metadata in BigQuery is the move that matters. It means the semantic context for "what a customer record actually means in this enterprise" runs on SAP's side. Google gets the AI execution layer. SAP gets to stay the meaning layer.

That is SAP looking stronger, not weaker. The friendly stage handshake is going to turn into a knife fight in the field about where business logic lives. SAP appears to have positioned itself well for that fight.

ServiceNow

ServiceNow AI Control Tower integrates with Gemini Enterprise Agent Platform so that every agent and MCP server across both platforms appears in a single governed registry. Now Assist for IT Operations Management is available through Gemini Enterprise, focused on alert and incident management. Joint solutions ship in three industry domains: 5G autonomous network operations, retail predictive maintenance, and IT systems, all using ServiceNow agents and Gemini agents handing off through MCP and A2A. ServiceNow took home four 2026 Google Cloud Partner of the Year awards, including Agentic AI Innovation.

John Aisien positioned the agreement as "open, interoperable platforms, not walled gardens”. This framing is doing real work, and it is also strategically necessary. ServiceNow is the workflow titan most directly in Google's strategic crosshairs. Both companies want to be the orchestration layer above all systems. This partnership smooths the surface. Still, the strategic overlap is significant.

ServiceNow's strongest argument remains: "We already run the workflows, approvals, incidents, assets, service models, and operational context. Don't bolt orchestration on top. Run it where the work already lives". Google's counter is: "We can orchestrate across all of you, including ServiceNow". Both arguments are valid, and both are strong. Buyers will pick based on what they value more, neutrality across systems or depth inside the workflow platform that already governs work.

Partners today. Rival underneath. Both are true.

Oracle

Oracle's announcements were broader than the Database Agent that most coverage led with. The full set: Oracle AI Database Agent for Gemini Enterprise (currently in preview on Google Cloud Marketplace), a Managed MCP Server for Oracle workloads (also in preview), Database Center integration, Knowledge Catalog integration, GoldenGate integration, VPC Service Controls. Oracle AI Database@Google Cloud is now available across 15 regions with more to come.

Business users can query Oracle data in natural language without writing SQL. Identity propagates from Gemini Enterprise to the database via OAuth. Oracle's Deep Data Security enforces row- and column-level access at the database layer. Query processing stays inside the database, which Oracle frames as a security and latency benefit, and which has the side effect of keeping Oracle's data gravity intact.

Oracle is doing what Oracle has always done well. Make sure its database estate is unavoidable. Give Google enough access that the partnership is real. Do not pretend Oracle is going to own the AI front end. The Managed MCP Server, Knowledge Catalog hookup, Database Center, and GoldenGate pieces all point in the same direction: Oracle data stays central to AI execution regardless of where the agents are built; and the data stays in Oracle.

The database does not need applause. It needs to remain indispensable. Mission accomplished, I'd say.

Adobe

Adobe Marketing Agent for Gemini Enterprise lands in Google’s Agent Gallery. It connects natural language queries to Adobe's CX agentic capabilities, with insights on campaign performance, audiences, and journey monitoring, accessible from inside Gemini Enterprise. The integration is more lightweight than the others, which is consistent with Adobe's pattern.

Adobe benefits from showing up in the Gemini work surface, especially when marketing teams already live and breathe inside Workspace and Slack. But this is a distribution play, not a control-layer move on the level of SAP, Salesforce, Oracle, or ServiceNow. Adobe joins the gallery without conceding much architecturally and without claiming a piece of the orchestration plane. This is consistent with the Adobe Summit messaging.

Workday

Workday shows up through the Sana Self-Service Agent, which summarizes information from Workday and other sources and handles HR and finance tasks across hundreds of skills covering pay, time, and absence. Workday is also part of the Knowledge Catalog third-party context aggregation.

Workday is playing the employee-service and finance/HR productivity angle. It’s useful, sticky, high-volume in daily user activity. Compared with SAP and ServiceNow, it is narrower in operational control. Compared with Adobe, it is comparable in scope. Workday had a solid presence at this event, not a strategic re-positioning.

Palantir

Google says that Palantir is adding Gemini and BigQuery integrations for commercial customers, connecting models to critical AI workflows and operations. Palantir is also part of the Knowledge Catalog third-party context aggregation.

Palantir is the awkward guest at the titan table. It’s not a classic business application vendor but increasingly competing at the operational decision layer with Foundry and AIP. Google wants Palantir workloads close to BigQuery and Gemini. Palantir wants model optionality without losing AIP control. The integration is real and worth tracking precisely because Palantir does not usually settle for being an ingredient.

The pecking order this event produced

Most strategically advantaged: Google Cloud. It created the playground. The full agent control plane (Identity, Registry, Gateway, Observability, Simulation, Evaluation), the Knowledge Catalog, and the Marketplace make Google the layer everyone else runs on. Google’s risk is that it wants to be the enterprise control plane without owning the transactional cores that SAP, Salesforce, Oracle, ServiceNow, and Workday control. That requires relentless execution, not keynote poetry.

Most durable titan: SAP. SAP owns the operational core. The combination of BDC Connect and Cortex Framework gives SAP a stronger bridge into Google's AI without surrendering enterprise meaning. SAP's posture is "use Google's AI, but ground it in SAP business truth". That is the right defense and a subtle offense at the same time.

Most interesting tension: Salesforce. There's a great integration story today. The unresolved question is whether Agentforce and Gemini Enterprise will eventually compete for governance and orchestration once buyers are running both in production. And they will. Procurement will notice when both vendors invoice for the same workflow.

Most direct control-plane rival: ServiceNow. A visible partner. And an architectural competitor. The "open, interoperable" framing is correct in principle, and it is also the terminology a vendor uses when its core product overlaps strategically with the platform it just partnered with. The deciding factor for buyers is whether they want a neutral AI control plane above systems, or agentic execution inside the workflow platform that already governs work.

Most pragmatic: Oracle. No applause needed. The database stays indispensable. Managed MCP Server, GoldenGate, Knowledge Catalog hookup, VPC Service Controls all point in the right direction for Oracle. That’s pragmatic, and dangerous in the right way.

Useful but narrower: Adobe and Workday. Both gain Gemini Enterprise distribution reach. Neither announcement changes their strategic center of gravity. There is nothing earthshattering about them. They are domain wins, not control-plane bids.

Adjacent: Palantir. This is worth watching specifically because Palantir does not usually accept ingredient status.

The two missing names

Microsoft. Copilot exists exactly to defend the position Google is now contesting. Microsoft has spent two years building Copilot Studio, Microsoft 365 Copilot, Dynamics 365 agents, and an Azure-side AI tooling that competes head-on with what Google just announced. Microsoft was never going to show up at Cloud Next to validate Gemini Enterprise. Why would it?

The more interesting question is whether Salesforce, SAP, ServiceNow, and Oracle agents will sit as comfortably inside Copilot in twelve months as they now do inside Gemini Enterprise. Right now, the answer is no, and the gap appears to be widening. I expect Microsoft to respond at Build and Ignite. The main question then is whether the response will be "we have parity" or "we are bigger and we will route around you”.

Zoho works a different market segment. Zoho also builds its own AI stack. The company rarely participates in this kind of big vendor partnership theater. The absence is consistent and not so interesting when looked at in isolation. It becomes interesting, however, when paired with the observation that Zoho's mid-market and SMB-plus customers are largely outside the buying pattern Cloud Next 2026 is shaping. Two different conversations happening in two different rooms.

Open protocols, not walled gardens. Maybe.

I want to be careful about reading the "open, interoperable" framing.

A2A, A2UI, and MCP are all real, and they matter. ServiceNow's positioning is correct in principle. Salesforce kept Slack. Oracle kept the database. SAP kept Joule as the engagement layer in SAP applications. Adobe kept its CX stack untouched. Workday kept HR. Palantir kept AIP. None of these vendors handed over the asset they care most about.

But the registry is Google's. The gateway is Google's. The gallery, the runtime, the inbox, the identity model, the agent governance plane: all Google. Open protocols are not the same thing as a neutral platform. They are the price of admission to a platform that acts as a host.

The real question for the next twelve months is whether the protocols stay open enough that buyers can swap the host. If a customer can take A2A-compliant agents built around Gemini Enterprise and re-host them on Copilot Studio or AWS Bedrock AgentCore without rewriting most of the orchestration, the open framing holds. If swapping costs are high in practice, "open" is doing marketing work that the architecture does not back up.

I do not yet have any evidence either way. I expect to in the next two quarters with the first multi-vendor pilots moving into production.

What buyers should do this quarter

Force each titan to defend the front door. Salesforce will say Slack and Agentforce. SAP will say Engagement Cloud and Joule. ServiceNow will say ServiceNow. Microsoft will say Copilot. Google will say Gemini Enterprise. Make them defend the answer with specific cross-system workflows for agentic work. Note which vendor accepts being an ingredient in someone else's interface and which one fights for the seat.

Pin down the dates on zero-copy commitments. The Salesforce zero-copy with Google Lakehouse is late 2026. SAP BDC Connect is rolling out across 2026. Oracle's Managed MCP Server is in preview. Most of the headline-friendly capabilities are not in your tenant today. Build your 2026 plan around what you can run by Q3, not what is on a slide.

If you are a Microsoft shop, run a parallel evaluation. Bring in Copilot Studio. Ask whether Salesforce, SAP, and ServiceNow agents are first-class citizens inside it at the depth that Cloud Next demonstrated for Gemini Enterprise. Force Microsoft to demonstrate parity, not promises.

Treat the Agent Marketplace and Agent Gallery as procurement infrastructure. If your IT team adopts Gemini Enterprise as the agent procurement layer, that decision shapes which titans show up first in your future RFPs. Make this choice deliberately.

Test the governance question before you buy. If you end up with Agentforce, Joule, Now Assist, and a Gemini-Enterprise-built custom agent all running for the same business process, who governs them? Procurement will notice when you are paying twice for orchestration. Make a vendor own the answer in writing, and see to it that the SKUs do not overlap too much.

The unspoken partnership

The most interesting partnership at Next 2026 was the one nobody put in a press release. Five of seven titans, plus Workday and Palantir, plus a long list of consulting firms and ISVs, are all visibly aligning to ensure that Microsoft Copilot is not the only place enterprise AI gets done. None of them said that. All of their actions imply it.

That is the alliance worth watching.

I am still skeptical about how durable this alignment is once Microsoft responds, and buyer realities surface in the second half of 2026, and once we see what "open protocol" really means in production. All vendors will optimize for their own positions. They always do, and they need to. The orchestration question may stay answered for a year, or it may reopen the moment someone like Microsoft offers a credible alternative.

For now, Gemini Enterprise has the momentum. The titans showed up. The protocols are public. The Marketplace is live. The control-plane components are named. Google moved from "another model vendor" to "the agentic substrate the application titans run inside of”.

This is a very different conversation than the one we were having last year. It’s worth paying close attention to how it evolves and whether it is the right one.

The hype around generative AI, in particular ChatGPT is still at a fever pitch. It created thousands of start-ups and at the moment attracts lots of venture capital. 

Basically, everyone – and their dog – jumps on the bandwagon, with the Gartner Group predicting that it is getting worse, before it is going to be better. According to them, generative AI is yet to cross the peak of inflated expectations. 

There are a few notable exceptions, though. So far, I haven’t heard major announcements by players like SAP, Oracle, SugarCRM, Zoho, or Freshworks.

Before being accused of vendor bashing … I take this is a good sign. Why?

Because it shows that vendors like these have understood that it is worthwhile thinking about valuable scenarios before jumping the gun and coming out with announcements just to stay top of the mind of potential customers. I dare say that these vendors (as well as some unmentioned others) are doing exactly the former, as all of them are highly innovative.

Don’t get me wrong, though. It is important to announce new capabilities. It is probably just not a good style to do so too much in advance, just to potentially freeze a market. This only leads to disappointments on the customer side and ultimately does not serve a vendor’s reputation. 

For business vendors, it is important to understand and articulate the value that they generate by implementing any technology. Sometimes, it is better to use existing technology instead of shifting to the shiny new toy. The potential benefits in these cases simply do not outweigh the disadvantages, starting from cost of running the new technology and extending to the added business value being marginal. Sometimes technology is a solution in search of a problem (anyone remember NFT or  Metaverse?), sometimes the new technology even turns out to be outright harmful.

Although the better is the enemy of the good, not everything new is actually better than the old. Vendors as well as buyers should keep this simple truth in mind.

Specifically looking at generative AI, it is therefore important to look at what the strengths and limitations of this technology are and to map out where business scenarios map to them. For this, I have outlined a simple framework a short while ago.

In brief, value comes out of solutions that adequately address the dimensions of fluency and accuracy. Not every business challenge needs to be addressed with equal fluency or accuracy. However, and this is important, accuracy also covers bias. Bias needs to be understood and managed. 

I have outlined a few examples in that article.

In the past few weeks, vendors like Cognigy, Microsoft, Salesforce and Google did some major announcements covering enterprise use cases of generative AI. In the meantime, Open AI announced version 4 of GPT. Let’s have a look what they were about and how they fit into the fluency and accuracy categories. Notably, all vendors emphasize on a human-in-the-loop functionality being embedded in their new AI features.

Cognigy

Cognigy is a vendor of conversational AI, currently focusing on the call center market, as both, agents and customers can receive a lot of benefit from a conversational AI system. Part of any conversational AI system is the ability to design and implement conversations. As such, the company implements technical as well as business scenarios with the help of generative AI.

The technical scenarios range from seemingly simple ones like the creation of seeding sentences to train the system’s intent detection to the development of conversation flows from written commands.

Both of these scenarios are in the high fluency area, while the creation of conversation flows also requires high accuracy. Generating seed phrases as a training set needs a solid understanding of synonyms and language use while the generation of whole conversation flows also needs precise formulations. After all, code is a precise language, even if visualized by symbols.

The business scenarios are all about improving the understanding of the customer and providing well worded responses, written or spoken – with the spoken ones obviously being more impressive. Cognigy basically combines the strengths of the conversational AI system – keeping focus on the task to be accomplished and connectivity to business systems – with the ability of the generative AI to formulate human-like sentences and to exhibit empathy by reacting on statements that deviate from the core task to be accomplished. Two examples for this are here and here.

Google

Google is an example that shows the hype that is generated by ChatGPT. The company is using generative AI features in its workspace products for quite a while now. For emails, smart reply exists since 2017, smart compose that suggests sentences of fragments thereof since 2018, auto summarizing of documents and spaces since 2022. Spam and phishing filtering is basically available forever and gets continuously improved. On March 14, Google announced the rolling rollout of additional features for Gmail, Docs, Slides Sheets, Meet and Chat. These features will base on Googles own PaLM LLM. Google will start with text generation from topical prompts or a rewriting of a given text.

In contrast to the other vendors, Google is focusing on collaboration efficiency, which is reasonable as Google is not a business applications vendor in the traditional sense. As these features will be made available only in the course of 2023, it remains to be seen how good they really are. However, all of these features need to score high on the fluency scale, with the drafting, replying, summarization and prioritization of texts also requiring high accuracy.

Microsoft

No need to explain what Microsoft in general does. Being the main investor into Open AI, it naturally has a front runner role when it comes to the integration of generative AI by Open AI into business and other applications. Microsoft did two main announcements in the past weeks that stretch the range of business applications. First, it announced an integration of generative AI into its Viva Sales product, already in February. On March 6, the company then announced an “AI copilot for CRM and ERP”.

In February, Microsoft announced the integration of GPT into Viva Sales, with the ability to automatically formulate emails for specific scenarios, like replying to an inquiry, or formulating a proposal, also utilizing data coming via Microsoft Graph. 

In March, this got enhanced to cover not only Viva Sales but functions covering sales, service, marketing and supply chain, followed by an announcement covering Microsoft 365 (formerly known as MS Office) on March 16. These are now being dubbed the Dynamics 365 Copilot and Microsoft 365 Copilot. The functionality in Viva Sales gets enhanced by some scenarios, an additional feedback loop and the capability to create meeting minutes including action items. These scenarios require a high fluency and varying degrees of accuracy, with the summarizing of meetings being at the higher end.

The same capabilities are available in Dynamics 365 Customer Service email and chat. Incoming information is analysed and used to create an answer that includes information from the knowledge base.

These scenarios basically require the same degree of fluency and accuracy as the summarization of meetings to lead to fast and efficient issue resolution.

Marketing scenarios include the prompting of data in natural language, for example to create a target group. The system basically generates the query to fetch the corresponding data, which requires good knowledge of the database schemata, i.e. has high demands to accuracy.

The second scenario is the creation of the text for a marketing email to support the campaign. Notably, there is no support for the creation of a landing page yet. In this scenario, the requirement to fluency is higher than the one to accuracy.

Microsoft Dynamics 365 Business Central gets enhanced by the ability to generate product descriptions based upon product title and product attributes. 

Last, but not least, Dynamics 365 Supply Chain Management now allows to generate emails to suppliers, carriers, etc., based on intelligence surfaced in the news module that gets correlated to existing orders. 

The capabilities of the Microsoft 365 Copilot are largely equivalent to the ones described above, tapping into the Microsoft Graph.

Most of these Copilot features are available in what Microsoft names a limited preview only.

Salesforce

Salesforce, the undisputed leader in CRM, announced support for generative AI, named Einstein GPT, on March 7, as part of its TrailblazerDX event as “the world’s first generative AI for CRM”. The functionality shall be able to create content across sales, service, marketing, commerce and IT. Similar to what Microsoft announced, Einstein GPT is able to generate personalized emails for salespeople, generate specific responses in service scenarios or to generate targeted content for marketers, which includes landing pages. To do so, Einstein GPT extends Salesforce’s proprietary AI models, takes advantage of Salesforce’s Data Cloud and offers out of the box connectivity to Open AI’s AI models. Alternatively, it is possible to utilize other models, e.g., Salesforce partners Anthropic, Cohere, Hearth.ai or You.com.

Salesforce will invest in these companies via its investment arm. Additionally, Einstein GPT is capable of auto creating code from user prompts.

Salesforce’s focus is slightly different from Microsoft’s. No support for ERP and supply chain tasks is obvious. Differences in sales, service and marketing functionalities are more subtle. Where Microsoft concentrates on meeting minutes in its sales functionality, Salesforce supports scheduling of meetings.

What is interesting is Salesforce’s ability to generate kb articles from case notes as part of its service capability. If this functionality also includes improving existing ones, it could be a real game changer in customer service.

Einstein GPT for Marketing supports the generation of content supporting email, mobile, web and advertisement engagements.

With Einstein GPT for Slack Customer 360 apps it is possible to deliver insights like generated summaries of opportunities and other information to Slack. This supports the increasing importance of conversational user interfaces and is a good fit for a generative AI.

Last, but not least, Einstein GPT for Developers enables the generation of code. This functionality uses a Salesforce Research proprietary LLM.

Out of these scenarios, the generation of kb articles and code certainly have the highest demands on accuracy.

At this time, Einstein GPT is in a closed pilot.

My analysis and point of view

In brief, I see much more of a future for these technologies than I have seen in the past major hypes: Web 3, Blockchain, and Metaverse. This is mainly because these three appear to be solutions in search of a problem while we see clearly described enterprise use cases for generative AI.

It is quite obvious that these vendors that I selectively chose are using a land and expand approach. They are starting with very specific scenarios which get extended over time. Many of these use cases extend existing conversational, or in general, AI based scenarios. From a technology point of view the new solution might even replace the older one, which is of no consequence if there is a transparent migration.

The selected scenarios are regularly in the high accuracy and high fluency quadrant, which caters to one of the main use cases of a generative AI.

It is also quite obvious that there is a tremendous struggle for mindshare. All these announcements are coming at about the same time and they are mostly talking about limited availabilities, i.e. betas or trials, indicating work in progress. The actual releases are at some time in the future. As much as I do not like this, this seems to be the way the business works.

Not surprisingly, the vendors are mainly focusing on similar capabilities. This is partly due to my selection. The good news is, that all vendors argue with business benefits instead of promoting technology for technology sake. To provide these capabilities, and this is important, all vendors connect the generative abilities with data that is available in the organization, be it from databases, file repositories, business applications, or chat- and email conversations. 

In my eyes, all these functionalities are helpful in a sense that they take tedious work away from persons; so, they are definitely worthwhile to be trialled.

The challenge

For now, all these features require a human in the loop, i.e., they need active confirmation of a user. Which is a good thing, given that a generative AI still has a tendency to hallucinate, even though e.g. Open AI claims that GPT 4 has a 40 percent higher likelihood to produce factual responses than GPT 3.5.

The goal is to gain trust while the human is still in control. But what do humans do, when they trust, or trust enough? All of the sudden, the suggestion implicitly becomes a decision. This is OK, as long as it is really sure that these “decisions” are good. And that includes that a number of things are guaranteed.

The models must be trained to have minimum bias. There must be a verifiability of this. This does not jibe well with news like Microsoft laying off an ethical AI team while not explaining how this job is done in future. Principles are good, control is necessary. Similarly, Open AI’s turn to not disclosing anymore how the training data got created nor how many parameters it has, etc., citing the competitive environment. As PROs chief AI strategist Dr. Michael Wu recently said in a CRMKonvo, it is impossible to avoid bias, but it must be understood. Then the AI can be a real helper. I’d like to add that the user must be able to understand.

This leads to the second point. The system’s “reasoning” needs to be explainable by the system in a way that not only data scientists understand it. This is an area that many AI’s sorely lack and actions like the ones above are not helpful in this endeavour as they may raise the impression of delivering a black box that is not subject to strict governance.

Lastly, the data that gets used in training and operation must be clean enough. This is a job for not only the vendor but also for the organization that runs the AI. One reason is the reduction of bias, the other one going forward is good recommendations/decisions. While data cleansing is a lost cause, frameworks and regular clean-up work needs to be in place to be sure that the used data is good enough – not perfect, but good enough.

My advice

Given all this, I advise cautious use of these tools in a controlled environment, and to measure the results, both in terms of effort reduced and quality of the output. This is not an easy, but a mandatory, task to fully assess the value of these tools and to establish the necessary trust level. 

Additionally, have the vendors demonstrate which AI principles govern the development and what procedures they have in place to effectively make sure that these principles are baked into the product.

In that sense, the vendors’ early announcements and long closed trial periods are very helpful. 

• Improving Google’s containerization technologies for enterprise workloads
• Security, privacy, and integrity of customer data in the cloud. As part of this SAP software shall act as a data custodian (NB: How that works in legal and political environments remains to be seen) and joint solutions for access control, governance, risk and compliance shall get developed
• Integrate Google’s G Suite into SAP applications. This has already been implemented for Identity and Access Management.

My Take

Good Win for SAP

What about Google?

And the competition?

Last but not Least: The Customers